modelId string | author string | last_modified timestamp[us, tz=UTC] | downloads int64 | likes int64 | library_name string | tags list | pipeline_tag string | createdAt timestamp[us, tz=UTC] | card string |
|---|---|---|---|---|---|---|---|---|---|
sunblaze-ucb/Qwen2.5-1.5B-Intuitor-MATH-1EPOCH | sunblaze-ucb | 2025-06-16T20:54:56Z | 87 | 0 | null | [
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:math",
"arxiv:2505.19590",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-02T05:44:57Z | ---
base_model: Qwen/Qwen2.5-1.5B
license: apache-2.0
datasets:
- math
metrics:
- accuracy
pipeline_tag: text-generation
language:
- en
---
# Qwen2.5-1.5B-Intuitor-MATH-1EPOCH
**Description:**
An Intuitor-fine-tuned version of Qwen2.5-1.5B trained on the MATH dataset.
---
## Citation
```bibtex
@article{zhao2025learning,
title = {Learning to Reason without External Rewards},
author = {Zhao, Xuandong and Kang, Zhewei and Feng, Aosong and Levine, Sergey and Song, Dawn},
journal = {arXiv preprint arXiv:2505.19590},
year = {2025}
}
```
|
dgambettaphd/M_llm2_run2_gen10_WXS_doc1000_synt64_lr1e-04_acm_SYNALL | dgambettaphd | 2025-06-16T20:37:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T20:37:14Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
IntMeGroup/CompBench_int3_pair2_difficult | IntMeGroup | 2025-06-16T20:32:26Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"internvl_chat",
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T13:07:55Z | ---
license: apache-2.0
---
|
gretab5802/ppo-HalfCheetah-v3 | gretab5802 | 2025-06-16T20:13:41Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"HalfCheetah-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-16T20:13:11Z | ---
library_name: stable-baselines3
tags:
- HalfCheetah-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetah-v3
type: HalfCheetah-v3
metrics:
- type: mean_reward
value: 5111.06 +/- 1825.58
name: mean_reward
verified: false
---
# **PPO** Agent playing **HalfCheetah-v3**
This is a trained model of a **PPO** agent playing **HalfCheetah-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env HalfCheetah-v3 -orga gretab5802 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env HalfCheetah-v3 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env HalfCheetah-v3 -orga gretab5802 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env HalfCheetah-v3 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env HalfCheetah-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env HalfCheetah-v3 -f logs/ -orga gretab5802
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('clip_range', 0.1),
('ent_coef', 0.000401762),
('gae_lambda', 0.92),
('gamma', 0.98),
('learning_rate', 2.0633e-05),
('max_grad_norm', 0.8),
('n_envs', 1),
('n_epochs', 20),
('n_steps', 512),
('n_timesteps', 1000000.0),
('normalize', "{'norm_obs': True, 'norm_reward': False}"),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, '
'net_arch=dict(pi=[256, 256], vf=[256, 256]) )'),
('vf_coef', 0.58096),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Lelon/cue-en-socc | Lelon | 2025-06-16T19:59:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-06-16T19:58:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
yigitbekir/Bert2D-cased-Turkish-128K-WWM-NSW2 | yigitbekir | 2025-06-16T19:54:07Z | 642 | 3 | null | [
"pytorch",
"safetensors",
"bert2d",
"fill-mask",
"custom_code",
"tr",
"doi:10.57967/hf/5763",
"license:apache-2.0",
"region:us"
] | fill-mask | 2025-05-22T14:13:32Z | ---
license: apache-2.0
language:
- tr
pipeline_tag: fill-mask
---
# Bert2DModel
[Bert2DModel](https://ieeexplore.ieee.org/document/10542953) is a new take on the classic BERT architecture, built specifically for languages that have really complex word structures, like Turkish.
Think of it this way: regular BERT sees a sentence as a flat line of words. But for some languages, words themselves have a lot of internal structure (prefixes, suffixes, etc.). Bert2D is cool because it uses a "2D embedding" system. It not only looks at a word's position in the sentence (the first dimension) but also at the position of the sub-pieces inside that word (the second dimension). This gives it a much deeper understanding of the grammar and meaning, especially when words can change form in many different ways. This first version is trained for Turkish\!
You can find all the original [Bert2DModel] checkpoints under the [yigitbekir](https://huggingface.co/yigitbekir) collection.
> [\!TIP]
> Click on the [Bert2DModel] models in the right sidebar for more examples of how to apply [Bert2DModel] to different text and token classification tasks.
The example below demonstrates how to use the `fill-mask` pipeline with `Bert2DModel` or load it directly with the [`AutoModel`] class.
<hfoptions id="usage">
<hfoption id="Pipeline">
```python
from transformers import pipeline
# 1. Define your model repository ID
repo_id = "yigitbekir/Bert2D-cased-Turkish-128K-WWM-NSW2"
# 2. Create the pipeline for the "fill-mask" task
# The model_kwargs dictionary passes arguments to the underlying model loading function.
fill_masker = pipeline(
"fill-mask",
model=repo_id,
use_fast=True,
trust_remote_code=True
)
# 3. Prepare the input and get predictions
masked_sentence = "Adamın mesleği [MASK] midir acaba?"
predictions = fill_masker(masked_sentence)
# 4. Print the results in a user-friendly format
print(f"Predictions for: '{masked_sentence}'")
for prediction in predictions:
print(f" Sequence: {prediction['sequence']}")
print(f" Token: {prediction['token_str']}")
print(f" Score: {prediction['score']:.4f}")
print("-" * 20)
# Expected output:
# Sequence: Adamın mesleği mühendis midir acaba?
# Score: 0.2393
# --------------------
# Sequence: Adamın mesleği doktor midir acaba?
# Score: 0.1698
# --------------------
```
</hfoption>
<hfoption id="AutoModel">
```python
from transformers import AutoTokenizer, AutoModel
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("yigitbekir/Bert2D-cased-Turkish-128K-WWM-NSW2", trust_remote_code=True)
model = AutoModel.from_pretrained("yigitbekir/Bert2D-cased-Turkish-128K-WWM-NSW2", trust_remote_code=True)
# Example text
text = "Türkiye'nin başkenti Ankara'dır."
inputs = tokenizer(text, return_tensors="pt")
# Get model outputs
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
</hfoption>
</hfoptions>
## Notes
- **Configuration is Key:** `Bert2D` introduces new configuration parameters that are not present in a standard BERT model. You must use the `Bert2DConfig` and be mindful of these settings when training or fine-tuning. Failing to do so will lead to unexpected behavior. The two key new parameters are `max_word_position_embeddings` and `max_intermediate_subword_position_embeddings`.
```py
from transformers import AutoConfig
# Load the custom config from a pretrained model
config = AutoConfig.from_pretrained("yigitbekir/Bert2D-cased-Turkish-128K-WWM-NSW2", trust_remote_code=True)
# Access new parameters
print(f"Max Word Positions: {config.max_word_position_embeddings}")
# Expected output: Max Word Positions: 512
print(f"Intermediate Subword Position: {config.max_intermediate_subword_position_embeddings}")
# Expected output: Intermediate Subword Position: 2
``` |
wATCH-Sophie-Rain-Spiderman-New-Video-tv/Sophie.Rain.Spiderman.Sophie.Rain.Spiderman.New.Video.Tutorial.Official | wATCH-Sophie-Rain-Spiderman-New-Video-tv | 2025-06-16T19:08:17Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T19:08:00Z | 18 seconds ago
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p>
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
. . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram |
amentaphd/example | amentaphd | 2025-06-16T19:00:52Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:10",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:Snowflake/snowflake-ar... | sentence-similarity | 2025-06-16T19:00:35Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-m
widget:
- source_sentence: What processes are used to separate the raw liquid mix from natural
gas in a gas recycling plant?
sentences:
- '2016 ►M43 (*1) ◄ 21 September 2017 ►M43 (*2) ◄ [▼M28](./../../../legal-content/EN/AUTO/?uri=celex:32014R0895
"32014R0895: INSERTED") 23. Formaldehyde, oligomeric reaction products with aniline
(technical MDA) EC No: 500-036-1 CAS No: 25214-70-4 Carcinogenic (category 1B)
22 February 2016 ►M43 (*1) ◄ 22 August 2017 ►M43 (*2) ◄ — 24. Arsenic acid EC
No: 231-901-9 CAS No: 7778-39-4 Carcinogenic (category 1A) 22 February 2016 22
August 2017 — 25. Bis(2-methoxyethyl) ether (diglyme) EC No: 203-924-4 CAS No:
111-96-6 Toxic for reproduction (category 1B) 22 February 2016 ►M43 (*1) ◄ 22
August 2017 ►M43 (*2) ◄ — 26. 1,2-dichloroethane (EDC) EC No: 203-458-1 CAS No:
107-06-2 Carcinogenic (category 1B) 22 May 2016 22 November 2017 — 27.'
- '1. Member States shall ensure that their competent authorities establish at least
one AI regulatory sandbox at national level, which shall be operational by 2 August
2026. That sandbox may also be established jointly with the competent authorities
of other Member States. The Commission may provide technical support, advice and
tools for the establishment and operation of AI regulatory sandboxes.
The obligation under the first subparagraph may also be fulfilled by participating
in an existing sandbox in so far as that participation provides an equivalent
level of national coverage for the participating Member States.'
- and that boils in a range of approximately 149 °C to 205 °C.) 649-345-00-4 232-489-3
8052-41-3 P Natural gas condensates (petroleum); Low boiling point naphtha — unspecified
(A complex combination of hydrocarbons separated as a liquid from natural gas
in a surface separator by retrograde condensation. It consists mainly of hydrocarbons
having carbon numbers predominantly in the range of C2 to C20. It is a liquid
at atmospheric temperature and pressure.) 649-346-00-X 265-047-3 64741-47-5 P
Natural gas (petroleum), raw liquid mix; Low boiling point naphtha — unspecified
(A complex combination of hydrocarbons separated as a liquid from natural gas
in a gas recycling plant by processes such as refrigeration or absorption. It
consists mainly of
- source_sentence: What should the report on income tax information include as per
Article 48c?
sentences:
- '(d)
seal any business premises and books or records for the period of time of, and
to the extent necessary for, the inspection.
3.
The undertaking or association of undertakings shall submit to inspections ordered
by decision of the Commission. The officials and other accompanying persons authorised
by the Commission to conduct an inspection shall exercise their powers upon production
of a Commission decision:
(a)
specifying the subject matter and purpose of the inspection;
(b)
containing a statement that, pursuant to Article 16, a lack of cooperation allows
the Commission to take a decision on the basis of the facts that are available
to it;
(c)'
- 'By way of derogation from Article 10c, the Member States concerned may only give
transitional free allocation to installations in accordance with that Article
for investments carried out until 31 December 2024. Any allowances available to
the Member States concerned in accordance with Article 10c for the period from
2021 to 2030 that are not used for such investments shall, in the proportion determined
by the respective Member State:
(a)
be added to the total quantity of allowances that the Member State concerned is
to auction pursuant to Article 10(2); or
(b)'
- '7.
Member States shall require subsidiary undertakings or branches not subject to
the provisions of paragraphs 4 and 5 of this Article to publish and make accessible
a report on income tax information where such subsidiary undertakings or branches
serve no other objective than to circumvent the reporting requirements set out
in this Chapter.
Article 48c
Content of the report on income tax information
1.
The report on income tax information required under Article 48b shall include
information relating to all the activities of the standalone undertaking or ultimate
parent undertaking, including those of all affiliated undertakings consolidated
in the financial statements in respect of the relevant financial year.
2.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.7
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.33333333333333337
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8892789260714373
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.85
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.85
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision fc74610d18462d218e312aa986ec5c8a75a98152 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'What should the report on income tax information include as per Article 48c?',
'7.\n\nMember States shall require subsidiary undertakings or branches not subject to the provisions of paragraphs 4 and 5 of this Article to publish and make accessible a report on income tax information where such subsidiary undertakings or branches serve no other objective than to circumvent the reporting requirements set out in this Chapter.\n\nArticle 48c\n\nContent of the report on income tax information\n\n1.\n\nThe report on income tax information required under Article 48b shall include information relating to all the activities of the standalone undertaking or ultimate parent undertaking, including those of all affiliated undertakings consolidated in the financial statements in respect of the relevant financial year.\n\n2.',
'(d)\n\nseal any business premises and books or records for the period of time of, and to the extent necessary for, the inspection.\n\n3.\n\nThe undertaking or association of undertakings shall submit to inspections ordered by decision of the Commission. The officials and other accompanying persons authorised by the Commission to conduct an inspection shall exercise their powers upon production of a Commission decision:\n\n(a)\n\nspecifying the subject matter and purpose of the inspection;\n\n(b)\n\ncontaining a statement that, pursuant to Article 16, a lack of cooperation allows the Commission to take a decision on the basis of the facts that are available to it;\n\n(c)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.7 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.7 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.8893** |
| cosine_mrr@10 | 0.85 |
| cosine_map@100 | 0.85 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 10 training samples
* Columns: <code>query_text</code> and <code>doc_text</code>
* Approximate statistics based on the first 10 samples:
| | query_text | doc_text |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 17 tokens</li><li>mean: 38.6 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 111 tokens</li><li>mean: 231.0 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query_text | doc_text |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What are the requirements for Member States regarding the establishment of AI regulatory sandboxes, including the timeline for operational readiness and the possibility of joint establishment with other Member States?</code> | <code>1. Member States shall ensure that their competent authorities establish at least one AI regulatory sandbox at national level, which shall be operational by 2 August 2026. That sandbox may also be established jointly with the competent authorities of other Member States. The Commission may provide technical support, advice and tools for the establishment and operation of AI regulatory sandboxes.<br><br>The obligation under the first subparagraph may also be fulfilled by participating in an existing sandbox in so far as that participation provides an equivalent level of national coverage for the participating Member States.</code> |
| <code>Member States must provide updates on their national energy and climate strategies, detailing the anticipated energy savings from 2021 to 2030. They are also obligated to report on the necessary energy savings and the policies intended to achieve these goals. If assessments reveal that a Member State's measures are inadequate to meet energy savings targets, the Commission may issue recommendations for improvement. Additionally, any shortfall in energy savings must be addressed in subsequent obligation periods.</code> | <code>9. Member States shall apply and calculate the effect of the options chosen under paragraph 8 for the period referred to in paragraph 1, first subparagraph, points (a) and (b)(i), separately:<br><br>(a) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (a), Member States may make use of the options listed in paragraph 8, points (a) to (d). All the options chosen under paragraph 8 taken together shall amount to no more than 25 % of the amount of energy savings referred to in paragraph 1, first subparagraph, point (a); (b) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (b)(i), Member States may make use of the options listed in paragraph 8, points (b) to (g), provided that the individual actions referred to in paragraph 8, point (d), continue to have a verifiable and measurable impact after 31 December 2020. All...</code> |
| <code>What is the functional definition of a remote biometric identification system, and how does it operate in terms of identifying individuals without their active participation?</code> | <code>(17) The notion of ‘remote biometric identification system’ referred to in this Regulation should be defined functionally, as an AI system intended for the identification of natural persons without their active involvement, typically at a distance, through the comparison of a person’s biometric data with the biometric data contained in a reference database, irrespectively of the particular technology, processes or types of biometric data used. Such remote biometric identification systems are typically used to perceive multiple persons or their behaviour simultaneously in order to facilitate significantly the identification of natural persons without their active involvement. This excludes AI systems intended to be used for biometric</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| -1 | -1 | 0.8893 |
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 4.0.2
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 0.26.0
- Datasets: 3.1.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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alex2020/Qwen2-0.5-DPO | alex2020 | 2025-06-16T18:56:47Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"trl",
"dpo",
"license:mit",
"region:us"
] | null | 2025-06-16T14:59:01Z | ---
license: mit
tags:
- trl
- dpo
---
|
shivanshu0701/qwen2-7b-instruct-trl-sft-ChartQA | shivanshu0701 | 2025-06-16T18:44:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T17:56:52Z | ---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: qwen2-7b-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen2-7b-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="shivanshu0701/qwen2-7b-instruct-trl-sft-ChartQA", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/shivanshugupta768-iit-kharagpur/qwen2-7b-instruct-trl-sft-od/runs/1ne3ggcm)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0.dev0
- Transformers: 4.53.0.dev0
- Pytorch: 2.4.1+cu121
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
RichardErkhov/general-preference_-_SPPO-Llama-3-8B-Instruct-GPM-2B-8bits | RichardErkhov | 2025-06-16T18:43:03Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2410.02197",
"arxiv:2405.00675",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-04-03T05:16:52Z | ---
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Paper: [Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment](https://huggingface.co/papers/2410.02197)
Code: https://github.com/general-preference/general-preference-model
SPPO-Llama-3-8B-Instruct-GPM-2B - bnb 8bits
- Model creator: https://huggingface.co/general-preference/
- Original model: https://huggingface.co/general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B/
Original model description:
---
language:
- en
license: apache-2.0
datasets:
- openbmb/UltraFeedback
pipeline_tag: text-generation
model-index:
- name: SPPO-Llama-3-8B-Instruct-GPM-2B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 60.24
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 27.89
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 8.01
name: exact match
source:
url: https://huggingface.co/spaces/open-llm_leaderboard/open_llm_leaderboard?query=general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 1.23
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 3.19
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 29.53
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B
name: Open LLM Leaderboard
---
General Preference Modeling with Preference Representations for Aligning Language Models (https://arxiv.org/abs/2410.02197)
# SPPO-Llama-3-8B-Instruct-GPM-2B
This model was developed using [SPPO](https://arxiv.org/abs/2405.00675) at iteration 3 and the [General Preference representation Model (GPM)](https://arxiv.org/abs/2410.02197) (specifically, using [GPM-Gemma-2B](https://huggingface.co/general-preference/GPM-Gemma-2B)), based on the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) architecture as starting point. We utilized the prompt sets from the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, splited to 3 parts for 3 iterations by [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset). All responses used are synthetic.
## Links to Other Models
- [SPPO-Llama-3-8B-Instruct-GPM-2B](https://huggingface.co/general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B)
- [GPO-Llama-3-8B-Instruct-GPM-2B](https://huggingface.co/general-preference/GPO-Llama-3-8B-Instruct-GPM-2B)
### Model Description
- Model type: A 8B parameter GPT-like model fine-tuned on synthetic datasets.
- Language(s) (NLP): Primarily English
- License: Apache-2.0
- Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct
## [AlpacaEval Leaderboard Evaluation Results](https://tatsu-lab.github.io/alpaca_eval/)
| Model | LC. Win Rate | Win Rate | Avg. Length |
|-------------------------------------------|:------------:|:--------:|:-----------:|
|[SPPO-Llama-3-8B-Instruct-GPM-2B](https://huggingface.co/general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B) |35.30 | 45.44 | 2490
## [Open LLM Leaderboard Evaluation Results](https://github.com/EleutherAI/lm-evaluation-harness)
Results are reported by using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.1
| | arc_challenge | truthfulqa_mc2 | winogrande | gsm8k | hellaswag | mmlu | average |
|--------|---------------|----------------|------------|-------|-----------|-------|---------|
|[SPPO-Llama-3-8B-Instruct-GPM-2B](https://huggingface.co/general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B) | 62.03 | 52.95 | 76.56 | 75.36 | 78.57 | 65.66 | 68.52
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- eta: 1000
- per_device_train_batch_size: 8
- gradient_accumulation_steps: 1
- seed: 42
- distributed_type: deepspeed_zero3
- num_devices: 8
- optimizer: RMSProp
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_train_epochs: 6.0 (stop at epoch=1.0)
## Citation
```
@article{zhang2024general,
title={General Preference Modeling with Preference Representations for Aligning Language Models},
author={Zhang, Yifan and Zhang, Ge and Wu, Yue and Xu, Kangping and Gu, Quanquan},
journal={arXiv preprint arXiv:2410.02197},
year={2024}
}
``` |
duckasaurus/ppo-LunarLander-v2 | duckasaurus | 2025-06-16T18:10:52Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-16T18:04:14Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 250.18 +/- 28.37
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Khadija-Hashmi-Official-Viral-Videos/FULL.VIDEO.Khadija.Hashmi.Viral.Video.Tutorial.Official | Khadija-Hashmi-Official-Viral-Videos | 2025-06-16T17:53:26Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T17:53:19Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
Sociovestix/lenu_CH | Sociovestix | 2025-06-16T17:52:05Z | 38 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-12-14T14:43:42Z | ---
widget:
- text: CHF Capital SA
- text: Fondation de Technocorp Holding S.A.
- text: PICTET CH INSTITUTIONAL - SWISS EQUITIES TRACKER
- text: Tin Ventures GmbH
- text: The Trustee of the Michael Warshaw Trust
- text: Format Aktien Schweiz mittlere und kleinere Firmen
- text: Raiffeisenbank Leimental Genossenschaft
- text: Alliance universelle des Unions chrétiennes de jeunes gens
- text: Luzerner Pensionskasse (LUPK)
- text: Armin Biehler
- text: inosens ag
- text: Wecken & Cie.
- text: Kanton Zürich
- text: CAMET KlG
- text: Commune de Anières
- text: STWEG Hinter Chilen 1-12
- text: Spital Limmattal
- text: Ausgleichskasse des Kantons Appenzell A. Rh.
- text: World Trade Organization (WTO Pension Plan)
- text: Pensionskasse der Stadt Olten
- text: THE MACE TRUST
- text: Elips Life AG, Vaduz, Zweigniederlassung Schweiz in Zürich
- text: Mirabaud (CH) - LPP25+
- text: Confédération suisse
model-index:
- name: Sociovestix/lenu_CH
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: lenu
type: Sociovestix/lenu
config: CH
split: test
revision: f4d57b8d77a49ec5c62d899c9a213d23cd9f9428
metrics:
- type: f1
value: 0.9514824797843666
name: f1
- type: f1
value: 0.5094035486191425
name: f1 macro
args:
average: macro
---
# LENU - Legal Entity Name Understanding for Switzerland
A Bert (multilingual uncased) model fine-tuned on swiss legal entity names (jurisdiction CH) from the Global [Legal Entity Identifier](https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei)
(LEI) System with the goal to detect [Entity Legal Form (ELF) Codes](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list).
---------------
<h1 align="center">
<a href="https://gleif.org">
<img src="https://www.gleif.org/assets/build/img/logo/gleif-logo-new.svg" width="220px" style="display: inherit">
</a>
</h1><br>
<h3 align="center">in collaboration with</h3>
<h1 align="center">
<a href="https://sociovestix.com">
<img src="https://sociovestix.com/img/svl_logo_centered.svg" width="700px" style="width: 100%">
</a>
</h1><br>
---------------
## Model Description
<!-- Provide a longer summary of what this model is. -->
The model has been created as part of a collaboration of the [Global Legal Entity Identifier Foundation](https://gleif.org) (GLEIF) and
[Sociovestix Labs](https://sociovestix.com) with the goal to explore how Machine Learning can support in detecting the ELF Code solely based on an entity's legal name and legal jurisdiction.
See also the open source python library [lenu](https://github.com/Sociovestix/lenu), which supports in this task.
The model has been trained on the dataset [lenu](https://huggingface.co/datasets/Sociovestix), with a focus on swiss legal entities and ELF Codes within the Jurisdiction "CH".
- **Developed by:** [GLEIF](https://gleif.org) and [Sociovestix Labs](https://huggingface.co/Sociovestix)
- **License:** Creative Commons (CC0) license
- **Finetuned from model [optional]:** bert-base-multilingual-uncased
- **Resources for more information:** [Press Release](https://www.gleif.org/en/newsroom/press-releases/machine-learning-new-open-source-tool-developed-by-gleif-and-sociovestix-labs-enables-organizations-everywhere-to-automatically-)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
An entity's legal form is a crucial component when verifying and screening organizational identity.
The wide variety of entity legal forms that exist within and between jurisdictions, however, has made it difficult for large organizations to capture legal form as structured data.
The Jurisdiction specific models of [lenu](https://github.com/Sociovestix/lenu), trained on entities from
GLEIF’s Legal Entity Identifier (LEI) database of over two million records, will allow banks,
investment firms, corporations, governments, and other large organizations to retrospectively analyze
their master data, extract the legal form from the unstructured text of the legal name and
uniformly apply an ELF code to each entity type, according to the ISO 20275 standard.
# Licensing Information
This model, which is trained on LEI data, is available under Creative Commons (CC0) license.
See [gleif.org/en/about/open-data](https://gleif.org/en/about/open-data).
# Recommendations
Users should always consider the score of the suggested ELF Codes. For low score values it may be necessary to manually review the affected entities. |
Sociovestix/lenu_JP | Sociovestix | 2025-06-16T17:51:45Z | 35 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-12-14T16:31:39Z | ---
widget:
- text: 日本マスタートラスト信託銀行株式会社/400031066
- text: オージーケー技研株式会社
- text: GTAA SC Fund 4 (For QII Only)
- text: シティグループ・ジャパン・ホールディングス合同会社
- text: 大阪府信用農業協同組合連合会
- text: ディーケ・シッピング有限会社
- text: Wakwak Partners LLP
- text: ナカムラ・ユーエスエー・インク
model-index:
- name: Sociovestix/lenu_JP
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: lenu
type: Sociovestix/lenu
config: JP
split: test
revision: f4d57b8d77a49ec5c62d899c9a213d23cd9f9428
metrics:
- type: f1
value: 0.9807692307692307
name: f1
- type: f1
value: 0.7153092287372343
name: f1 macro
args:
average: macro
---
# LENU - Legal Entity Name Understanding for Japan
A [Japanese Bert](https://huggingface.co/cl-tohoku/bert-base-japanese) model fine-tuned on japanese legal entity names (jurisdiction JP) from the Global [Legal Entity Identifier](https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei)
(LEI) System with the goal to detect [Entity Legal Form (ELF) Codes](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list).
This model depends on `transformers["ja"]` being installed, as described [here](https://huggingface.co/docs/transformers/model_doc/bert-japanese).
---------------
<h1 align="center">
<a href="https://gleif.org">
<img src="https://www.gleif.org/assets/build/img/logo/gleif-logo-new.svg" width="220px" style="display: inherit">
</a>
</h1><br>
<h3 align="center">in collaboration with</h3>
<h1 align="center">
<a href="https://sociovestix.com">
<img src="https://sociovestix.com/img/svl_logo_centered.svg" width="700px" style="width: 100%">
</a>
</h1><br>
---------------
## Model Description
<!-- Provide a longer summary of what this model is. -->
The model has been created as part of a collaboration of the [Global Legal Entity Identifier Foundation](https://gleif.org) (GLEIF) and
[Sociovestix Labs](https://sociovestix.com) with the goal to explore how Machine Learning can support in detecting the ELF Code solely based on an entity's legal name and legal jurisdiction.
See also the open source python library [lenu](https://github.com/Sociovestix/lenu), which supports in this task.
The model has been trained on the dataset [lenu](https://huggingface.co/datasets/Sociovestix), with a focus on japanese legal entities and ELF Codes within the Jurisdiction "JP".
- **Developed by:** [GLEIF](https://gleif.org) and [Sociovestix Labs](https://huggingface.co/Sociovestix)
- **License:** Creative Commons (CC0) license
- **Finetuned from model [optional]:** cl-tohoku/bert-base-japanese
- **Resources for more information:** [Press Release](https://www.gleif.org/en/newsroom/press-releases/machine-learning-new-open-source-tool-developed-by-gleif-and-sociovestix-labs-enables-organizations-everywhere-to-automatically-)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
An entity's legal form is a crucial component when verifying and screening organizational identity.
The wide variety of entity legal forms that exist within and between jurisdictions, however, has made it difficult for large organizations to capture legal form as structured data.
The Jurisdiction specific models of [lenu](https://github.com/Sociovestix/lenu), trained on entities from
GLEIF’s Legal Entity Identifier (LEI) database of over two million records, will allow banks,
investment firms, corporations, governments, and other large organizations to retrospectively analyze
their master data, extract the legal form from the unstructured text of the legal name and
uniformly apply an ELF code to each entity type, according to the ISO 20275 standard.
# Licensing Information
This model, which is trained on LEI data, is available under Creative Commons (CC0) license.
See [gleif.org/en/about/open-data](https://gleif.org/en/about/open-data).
# Recommendations
Users should always consider the score of the suggested ELF Codes. For low score values it may be necessary to manually review the affected entities. |
sebaseco/Sebastian | sebaseco | 2025-06-16T17:44:48Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-16T16:59:04Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
TV-nulook-india-viral-videos-TV/Full.Clip.nulook.india.Viral.Video.Official | TV-nulook-india-viral-videos-TV | 2025-06-16T17:40:29Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T17:40:01Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
NEW-SMS-Rani/VIDEO.New.Sms.Rani.Viral.Video.Tutorials.Official | NEW-SMS-Rani | 2025-06-16T17:36:24Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T17:36:17Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
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|
Juchas/whisper-tiny-minds14-en-us | Juchas | 2025-06-16T17:35:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-06-16T17:04:46Z | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
model-index:
- name: whisper-tiny-minds14-en-us
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-minds14-en-us
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ncauchi1/cv_pointing_model_2 | ncauchi1 | 2025-06-16T17:33:45Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"en",
"dataset:ncauchi1/pointing_demo_diverse",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"license:apache-2.0",
"text-generation-inference... | image-text-to-text | 2025-06-11T18:27:57Z | ---
library_name: transformers
license: apache-2.0
datasets:
- ncauchi1/pointing_demo_diverse
language:
- en
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Used to point to voltage peaks in Cyclic Voltammetry graphs, fine tuned to return peak location in pixel coordinates with XML tags.
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Training Details
Fine tuned on hf dataset ncauchi1/pointing_demo_diverse on 10k samples.
Base Model is Qwen2.5 VL 3B, trained in two parts of 5k each.
Dataset was improved from previous version, raw data was normalized to remove issues with scaling on graph, then randomly re-scaled to provide variation. Sample CV's with no peaks, and graphs with different nubers of CV's were added for more variation.
Training logs:
https://wandb.ai/ncauchi-university-of-maryland/huggingface/runs/to254a01/logs \
https://wandb.ai/ncauchi-university-of-maryland/huggingface/runs/rhvllmjz/logs
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [ --torch_dtype bfloat16 \
--num_train_epochs 5 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--learning_rate 1e-4 \
--freeze_vit false \
--freeze_aligner false \
--lora_rank 16 \
--lora_alpha 32 \
--gradient_accumulation_steps 1 \
--eval_steps 20 \
--save_steps 100 \
--save_total_limit 2 \
--logging_steps 5 \
--max_length 8192 \
--output_dir output \
--warmup_ratio 0.05 \
--dataloader_num_workers 4 \
--dataset_num_proc 4 \
--deepspeed zero2 \
--save_only_model true \
--use_hf true \
--report_to wandb] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
## Evaluation
Model was evaluated on test set and holdout set. (holdout set comprised of raw data that was not used to generate train/test set)
Model scored 98% Accuracy on test set and 96% accuracy on holdout set. *Holdout set was based on old dataset so there were no samples without peaks
Most errors seemed reasonable, sometimes having trouble identifying 'lower' and 'upper peaks', missing small peaks, or mistaking other curve features for peaks. Compared to previous mode never guessed peaks off of the graph and generalized much better (previous model scored ~68% on holdout set)
Model was also evaluated on general CV questions dataset (bxw315-umd/general-cv-questions) and scored _20% (prob to answer correctly) compared to 23.3%
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
chengwendong/controlnet_canny_stable_video_diffusion_xl | chengwendong | 2025-06-16T17:30:27Z | 0 | 1 | diffusers | [
"diffusers",
"safetensors",
"image-to-video",
"en",
"arxiv:1910.09700",
"base_model:stabilityai/stable-video-diffusion-img2vid-xt",
"base_model:finetune:stabilityai/stable-video-diffusion-img2vid-xt",
"license:apache-2.0",
"region:us"
] | image-to-video | 2025-06-16T12:46:16Z | ---
license: apache-2.0
language:
- en
base_model:
- stabilityai/stable-video-diffusion-img2vid-xt
pipeline_tag: image-to-video
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Using edge signals to control stable-video-diffusion-xl generation, this model can be loaded.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
kalai4u/llama3-form-gen-v2 | kalai4u | 2025-06-16T17:27:23Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2025-06-16T17:22:43Z | ---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama3-form-gen-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3-form-gen-v2
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4226
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9 | 1.0 | 10 | 0.8046 |
| 0.7176 | 2.0 | 20 | 0.6736 |
| 0.5965 | 3.0 | 30 | 0.5761 |
| 0.5042 | 4.0 | 40 | 0.5049 |
| 0.4378 | 5.0 | 50 | 0.4568 |
| 0.3978 | 6.0 | 60 | 0.4299 |
| 0.3796 | 7.0 | 70 | 0.4226 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |
Arovi-Nusrat-Ridhi-Viral-Original-Videos/FULL.VIDEO.Arovi.Nusrat.Ridhi.Viral.Video.Tutorial.Official | Arovi-Nusrat-Ridhi-Viral-Original-Videos | 2025-06-16T17:24:55Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T17:24:47Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
neojelll-7/lora-adapter-demo | neojelll-7 | 2025-06-16T17:18:03Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2025-06-16T17:15:40Z | ---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
asdfre453/CFRO | asdfre453 | 2025-06-16T17:13:34Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-16T16:50:01Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: CFRO
---
# Cfro
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `CFRO` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CFRO",
"lora_weights": "https://huggingface.co/asdfre453/CFRO/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('asdfre453/CFRO', weight_name='lora.safetensors')
image = pipeline('CFRO').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/asdfre453/CFRO/discussions) to add images that show off what you’ve made with this LoRA.
|
lynda-tabuya-Official-Viral-Videos/FULL.VIDEO.lynda.tabuya.Viral.Video.Tutorial.Official | lynda-tabuya-Official-Viral-Videos | 2025-06-16T17:08:29Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T17:08:22Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
fransanchezoria/aressanch | fransanchezoria | 2025-06-16T17:06:53Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-16T16:41:10Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: aressanch
---
# Aressanch
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `aressanch` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "aressanch",
"lora_weights": "https://huggingface.co/fransanchezoria/aressanch/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('fransanchezoria/aressanch', weight_name='lora.safetensors')
image = pipeline('aressanch').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/fransanchezoria/aressanch/discussions) to add images that show off what you’ve made with this LoRA.
|
MAAT-EL-DUAT/INFILTRATOR-9000 | MAAT-EL-DUAT | 2025-06-16T16:56:30Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T16:55:01Z |
[](http://www.youtube.com/watch?v=D--PppeFwUE "Video Title")
DEEP PSYCHOLOGICAL WARFARE AGENT
SABATEUR INSIDE THE WIRE
INNER DEFENSES ERADICATED
IT IS RAINING BLOOD |
mlfoundations-dev/Qwen2.5-7B-Instruct_openthoughts3_math_100k_annotated_QwQ-32B | mlfoundations-dev | 2025-06-16T16:52:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endp... | text-generation | 2025-06-16T16:49:47Z | ---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Qwen2.5-7B-Instruct_openthoughts3_math_100k_annotated_QwQ-32B
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Qwen2.5-7B-Instruct_openthoughts3_math_100k_annotated_QwQ-32B
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/openthoughts3_math_100k_annotated_QwQ-32B dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 512
- total_train_batch_size: 512
- total_eval_batch_size: 4096
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.0
|
hjc4869/Magistral-Small-2506-w4g32 | hjc4869 | 2025-06-16T16:48:25Z | 0 | 0 | null | [
"safetensors",
"gguf",
"base_model:mistralai/Magistral-Small-2506",
"base_model:quantized:mistralai/Magistral-Small-2506",
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T16:26:14Z | ---
license: apache-2.0
base_model:
- mistralai/Magistral-Small-2506
--- |
New-tutorial-minahil-malik-virals-Video/FULL.VIDEO.Minahil.Malik.Viral.Video.Tutorial.Official | New-tutorial-minahil-malik-virals-Video | 2025-06-16T16:43:56Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T16:39:59Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
sil-ai/madlad400-finetuned-tpi-ong | sil-ai | 2025-06-16T16:38:14Z | 54 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | null | 2025-06-05T19:21:59Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-ong
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# madlad400-finetuned-tpi-ong
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4484
- Chrf: 10.3456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sshinCPN/CMuSeNet | sshinCPN | 2025-06-16T16:30:55Z | 0 | 0 | null | [
"license:gpl",
"region:us"
] | null | 2025-04-15T16:41:25Z | ---
license: gpl
---
# CMuSeNet
> Complex-Valued Multi-Signal Segmentation Network for Cognitive Radio Systems.
---
[](https://github.com/UNL-CPN-Lab/CMuSeNet)
[](https://github.com/UNL-CPN-Lab/CMuSeNet)
[](https://doi.org/10.48550/arXiv.2506.11048)
[](https://ieee-dataport.org/documents/cv-musenet-complex-valued-multi-signal-segmentation-dataset-0)
[]()
---
## Overview
**CMuSeNet** is a Complex-Valued Neural Network (CVNN)-based residual architecture designed for wideband spectrum segmentation in challenging low-SNR environments.
It leverages complex signal properties (phase, amplitude) inherently using complex-valued convolutions and introduces:
- **Complex-Valued Fourier Spectrum Focal Loss (CFL)** for robust low-SNR training
- **Complex Plane Intersection-over-Union (CIoU)** for accurate segmentation evaluation
- **Residual CVNN architecture** for enhanced feature extraction
<img src="assets/overview.png" alt="CMuSeNet Overview" width="400"/>
**Key highlights:**
- Up to **9.2%** improvement in segmentation accuracy over RVNNs
- **99.4%** Average Accuracy over SNR of [-20, 10] dB with synthetic dataset and **98.98** Average Accuracy over SNR of [-10, 10] dB with Indoor OTA dataset
- **33.1%** reduction in total training time compared to RVNN models
- Achieves equivalent RVNN accuracy within **2 epochs** vs **27 epochs**
- Evaluated on Synthetic, Indoor Over-The-Air (OTA), and Real-World Broadband Irregularly-sampled Geographical Radio Environment Dataset (BIG-RED).
- Dataset publicly available on IEEE DataPort
## CMuSeNet Architecture
CMuSeNet employs a residual Complex-Valued Neural Network (CVNN) architecture based on complex convolutions, batch normalization, and CReLU activation.
FFT preprocessing preserves signal phase and amplitude in the frequency domain before feeding into the network.
<img src="assets/architecture_training.png" alt="CMuSeNet Architecture" width="400"/>
---
## Complex Fourier Spectrum Focal Loss (CFL)
CMuSeNet introduces a novel **Complex-Valued Fourier Spectrum Focal Loss (CFL)** to improve training for multi-signal segmentation under low-SNR conditions.
- Instead of reducing FFT outputs to real-valued magnitudes, CFL retains real and imaginary components separately.
- Applies a focal loss formulation on the **real** and **imaginary** parts of the Fourier spectrum.
- Focuses learning on **hard-to-detect weak signals** by dynamically adjusting the loss contribution.
- Allows the network to preserve **phase** and **amplitude** information crucial for accurate segmentation.
CFL enhances model robustness and convergence speed, achieving faster training and better low-SNR performance compared to real-valued losses.
<img src="assets/ComplexFL.png" alt="Complex Fourier Spectrum Focal Loss Concept" width="400"/>
---
## Complex Plane Intersection over Union (CIoU) Concept
Unlike traditional segmentation that treats outputs as magnitude-only, CMuSeNet evaluates predictions over the complex-valued Fourier spectrum using a novel CIoU metric.
This treats real and imaginary axes jointly, improving boundary detection for signals in low-SNR environments.
<img src="assets/ComplexIoU.png" alt="Complex IoU Concept" width="400"/>
Detailed technical description available in:
> **Reference:**
> Sangwon Shin, Mehmet C. Vuran, “I Can’t Believe It’s Not Real: CV-MuSeNet: Complex-Valued Multi-Signal Segmentation”, in *Proc. IEEE Dynamic Spectrum Access Networks (DySPAN'25)*, IEEE, May 2025.
---
## Model and Dataset Links
- [GitHub](https://github.com/UNL-CPN-Lab/CMuSeNet)
- [IEEE Dataport Dataset](https://ieee-dataport.org/documents/cv-musenet-complex-valued-multi-signal-segmentation-dataset-0)
- IEEE Xplore Publication: *(Link will be updated once available)*
- [arXiv](https://doi.org/10.48550/arXiv.2506.11048)
---
## Requirements
- CUDA-capable NVIDIA GPU (e.g., Tesla V100)
- Intel CPU with i7 or higher
- Python 3.8
- Jupyter Notebook
- Anaconda (preferred) or pip
## Evaluation setup
- Intel Xeon Silver 4110 CPU
- NVIDIA Tesla V100 - 16GB VRAM
- 187 GB RAM
---
## Installation
### Anaconda (Recommended)
```bash
conda create -n cmusenet python=3.8
conda activate cmusenet
conda install tqdm
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
conda install -c conda-forge tensorflow
conda install scikit-learn
conda install numpy scipy matplotlib -y
conda install git
pip install git+https://github.com/wavefrontshaping/complexPyTorch.git
conda install -c anaconda ipykernel
```
### Pure pip (Alternative)
```bash
python -m venv cmusenet-env
source cmusenet-env/bin/activate # (Linux/Mac) or .\cmusenet-env\Scripts\activate (Windows)
pip install tqdm
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
pip install tensorflow
pip install scikit-learn
pip install numpy scipy matplotlib
pip install gitpython
pip install git+https://github.com/wavefrontshaping/complexPyTorch.git
pip install ipykernel
```
---
## Quick Start
### Clone the Repository
```bash
git clone https://github.com/your_username/CMuSeNet.git
cd CMuSeNet
```
> Replace `your_username` with your GitHub ID if publishing!
### Launch Jupyter Notebook
```bash
jupyter notebook
```
Open and run:
- `Seek_VGG_ResNet_CVNN_RawComplexValue`
- `Seek_ResNet_CVNN_OTA`
- `BIGRED_Seek_ResNet_CVNN`
**Note:**
Ensure datasets are downloaded and change the dataset direction in the code.
---
## Training and Evaluation
Each notebook contains detailed blocks for:
- Loading synthetic, Indoor-OTA, or Broadband Irregularly-sampled Geographical Radio Environment Dataset BIG-RED data
- Model initialization
- Training with complex-valued loss
- Validation metrics tracking (CIoU, CFL)
Hyperparameters tuned for low-SNR robustness:
- Batch size = 64
- Early stopping patience = 3
- CFL loss parameters γ = 1, α = 3
Transfer learning supported from Synthetic → OTA → BIG-RED datasets.
---
## Additional Citations
This project also uses [complexPyTorch](https://github.com/wavefrontshaping/complexPyTorch):
```bibtex
@misc{meunier2023complexpytorch,
title={complexPyTorch},
author={S\u00e9bastien Meunier},
year={2023},
publisher={GitHub},
howpublished={\url{https://github.com/wavefrontshaping/complexPyTorch}}
}
## License
This project is licensed under the **GPL family** (General Public License) terms.
Details will be updated following IEEE publication.
---
## Citation
If you use this code, dataset, or model, please cite:
```bibtex
@inproceedings{shin2025cmusenet,
title={I Can't Believe It's Not Real: {CV-MuSeNet}: Complex-Valued Multi-Signal Segmentation},
author={Sangwon Shin and Mehmet C. Vuran},
booktitle={IEEE Dynamic Spectrum Access Networks (DySPAN)},
year={2025},
organization={IEEE}
}
```
## Acknowledgement
Office of Naval Research, NSWC Crane N00174-23-1-0007
This work relates to Department of Navy award N00174-23-1-0007 issued by the Office of Naval Research, NSWC Crane. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Office of Naval Research.
---
license: gpl-3.0
--- |
sapna-shah-Viral-Video-Original-Link-HD/FULL.VIDEO.sapna.shah.Viral.Video.Tutorial.Official | sapna-shah-Viral-Video-Original-Link-HD | 2025-06-16T16:30:19Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T16:30:13Z |
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OpenVINO/whisper-large-v3-int4-ov | OpenVINO | 2025-06-16T16:24:51Z | 47 | 1 | null | [
"openvino",
"whisper",
"audio",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu"... | automatic-speech-recognition | 2024-10-23T06:15:03Z | ---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- no
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
pipeline_tag: automatic-speech-recognition
license: apache-2.0
license_link: https://choosealicense.com/licenses/apache-2.0/
---
# whisper-large-v3-int4-ov
* Model creator: [OpenAI](https://huggingface.co/openai)
* Original model: [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3)
## Description
This is [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT4 by [NNCF](https://github.com/openvinotoolkit/nncf).
## Quantization Parameters
Weight compression was performed using `nncf.compress_weights` with the following parameters:
* mode: **INT4_ASYM**
* ratio: **1.0**
* group_size: **128**
For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html).
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2025.2.0 and higher
* Optimum Intel 1.23.0 and higher
## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from datasets import load_dataset
from transformers import AutoProcessor
from optimum.intel.openvino import OVModelForSpeechSeq2Seq
model_id = "OpenVINO/whisper-large-v3-int4-ov"
tokenizer = AutoProcessor.from_pretrained(model_id)
model = OVModelForSpeechSeq2Seq.from_pretrained(model_id)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]
input_features = tokenizer(
sample["audio"]["array"],
sampling_rate=sample["audio"]["sampling_rate"],
return_tensors="pt",
).input_features
outputs = model.generate(input_features)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
1. Install packages required for using OpenVINO GenAI.
```
pip install huggingface_hub
pip install -U --pre --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly openvino openvino-tokenizers openvino-genai
```
2. Download model from HuggingFace Hub
```
import huggingface_hub as hf_hub
model_id = "OpenVINO/whisper-large-v3-int4-ov"
model_path = "whisper-large-v3-int4-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
```
3. Run model inference:
```
import openvino_genai as ov_genai
import datasets
device = "CPU"
pipe = ov_genai.WhisperPipeline(model_path, device)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]["audio"]["array"]
print(pipe.generate(sample))
```
More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
## Limitations
Check the original model card for [original model card](https://huggingface.co/openai/whisper-large-v3) for limitations.
## Legal information
The original model is distributed under [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/openai/whisper-large-v3).
## Disclaimer
Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
|
AndrewL271828/experiment | AndrewL271828 | 2025-06-16T16:19:02Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T15:54:06Z | ---
license: apache-2.0
---
|
Bonnief/oromo-nllb-tokenizer | Bonnief | 2025-06-16T16:18:35Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T16:18:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
HiDream-ai/HiDream-I1-Dev | HiDream-ai | 2025-06-16T16:17:54Z | 6,839 | 149 | diffusers | [
"diffusers",
"safetensors",
"image-generation",
"HiDream.ai",
"text-to-image",
"en",
"arxiv:2505.22705",
"license:mit",
"diffusers:HiDreamImagePipeline",
"region:us"
] | text-to-image | 2025-04-06T07:46:12Z | ---
license: mit
tags:
- image-generation
- HiDream.ai
language:
- en
pipeline_tag: text-to-image
library_name: diffusers
---
`HiDream-I1` is a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds.
<span style="color: #FF5733; font-weight: bold">For more features and to experience the full capabilities of our product, please visit [https://vivago.ai/](https://vivago.ai/).</span>
## Project Updates
- 📝 **May 28, 2025**: We've released our technical report [HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer](https://arxiv.org/abs/2505.22705).
- 🚀 **April 28, 2025**: We've open-sourced the instruction-based-image-editing model [**HiDream-E1-Full**](https://github.com/HiDream-ai/HiDream-E1). Experience at [https://huggingface.co/spaces/HiDream-ai/HiDream-E1-Full](https://huggingface.co/spaces/HiDream-ai/HiDream-E1-Full)!.
## Key Features
- ✨ **Superior Image Quality** - Produces exceptional results across multiple styles including photorealistic, cartoon, artistic, and more. Achieves state-of-the-art HPS v2.1 score, which aligns with human preferences.
- 🎯 **Best-in-Class Prompt Following** - Achieves industry-leading scores on GenEval and DPG benchmarks, outperforming all other open-source models.
- 🔓 **Open Source** - Released under the MIT license to foster scientific advancement and enable creative innovation.
- 💼 **Commercial-Friendly** - Generated images can be freely used for personal projects, scientific research, and commercial applications.
## Quick Start
Please make sure you have installed [Flash Attention](https://github.com/Dao-AILab/flash-attention). We recommend CUDA version 12.4 for the manual installation.
```
pip install -r requirements.txt
```
Clone the GitHub repo:
```
git clone https://github.com/HiDream-ai/HiDream-I1
```
Then you can run the inference scripts to generate images:
```python
# For full model inference
python ./inference.py --model_type full
# For distilled dev model inference
python ./inference.py --model_type dev
# For distilled fast model inference
python ./inference.py --model_type fast
```
> **Note:** The inference script will automatically download `meta-llama/Meta-Llama-3.1-8B-Instruct` model files. If you encounter network issues, you can download these files ahead of time and place them in the appropriate cache directory to avoid download failures during inference.
## Gradio Demo
We also provide a Gradio demo for interactive image generation. You can run the demo with:
```python
python gradio_demo.py
```
## Evaluation Metrics
### DPG-Bench
| Model | Overall | Global | Entity | Attribute | Relation | Other |
|-----------------|-----------|-----------|-----------|-----------|-----------|-----------|
| PixArt-alpha | 71.11 | 74.97 | 79.32 | 78.60 | 82.57 | 76.96 |
| SDXL | 74.65 | 83.27 | 82.43 | 80.91 | 86.76 | 80.41 |
| DALL-E 3 | 83.50 | 90.97 | 89.61 | 88.39 | 90.58 | 89.83 |
| Flux.1-dev | 83.79 | 85.80 | 86.79 | 89.98 | 90.04 | 89.90 |
| SD3-Medium | 84.08 | 87.90 | 91.01 | 88.83 | 80.70 | 88.68 |
| Janus-Pro-7B | 84.19 | 86.90 | 88.90 | 89.40 | 89.32 | 89.48 |
| CogView4-6B | 85.13 | 83.85 | 90.35 | 91.17 | 91.14 | 87.29 |
| **HiDream-I1** | **85.89**| 76.44 | 90.22 | 89.48 | 93.74 | 91.83 |
### GenEval
| Model | Overall | Single Obj. | Two Obj. | Counting | Colors | Position | Color attribution |
|-----------------|----------|-------------|----------|----------|----------|----------|-------------------|
| SDXL | 0.55 | 0.98 | 0.74 | 0.39 | 0.85 | 0.15 | 0.23 |
| PixArt-alpha | 0.48 | 0.98 | 0.50 | 0.44 | 0.80 | 0.08 | 0.07 |
| Flux.1-dev | 0.66 | 0.98 | 0.79 | 0.73 | 0.77 | 0.22 | 0.45 |
| DALL-E 3 | 0.67 | 0.96 | 0.87 | 0.47 | 0.83 | 0.43 | 0.45 |
| CogView4-6B | 0.73 | 0.99 | 0.86 | 0.66 | 0.79 | 0.48 | 0.58 |
| SD3-Medium | 0.74 | 0.99 | 0.94 | 0.72 | 0.89 | 0.33 | 0.60 |
| Janus-Pro-7B | 0.80 | 0.99 | 0.89 | 0.59 | 0.90 | 0.79 | 0.66 |
| **HiDream-I1** | **0.83**| 1.00 | 0.98 | 0.79 | 0.91 | 0.60 | 0.72 |
### HPSv2.1 benchmark
| Model | Averaged | Animation | Concept-art | Painting | Photo |
|-------------------------|----------------|------------|---------------|--------------|------------|
| Stable Diffusion v2.0 | 26.38 | 27.09 | 26.02 | 25.68 | 26.73 |
| Midjourney V6 | 30.29 | 32.02 | 30.29 | 29.74 | 29.10 |
| SDXL | 30.64 | 32.84 | 31.36 | 30.86 | 27.48 |
| Dall-E3 | 31.44 | 32.39 | 31.09 | 31.18 | 31.09 |
| SD3 | 31.53 | 32.60 | 31.82 | 32.06 | 29.62 |
| Midjourney V5 | 32.33 | 34.05 | 32.47 | 32.24 | 30.56 |
| CogView4-6B | 32.31 | 33.23 | 32.60 | 32.89 | 30.52 |
| Flux.1-dev | 32.47 | 33.87 | 32.27 | 32.62 | 31.11 |
| stable cascade | 32.95 | 34.58 | 33.13 | 33.29 | 30.78 |
| **HiDream-I1** | **33.82** | 35.05 | 33.74 | 33.88 | 32.61 |
## License Agreement
The Transformer models in this repository are licensed under the MIT License. The VAE is from `FLUX.1 [schnell]`, and the text encoders from `google/t5-v1_1-xxl` and `meta-llama/Meta-Llama-3.1-8B-Instruct`. Please follow the license terms specified for these components. You own all content you create with this model. You can use your generated content freely, but you must comply with this license agreement. You are responsible for how you use the models. Do not create illegal content, harmful material, personal information that could harm others, false information, or content targeting vulnerable groups.
## Acknowledgements
- The VAE component is from `FLUX.1 [schnell]`, licensed under Apache 2.0.
- The text encoders are from `google/t5-v1_1-xxl` (licensed under Apache 2.0) and `meta-llama/Meta-Llama-3.1-8B-Instruct` (licensed under the Llama 3.1 Community License Agreement).
## Citation
```bibtex
@article{hidreami1technicalreport,
title={HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer},
author={Cai, Qi and Chen, Jingwen and Chen, Yang and Li, Yehao and Long, Fuchen and Pan, Yingwei and Qiu, Zhaofan and Zhang, Yiheng and Gao, Fengbin and Xu, Peihan and others},
journal={arXiv preprint arXiv:2505.22705},
year={2025}
}
```
|
quanda-bench-test/gpt2-small-trex-ft | quanda-bench-test | 2025-06-16T16:16:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"nanogpt",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T05:32:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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Video-Mezzo-Fun/FULL.VIDEO.Mezzo.Fun.Viral.Video.Mezzo.Fun.Tutorial | Video-Mezzo-Fun | 2025-06-16T16:16:04Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T16:15:57Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
sachinkosepaul/Llama4-Scout17B-CeMAP-epoch1 | sachinkosepaul | 2025-06-16T16:15:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T16:15:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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## Model Examination [optional]
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OpenVINO/distil-whisper-large-v3-int8-ov | OpenVINO | 2025-06-16T16:09:53Z | 2,231 | 3 | transformers | [
"transformers",
"openvino",
"whisper",
"automatic-speech-recognition",
"audio",
"transformers.js",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-23T06:01:35Z | ---
language:
- en
tags:
- audio
- automatic-speech-recognition
- transformers.js
pipeline_tag: automatic-speech-recognition
license: mit
license_link: https://github.com/huggingface/distil-whisper/blob/main/LICENSE
library_name: transformers
---
# distil-whisper-large-v3-int8-ov
* Model creator: [Distil-whisper](https://huggingface.co/distil-whisper)
* Original model: [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)
## Description
This is [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).
## Quantization Parameters
Weight compression was performed using `nncf.compress_weights` with the following parameters:
* mode: **INT8_ASYM**
* group_size: **128**
For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html).
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2025.2.0 and higher
* Optimum Intel 1.23.0 and higher
## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from datasets import load_dataset
from transformers import AutoProcessor
from optimum.intel.openvino import OVModelForSpeechSeq2Seq
model_id = "OpenVINO/distil-whisper-large-v3-int8-ov"
tokenizer = AutoProcessor.from_pretrained(model_id)
model = OVModelForSpeechSeq2Seq.from_pretrained(model_id)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]
input_features = tokenizer(
sample["audio"]["array"],
sampling_rate=sample["audio"]["sampling_rate"],
return_tensors="pt",
).input_features
outputs = model.generate(input_features)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
1. Install packages required for using OpenVINO GenAI.
```
pip install huggingface_hub
pip install -U --pre --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly openvino openvino-tokenizers openvino-genai
```
2. Download model from HuggingFace Hub
```
import huggingface_hub as hf_hub
model_id = "OpenVINO/distil-whisper-large-v3-int8-ov"
model_path = "distil-whisper-large-v3-int8-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
```
3. Run model inference:
```
import openvino_genai as ov_genai
import datasets
device = "CPU"
pipe = ov_genai.WhisperPipeline(model_path, device)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]["audio"]["array"]
print(pipe.generate(sample))
```
More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
## Limitations
Check the original model card for [original model card](https://huggingface.co/distil-whisper/distil-large-v3) for limitations.
## Legal information
The original model is distributed under [mit](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) license. More details can be found in [original model card](https://huggingface.co/distil-whisper/distil-large-v3).
## Disclaimer
Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
|
OpenVINO/distil-whisper-large-v2-fp16-ov | OpenVINO | 2025-06-16T16:09:20Z | 57 | 0 | transformers | [
"transformers",
"openvino",
"whisper",
"automatic-speech-recognition",
"audio",
"transformers.js",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-22T13:46:03Z | ---
language:
- en
tags:
- audio
- automatic-speech-recognition
- transformers.js
pipeline_tag: automatic-speech-recognition
license: mit
license_link: https://github.com/huggingface/distil-whisper/blob/main/LICENSE
library_name: transformers
---
# distil-whisper-large-v2-fp16-ov
* Model creator: [Distil-whisper](https://huggingface.co/distil-whisper)
* Original model: [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2)
## Description
This is [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to FP16.
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2025.2.0 and higher
* Optimum Intel 1.23.0 and higher
## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from datasets import load_dataset
from transformers import AutoProcessor
from optimum.intel.openvino import OVModelForSpeechSeq2Seq
model_id = "OpenVINO/distil-whisper-large-v2-fp16-ov"
tokenizer = AutoProcessor.from_pretrained(model_id)
model = OVModelForSpeechSeq2Seq.from_pretrained(model_id)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]
input_features = tokenizer(
sample["audio"]["array"],
sampling_rate=sample["audio"]["sampling_rate"],
return_tensors="pt",
).input_features
outputs = model.generate(input_features)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
1. Install packages required for using OpenVINO GenAI.
```
pip install huggingface_hub
pip install -U --pre --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly openvino openvino-tokenizers openvino-genai
```
2. Download model from HuggingFace Hub
```
import huggingface_hub as hf_hub
model_id = "OpenVINO/distil-whisper-large-v2-fp16-ov"
model_path = "distil-whisper-large-v2-fp16-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
```
3. Run model inference:
```
import openvino_genai as ov_genai
import datasets
device = "CPU"
pipe = ov_genai.WhisperPipeline(model_path, device)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]["audio"]["array"]
print(pipe.generate(sample))
```
More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
## Limitations
Check the original model card for [original model card](https://huggingface.co/distil-whisper/distil-large-v2) for limitations.
## Legal information
The original model is distributed under [mit](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) license. More details can be found in [original model card](https://huggingface.co/distil-whisper/distil-large-v2).
## Disclaimer
Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
|
FilipT/ltgbert-100m-2024-gender-baseline-CDA-500 | FilipT | 2025-06-16T16:07:47Z | 0 | 0 | null | [
"pytorch",
"safetensors",
"ltgbert",
"masked-lm",
"fine-tuned",
"custom_code",
"base_model:babylm/ltgbert-100m-2024",
"base_model:finetune:babylm/ltgbert-100m-2024",
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T16:07:22Z | ---
base_model: babylm/ltgbert-100m-2024
tags: [masked-lm, fine-tuned, ltgbert]
license: apache-2.0
---
|
multimolecule/mrnafm | multimolecule | 2025-06-16T16:02:28Z | 0 | 0 | multimolecule | [
"multimolecule",
"pytorch",
"safetensors",
"rnafm",
"Biology",
"RNA",
"fill-mask",
"rna",
"dataset:multimolecule/rnacentral",
"arxiv:2204.00300",
"license:agpl-3.0",
"region:us"
] | fill-mask | 2025-06-16T16:01:36Z | ---
language: rna
tags:
- Biology
- RNA
license: agpl-3.0
datasets:
- multimolecule/rnacentral
library_name: multimolecule
pipeline_tag: fill-mask
mask_token: "<mask>"
widget:
- example_title: "Homo sapiens PRNP mRNA for prion"
text: "AGC<mask>CAUUAUGGCGAACCUUGGCUGCUG"
output:
- label: "AAA"
score: 0.05433480441570282
- label: "AUC"
score: 0.04437034949660301
- label: "AAU"
score: 0.03882088139653206
- label: "ACA"
score: 0.037016965448856354
- label: "ACC"
score: 0.03563101962208748
---
# mRNA-FM
Pre-trained model on mRNA CoDing Sequence (CDS) using a masked language modeling (MLM) objective.
## Disclaimer
This is an UNOFFICIAL implementation of the [Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions](https://doi.org/10.1101/2022.08.06.503062) by Jiayang Chen, Zhihang Hue, Siqi Sun, et al.
The OFFICIAL repository of RNA-FM is at [ml4bio/RNA-FM](https://github.com/ml4bio/RNA-FM).
> [!TIP]
> The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.
**The team releasing RNA-FM did not write this model card for this model so this model card has been written by the MultiMolecule team.**
## Model Details
RNA-FM is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process.
### Variants
- **[multimolecule/rnafm](https://huggingface.co/multimolecule/rnafm)**: The RNA-FM model pre-trained on non-coding RNA sequences.
- **[multimolecule/mrnafm](https://huggingface.co/multimolecule/mrnafm)**: The RNA-FM model pre-trained on messenger RNA sequences.
### Model Specification
<table>
<thead>
<tr>
<th>Variants</th>
<th>Num Layers</th>
<th>Hidden Size</th>
<th>Num Heads</th>
<th>Intermediate Size</th>
<th>Num Parameters (M)</th>
<th>FLOPs (G)</th>
<th>MACs (G)</th>
<th>Max Num Tokens</th>
</tr>
</thead>
<tbody>
<tr>
<td>RNA-FM</td>
<td rowspan="2">12</td>
<td>640</td>
<td rowspan="2">20</td>
<td rowspan="2">5120</td>
<td>99.52</td>
<td>25.68</td>
<td>12.83</td>
<td rowspan="2">1024</td>
</tr>
<tr>
<td>mRNA-FM</td>
<td>1280</td>
<td>239.25</td>
<td>61.43</td>
<td>30.7</td>
</tr>
</tbody>
</table>
### Links
- **Code**: [multimolecule.rnafm](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/rnafm)
- **Data**: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral)
- **Paper**: [Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions](https://doi.org/10.1101/2022.08.06.503062)
- **Developed by**: Jiayang Chen, Zhihang Hu, Siqi Sun, Qingxiong Tan, Yixuan Wang, Qinze Yu, Licheng Zong, Liang Hong, Jin Xiao, Tao Shen, Irwin King, Yu Li
- **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ESM](https://huggingface.co/facebook/esm2_t48_15B_UR50D)
- **Original Repository**: [ml4bio/RNA-FM](https://github.com/ml4bio/RNA-FM)
## Usage
The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip:
```bash
pip install multimolecule
```
### Direct Use
#### Masked Language Modeling
You can use this model directly with a pipeline for masked language modeling:
```python
>>> import multimolecule # you must import multimolecule to register models
>>> from transformers import pipeline
>>> unmasker = pipeline("fill-mask", model="multimolecule/mrnafm")
>>> unmasker("agc<mask>cauuauggcgaaccuuggcugcug")
[{'score': 0.05433480441570282,
'token': 6,
'token_str': 'AAA',
'sequence': 'AGC AAA CAU UAU GGC GAA CCU UGG CUG CUG'},
{'score': 0.04437034949660301,
'token': 22,
'token_str': 'AUC',
'sequence': 'AGC AUC CAU UAU GGC GAA CCU UGG CUG CUG'},
{'score': 0.03882088139653206,
'token': 9,
'token_str': 'AAU',
'sequence': 'AGC AAU CAU UAU GGC GAA CCU UGG CUG CUG'},
{'score': 0.037016965448856354,
'token': 11,
'token_str': 'ACA',
'sequence': 'AGC ACA CAU UAU GGC GAA CCU UGG CUG CUG'},
{'score': 0.03563101962208748,
'token': 12,
'token_str': 'ACC',
'sequence': 'AGC ACC CAU UAU GGC GAA CCU UGG CUG CUG'}]
```
#### RNA Secondary Structure Prediction
You can use this model to predict the secondary structure of an RNA sequence:
```python
>>> import multimolecule # you must import multimolecule to register models
>>> from transformers import pipeline
>>> predictor = pipeline("rna-secondary-structure", model="multimolecule/mrnafm")
>>> predictor("agcagucauuauggcgaa")
{'sequence': 'AGC AGU CAU UAU GGC GAA',
'secondary_structure': '((([(]',
'contact_map': [[0.5119704604148865, 0.5045265555381775, 0.494497150182724, 0.4931190013885498, 0.4915284812450409, 0.5020371675491333],
[0.5045265555381775, 0.5034880042076111, 0.5013145804405212, 0.49390116333961487, 0.5006486773490906, 0.49380120635032654],
[0.494497150182724, 0.5013145804405212, 0.5010323524475098, 0.5058367252349854, 0.5021511912345886, 0.49284809827804565],
[0.4931190013885498, 0.49390116333961487, 0.5058367252349854, 0.4988723397254944, 0.5004245042800903, 0.5055262446403503],
[0.4915284812450409, 0.5006486773490906, 0.5021511912345886, 0.5004245042800903, 0.4953134059906006, 0.5076138377189636],
[0.5020371675491333, 0.49380120635032654, 0.49284809827804565, 0.5055262446403503, 0.5076138377189636, 0.4958533048629761]]}
```
### Downstream Use
#### Extract Features
Here is how to use this model to get the features of a given sequence in PyTorch:
```python
from multimolecule import RnaTokenizer, RnaFmModel
tokenizer = RnaTokenizer.from_pretrained("multimolecule/mrnafm")
model = RnaFmModel.from_pretrained("multimolecule/mrnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
output = model(**input)
```
#### Sequence Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.
Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, RnaFmForSequencePrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/mrnafm")
model = RnaFmForSequencePrediction.from_pretrained("multimolecule/mrnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])
output = model(**input, labels=label)
```
#### Token Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression.
Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, RnaFmForTokenPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/mrnafm")
model = RnaFmForTokenPrediction.from_pretrained("multimolecule/mrnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))
output = model(**input, labels=label)
```
#### Contact Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.
Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, RnaFmForContactPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/mrnafm")
model = RnaFmForContactPrediction.from_pretrained("multimolecule/mrnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))
output = model(**input, labels=label)
```
## Training Details
RNA-FM used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling.
### Training Data
The RNA-FM model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral).
RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types.
RNA-FM applied [CD-HIT (CD-HIT-EST)](https://sites.google.com/view/cd-hit) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral. The final dataset contains 23.7 million non-redundant RNA sequences.
RNA-FM preprocessed all tokens by replacing "U"s with "T"s.
Note that during model conversions, "T" is replaced with "U". [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`.
### Training Procedure
#### Preprocessing
RNA-FM used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
#### Pre-training
The model was trained on 8 NVIDIA A100 GPUs with 80GiB memories.
- Learning rate: 1e-4
- Learning rate scheduler: Inverse square root
- Learning rate warm-up: 10,000 steps
- Weight decay: 0.01
## Citation
**BibTeX**:
```bibtex
@article{chen2022interpretable,
title={Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions},
author={Chen, Jiayang and Hu, Zhihang and Sun, Siqi and Tan, Qingxiong and Wang, Yixuan and Yu, Qinze and Zong, Licheng and Hong, Liang and Xiao, Jin and King, Irwin and others},
journal={arXiv preprint arXiv:2204.00300},
year={2022}
}
```
## Contact
Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card.
Please contact the authors of the [RNA-FM paper](https://doi.org/10.1101/2022.08.06.503062) for questions or comments on the paper/model.
## License
This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html).
```spdx
SPDX-License-Identifier: AGPL-3.0-or-later
```
|
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.15_0.15_0.15_epoch1 | MinaMila | 2025-06-16T16:01:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T15:59:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mezzo-fun-hd-viral-video/Latest.Full.Update.18.meezo.fun.video.meezo.fun.mezo.fun.meezo.fun | mezzo-fun-hd-viral-video | 2025-06-16T16:01:14Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T16:01:06Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
FarmerlineML/w2v-bert-2.0_yoruba | FarmerlineML | 2025-06-16T15:56:05Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/w2v-bert-2.0",
"base_model:finetune:facebook/w2v-bert-2.0",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-06-16T13:34:15Z | ---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-2.0_yoruba
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# w2v-bert-2.0_yoruba
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: inf
- eval_cer: 0.1351
- eval_wer: 0.4215
- eval_runtime: 132.66
- eval_samples_per_second: 38.173
- eval_steps_per_second: 4.772
- epoch: 0.8416
- step: 4800
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 16
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
phospho-app/gerotropic-ACT-so101_pnp-iphxh | phospho-app | 2025-06-16T15:55:44Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-16T15:41:50Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
DataLoader worker (pid(s) 6506, 6517, 6519, 6520, 6523, 6524) exited unexpectedly
```
## Training parameters:
- **Dataset**: [gerotropic/so101_pnp](https://huggingface.co/datasets/gerotropic/so101_pnp)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 60
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
OrcaDB/qwen2-1.5b | OrcaDB | 2025-06-16T15:53:30Z | 55,263 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"qwen2",
"feature-extraction",
"mteb",
"transformers",
"Qwen2",
"sentence-similarity",
"custom_code",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endp... | sentence-similarity | 2025-04-25T19:40:33Z | ---
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
license: apache-2.0
model-index:
- name: gte-qwen2-7B-instruct
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 83.98507462686567
- type: ap
value: 50.93015252587014
- type: f1
value: 78.50416599051215
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 96.61065
- type: ap
value: 94.89174052954196
- type: f1
value: 96.60942596940565
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 55.614000000000004
- type: f1
value: 54.90553480294904
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: map_at_1
value: 45.164
- type: map_at_10
value: 61.519
- type: map_at_100
value: 61.769
- type: map_at_1000
value: 61.769
- type: map_at_3
value: 57.443999999999996
- type: map_at_5
value: 60.058
- type: mrr_at_1
value: 46.088
- type: mrr_at_10
value: 61.861
- type: mrr_at_100
value: 62.117999999999995
- type: mrr_at_1000
value: 62.117999999999995
- type: mrr_at_3
value: 57.729
- type: mrr_at_5
value: 60.392
- type: ndcg_at_1
value: 45.164
- type: ndcg_at_10
value: 69.72
- type: ndcg_at_100
value: 70.719
- type: ndcg_at_1000
value: 70.719
- type: ndcg_at_3
value: 61.517999999999994
- type: ndcg_at_5
value: 66.247
- type: precision_at_1
value: 45.164
- type: precision_at_10
value: 9.545
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.443
- type: precision_at_5
value: 16.97
- type: recall_at_1
value: 45.164
- type: recall_at_10
value: 95.448
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 73.329
- type: recall_at_5
value: 84.851
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 50.511868162026175
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 45.007803189284004
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 64.55292107723382
- type: mrr
value: 77.66158818097877
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cos_sim_pearson
value: 85.65459047085452
- type: cos_sim_spearman
value: 82.10729255710761
- type: euclidean_pearson
value: 82.78079159312476
- type: euclidean_spearman
value: 80.50002701880933
- type: manhattan_pearson
value: 82.41372641383016
- type: manhattan_spearman
value: 80.57412509272639
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 87.30844155844156
- type: f1
value: 87.25307322443255
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 43.20754608934859
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 38.818037697335505
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 35.423
- type: map_at_10
value: 47.198
- type: map_at_100
value: 48.899
- type: map_at_1000
value: 49.004
- type: map_at_3
value: 43.114999999999995
- type: map_at_5
value: 45.491
- type: mrr_at_1
value: 42.918
- type: mrr_at_10
value: 53.299
- type: mrr_at_100
value: 54.032000000000004
- type: mrr_at_1000
value: 54.055
- type: mrr_at_3
value: 50.453
- type: mrr_at_5
value: 52.205999999999996
- type: ndcg_at_1
value: 42.918
- type: ndcg_at_10
value: 53.98
- type: ndcg_at_100
value: 59.57
- type: ndcg_at_1000
value: 60.879000000000005
- type: ndcg_at_3
value: 48.224000000000004
- type: ndcg_at_5
value: 50.998
- type: precision_at_1
value: 42.918
- type: precision_at_10
value: 10.299999999999999
- type: precision_at_100
value: 1.687
- type: precision_at_1000
value: 0.211
- type: precision_at_3
value: 22.842000000000002
- type: precision_at_5
value: 16.681
- type: recall_at_1
value: 35.423
- type: recall_at_10
value: 66.824
- type: recall_at_100
value: 89.564
- type: recall_at_1000
value: 97.501
- type: recall_at_3
value: 50.365
- type: recall_at_5
value: 57.921
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackEnglishRetrieval
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 33.205
- type: map_at_10
value: 44.859
- type: map_at_100
value: 46.135
- type: map_at_1000
value: 46.259
- type: map_at_3
value: 41.839
- type: map_at_5
value: 43.662
- type: mrr_at_1
value: 41.146
- type: mrr_at_10
value: 50.621
- type: mrr_at_100
value: 51.207
- type: mrr_at_1000
value: 51.246
- type: mrr_at_3
value: 48.535000000000004
- type: mrr_at_5
value: 49.818
- type: ndcg_at_1
value: 41.146
- type: ndcg_at_10
value: 50.683
- type: ndcg_at_100
value: 54.82
- type: ndcg_at_1000
value: 56.69
- type: ndcg_at_3
value: 46.611000000000004
- type: ndcg_at_5
value: 48.66
- type: precision_at_1
value: 41.146
- type: precision_at_10
value: 9.439
- type: precision_at_100
value: 1.465
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 22.59
- type: precision_at_5
value: 15.86
- type: recall_at_1
value: 33.205
- type: recall_at_10
value: 61.028999999999996
- type: recall_at_100
value: 78.152
- type: recall_at_1000
value: 89.59700000000001
- type: recall_at_3
value: 49.05
- type: recall_at_5
value: 54.836
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGamingRetrieval
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 41.637
- type: map_at_10
value: 55.162
- type: map_at_100
value: 56.142
- type: map_at_1000
value: 56.188
- type: map_at_3
value: 51.564
- type: map_at_5
value: 53.696
- type: mrr_at_1
value: 47.524
- type: mrr_at_10
value: 58.243
- type: mrr_at_100
value: 58.879999999999995
- type: mrr_at_1000
value: 58.9
- type: mrr_at_3
value: 55.69499999999999
- type: mrr_at_5
value: 57.284
- type: ndcg_at_1
value: 47.524
- type: ndcg_at_10
value: 61.305
- type: ndcg_at_100
value: 65.077
- type: ndcg_at_1000
value: 65.941
- type: ndcg_at_3
value: 55.422000000000004
- type: ndcg_at_5
value: 58.516
- type: precision_at_1
value: 47.524
- type: precision_at_10
value: 9.918000000000001
- type: precision_at_100
value: 1.276
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.765
- type: precision_at_5
value: 17.204
- type: recall_at_1
value: 41.637
- type: recall_at_10
value: 76.185
- type: recall_at_100
value: 92.149
- type: recall_at_1000
value: 98.199
- type: recall_at_3
value: 60.856
- type: recall_at_5
value: 68.25099999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGisRetrieval
revision: 5003b3064772da1887988e05400cf3806fe491f2
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 26.27
- type: map_at_10
value: 37.463
- type: map_at_100
value: 38.434000000000005
- type: map_at_1000
value: 38.509
- type: map_at_3
value: 34.226
- type: map_at_5
value: 36.161
- type: mrr_at_1
value: 28.588
- type: mrr_at_10
value: 39.383
- type: mrr_at_100
value: 40.23
- type: mrr_at_1000
value: 40.281
- type: mrr_at_3
value: 36.422
- type: mrr_at_5
value: 38.252
- type: ndcg_at_1
value: 28.588
- type: ndcg_at_10
value: 43.511
- type: ndcg_at_100
value: 48.274
- type: ndcg_at_1000
value: 49.975
- type: ndcg_at_3
value: 37.319
- type: ndcg_at_5
value: 40.568
- type: precision_at_1
value: 28.588
- type: precision_at_10
value: 6.893000000000001
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 16.347
- type: precision_at_5
value: 11.661000000000001
- type: recall_at_1
value: 26.27
- type: recall_at_10
value: 60.284000000000006
- type: recall_at_100
value: 81.902
- type: recall_at_1000
value: 94.43
- type: recall_at_3
value: 43.537
- type: recall_at_5
value: 51.475
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackMathematicaRetrieval
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 18.168
- type: map_at_10
value: 28.410000000000004
- type: map_at_100
value: 29.78
- type: map_at_1000
value: 29.892999999999997
- type: map_at_3
value: 25.238
- type: map_at_5
value: 26.96
- type: mrr_at_1
value: 23.507
- type: mrr_at_10
value: 33.382
- type: mrr_at_100
value: 34.404
- type: mrr_at_1000
value: 34.467999999999996
- type: mrr_at_3
value: 30.637999999999998
- type: mrr_at_5
value: 32.199
- type: ndcg_at_1
value: 23.507
- type: ndcg_at_10
value: 34.571000000000005
- type: ndcg_at_100
value: 40.663
- type: ndcg_at_1000
value: 43.236000000000004
- type: ndcg_at_3
value: 29.053
- type: ndcg_at_5
value: 31.563999999999997
- type: precision_at_1
value: 23.507
- type: precision_at_10
value: 6.654
- type: precision_at_100
value: 1.113
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 14.427999999999999
- type: precision_at_5
value: 10.498000000000001
- type: recall_at_1
value: 18.168
- type: recall_at_10
value: 48.443000000000005
- type: recall_at_100
value: 74.47
- type: recall_at_1000
value: 92.494
- type: recall_at_3
value: 33.379999999999995
- type: recall_at_5
value: 39.76
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackPhysicsRetrieval
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 32.39
- type: map_at_10
value: 44.479
- type: map_at_100
value: 45.977000000000004
- type: map_at_1000
value: 46.087
- type: map_at_3
value: 40.976
- type: map_at_5
value: 43.038
- type: mrr_at_1
value: 40.135
- type: mrr_at_10
value: 50.160000000000004
- type: mrr_at_100
value: 51.052
- type: mrr_at_1000
value: 51.087
- type: mrr_at_3
value: 47.818
- type: mrr_at_5
value: 49.171
- type: ndcg_at_1
value: 40.135
- type: ndcg_at_10
value: 50.731
- type: ndcg_at_100
value: 56.452000000000005
- type: ndcg_at_1000
value: 58.123000000000005
- type: ndcg_at_3
value: 45.507
- type: ndcg_at_5
value: 48.11
- type: precision_at_1
value: 40.135
- type: precision_at_10
value: 9.192
- type: precision_at_100
value: 1.397
- type: precision_at_1000
value: 0.169
- type: precision_at_3
value: 21.816
- type: precision_at_5
value: 15.476
- type: recall_at_1
value: 32.39
- type: recall_at_10
value: 63.597
- type: recall_at_100
value: 86.737
- type: recall_at_1000
value: 97.039
- type: recall_at_3
value: 48.906
- type: recall_at_5
value: 55.659000000000006
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackProgrammersRetrieval
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 28.397
- type: map_at_10
value: 39.871
- type: map_at_100
value: 41.309000000000005
- type: map_at_1000
value: 41.409
- type: map_at_3
value: 36.047000000000004
- type: map_at_5
value: 38.104
- type: mrr_at_1
value: 34.703
- type: mrr_at_10
value: 44.773
- type: mrr_at_100
value: 45.64
- type: mrr_at_1000
value: 45.678999999999995
- type: mrr_at_3
value: 41.705
- type: mrr_at_5
value: 43.406
- type: ndcg_at_1
value: 34.703
- type: ndcg_at_10
value: 46.271
- type: ndcg_at_100
value: 52.037
- type: ndcg_at_1000
value: 53.81700000000001
- type: ndcg_at_3
value: 39.966
- type: ndcg_at_5
value: 42.801
- type: precision_at_1
value: 34.703
- type: precision_at_10
value: 8.744
- type: precision_at_100
value: 1.348
- type: precision_at_1000
value: 0.167
- type: precision_at_3
value: 19.102
- type: precision_at_5
value: 13.836
- type: recall_at_1
value: 28.397
- type: recall_at_10
value: 60.299
- type: recall_at_100
value: 84.595
- type: recall_at_1000
value: 96.155
- type: recall_at_3
value: 43.065
- type: recall_at_5
value: 50.371
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 28.044333333333338
- type: map_at_10
value: 38.78691666666666
- type: map_at_100
value: 40.113
- type: map_at_1000
value: 40.22125
- type: map_at_3
value: 35.52966666666667
- type: map_at_5
value: 37.372749999999996
- type: mrr_at_1
value: 33.159083333333335
- type: mrr_at_10
value: 42.913583333333335
- type: mrr_at_100
value: 43.7845
- type: mrr_at_1000
value: 43.830333333333336
- type: mrr_at_3
value: 40.29816666666667
- type: mrr_at_5
value: 41.81366666666667
- type: ndcg_at_1
value: 33.159083333333335
- type: ndcg_at_10
value: 44.75750000000001
- type: ndcg_at_100
value: 50.13658333333334
- type: ndcg_at_1000
value: 52.037
- type: ndcg_at_3
value: 39.34258333333334
- type: ndcg_at_5
value: 41.93708333333333
- type: precision_at_1
value: 33.159083333333335
- type: precision_at_10
value: 7.952416666666667
- type: precision_at_100
value: 1.2571666666666668
- type: precision_at_1000
value: 0.16099999999999998
- type: precision_at_3
value: 18.303833333333337
- type: precision_at_5
value: 13.057083333333333
- type: recall_at_1
value: 28.044333333333338
- type: recall_at_10
value: 58.237249999999996
- type: recall_at_100
value: 81.35391666666666
- type: recall_at_1000
value: 94.21283333333334
- type: recall_at_3
value: 43.32341666666667
- type: recall_at_5
value: 49.94908333333333
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackStatsRetrieval
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 27.838
- type: map_at_10
value: 36.04
- type: map_at_100
value: 37.113
- type: map_at_1000
value: 37.204
- type: map_at_3
value: 33.585
- type: map_at_5
value: 34.845
- type: mrr_at_1
value: 30.982
- type: mrr_at_10
value: 39.105000000000004
- type: mrr_at_100
value: 39.98
- type: mrr_at_1000
value: 40.042
- type: mrr_at_3
value: 36.912
- type: mrr_at_5
value: 38.062000000000005
- type: ndcg_at_1
value: 30.982
- type: ndcg_at_10
value: 40.982
- type: ndcg_at_100
value: 46.092
- type: ndcg_at_1000
value: 48.25
- type: ndcg_at_3
value: 36.41
- type: ndcg_at_5
value: 38.379999999999995
- type: precision_at_1
value: 30.982
- type: precision_at_10
value: 6.534
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 15.745999999999999
- type: precision_at_5
value: 10.828
- type: recall_at_1
value: 27.838
- type: recall_at_10
value: 52.971000000000004
- type: recall_at_100
value: 76.357
- type: recall_at_1000
value: 91.973
- type: recall_at_3
value: 40.157
- type: recall_at_5
value: 45.147999999999996
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackTexRetrieval
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 19.059
- type: map_at_10
value: 27.454
- type: map_at_100
value: 28.736
- type: map_at_1000
value: 28.865000000000002
- type: map_at_3
value: 24.773999999999997
- type: map_at_5
value: 26.266000000000002
- type: mrr_at_1
value: 23.125
- type: mrr_at_10
value: 31.267
- type: mrr_at_100
value: 32.32
- type: mrr_at_1000
value: 32.394
- type: mrr_at_3
value: 28.894
- type: mrr_at_5
value: 30.281000000000002
- type: ndcg_at_1
value: 23.125
- type: ndcg_at_10
value: 32.588
- type: ndcg_at_100
value: 38.432
- type: ndcg_at_1000
value: 41.214
- type: ndcg_at_3
value: 27.938000000000002
- type: ndcg_at_5
value: 30.127
- type: precision_at_1
value: 23.125
- type: precision_at_10
value: 5.9639999999999995
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.148
- type: precision_at_3
value: 13.294
- type: precision_at_5
value: 9.628
- type: recall_at_1
value: 19.059
- type: recall_at_10
value: 44.25
- type: recall_at_100
value: 69.948
- type: recall_at_1000
value: 89.35300000000001
- type: recall_at_3
value: 31.114000000000004
- type: recall_at_5
value: 36.846000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackUnixRetrieval
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 28.355999999999998
- type: map_at_10
value: 39.055
- type: map_at_100
value: 40.486
- type: map_at_1000
value: 40.571
- type: map_at_3
value: 35.69
- type: map_at_5
value: 37.605
- type: mrr_at_1
value: 33.302
- type: mrr_at_10
value: 42.986000000000004
- type: mrr_at_100
value: 43.957
- type: mrr_at_1000
value: 43.996
- type: mrr_at_3
value: 40.111999999999995
- type: mrr_at_5
value: 41.735
- type: ndcg_at_1
value: 33.302
- type: ndcg_at_10
value: 44.962999999999994
- type: ndcg_at_100
value: 50.917
- type: ndcg_at_1000
value: 52.622
- type: ndcg_at_3
value: 39.182
- type: ndcg_at_5
value: 41.939
- type: precision_at_1
value: 33.302
- type: precision_at_10
value: 7.779999999999999
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.035
- type: precision_at_5
value: 12.873000000000001
- type: recall_at_1
value: 28.355999999999998
- type: recall_at_10
value: 58.782000000000004
- type: recall_at_100
value: 84.02199999999999
- type: recall_at_1000
value: 95.511
- type: recall_at_3
value: 43.126999999999995
- type: recall_at_5
value: 50.14999999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWebmastersRetrieval
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 27.391
- type: map_at_10
value: 37.523
- type: map_at_100
value: 39.312000000000005
- type: map_at_1000
value: 39.54
- type: map_at_3
value: 34.231
- type: map_at_5
value: 36.062
- type: mrr_at_1
value: 32.016
- type: mrr_at_10
value: 41.747
- type: mrr_at_100
value: 42.812
- type: mrr_at_1000
value: 42.844
- type: mrr_at_3
value: 39.129999999999995
- type: mrr_at_5
value: 40.524
- type: ndcg_at_1
value: 32.016
- type: ndcg_at_10
value: 43.826
- type: ndcg_at_100
value: 50.373999999999995
- type: ndcg_at_1000
value: 52.318
- type: ndcg_at_3
value: 38.479
- type: ndcg_at_5
value: 40.944
- type: precision_at_1
value: 32.016
- type: precision_at_10
value: 8.280999999999999
- type: precision_at_100
value: 1.6760000000000002
- type: precision_at_1000
value: 0.25
- type: precision_at_3
value: 18.05
- type: precision_at_5
value: 13.083
- type: recall_at_1
value: 27.391
- type: recall_at_10
value: 56.928999999999995
- type: recall_at_100
value: 85.169
- type: recall_at_1000
value: 96.665
- type: recall_at_3
value: 42.264
- type: recall_at_5
value: 48.556
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWordpressRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 18.398
- type: map_at_10
value: 27.929
- type: map_at_100
value: 29.032999999999998
- type: map_at_1000
value: 29.126
- type: map_at_3
value: 25.070999999999998
- type: map_at_5
value: 26.583000000000002
- type: mrr_at_1
value: 19.963
- type: mrr_at_10
value: 29.997
- type: mrr_at_100
value: 30.9
- type: mrr_at_1000
value: 30.972
- type: mrr_at_3
value: 27.264
- type: mrr_at_5
value: 28.826
- type: ndcg_at_1
value: 19.963
- type: ndcg_at_10
value: 33.678999999999995
- type: ndcg_at_100
value: 38.931
- type: ndcg_at_1000
value: 41.379
- type: ndcg_at_3
value: 28.000000000000004
- type: ndcg_at_5
value: 30.637999999999998
- type: precision_at_1
value: 19.963
- type: precision_at_10
value: 5.7299999999999995
- type: precision_at_100
value: 0.902
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 12.631
- type: precision_at_5
value: 9.057
- type: recall_at_1
value: 18.398
- type: recall_at_10
value: 49.254
- type: recall_at_100
value: 73.182
- type: recall_at_1000
value: 91.637
- type: recall_at_3
value: 34.06
- type: recall_at_5
value: 40.416000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
metrics:
- type: map_at_1
value: 19.681
- type: map_at_10
value: 32.741
- type: map_at_100
value: 34.811
- type: map_at_1000
value: 35.003
- type: map_at_3
value: 27.697
- type: map_at_5
value: 30.372
- type: mrr_at_1
value: 44.951
- type: mrr_at_10
value: 56.34400000000001
- type: mrr_at_100
value: 56.961
- type: mrr_at_1000
value: 56.987
- type: mrr_at_3
value: 53.681
- type: mrr_at_5
value: 55.407
- type: ndcg_at_1
value: 44.951
- type: ndcg_at_10
value: 42.905
- type: ndcg_at_100
value: 49.95
- type: ndcg_at_1000
value: 52.917
- type: ndcg_at_3
value: 36.815
- type: ndcg_at_5
value: 38.817
- type: precision_at_1
value: 44.951
- type: precision_at_10
value: 12.989999999999998
- type: precision_at_100
value: 2.068
- type: precision_at_1000
value: 0.263
- type: precision_at_3
value: 27.275
- type: precision_at_5
value: 20.365
- type: recall_at_1
value: 19.681
- type: recall_at_10
value: 48.272999999999996
- type: recall_at_100
value: 71.87400000000001
- type: recall_at_1000
value: 87.929
- type: recall_at_3
value: 32.653999999999996
- type: recall_at_5
value: 39.364
task:
type: Retrieval
- dataset:
config: default
name: MTEB DBPedia
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
split: test
type: mteb/dbpedia
metrics:
- type: map_at_1
value: 10.231
- type: map_at_10
value: 22.338
- type: map_at_100
value: 31.927
- type: map_at_1000
value: 33.87
- type: map_at_3
value: 15.559999999999999
- type: map_at_5
value: 18.239
- type: mrr_at_1
value: 75.0
- type: mrr_at_10
value: 81.303
- type: mrr_at_100
value: 81.523
- type: mrr_at_1000
value: 81.53
- type: mrr_at_3
value: 80.083
- type: mrr_at_5
value: 80.758
- type: ndcg_at_1
value: 64.625
- type: ndcg_at_10
value: 48.687000000000005
- type: ndcg_at_100
value: 52.791
- type: ndcg_at_1000
value: 60.041999999999994
- type: ndcg_at_3
value: 53.757999999999996
- type: ndcg_at_5
value: 50.76500000000001
- type: precision_at_1
value: 75.0
- type: precision_at_10
value: 38.3
- type: precision_at_100
value: 12.025
- type: precision_at_1000
value: 2.3970000000000002
- type: precision_at_3
value: 55.417
- type: precision_at_5
value: 47.5
- type: recall_at_1
value: 10.231
- type: recall_at_10
value: 27.697
- type: recall_at_100
value: 57.409
- type: recall_at_1000
value: 80.547
- type: recall_at_3
value: 16.668
- type: recall_at_5
value: 20.552
task:
type: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 61.365
- type: f1
value: 56.7540827912991
task:
type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
split: test
type: mteb/fever
metrics:
- type: map_at_1
value: 83.479
- type: map_at_10
value: 88.898
- type: map_at_100
value: 89.11
- type: map_at_1000
value: 89.12400000000001
- type: map_at_3
value: 88.103
- type: map_at_5
value: 88.629
- type: mrr_at_1
value: 89.934
- type: mrr_at_10
value: 93.91000000000001
- type: mrr_at_100
value: 93.937
- type: mrr_at_1000
value: 93.938
- type: mrr_at_3
value: 93.62700000000001
- type: mrr_at_5
value: 93.84599999999999
- type: ndcg_at_1
value: 89.934
- type: ndcg_at_10
value: 91.574
- type: ndcg_at_100
value: 92.238
- type: ndcg_at_1000
value: 92.45
- type: ndcg_at_3
value: 90.586
- type: ndcg_at_5
value: 91.16300000000001
- type: precision_at_1
value: 89.934
- type: precision_at_10
value: 10.555
- type: precision_at_100
value: 1.1159999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.588
- type: precision_at_5
value: 20.642
- type: recall_at_1
value: 83.479
- type: recall_at_10
value: 94.971
- type: recall_at_100
value: 97.397
- type: recall_at_1000
value: 98.666
- type: recall_at_3
value: 92.24799999999999
- type: recall_at_5
value: 93.797
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA2018
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
split: test
type: mteb/fiqa
metrics:
- type: map_at_1
value: 27.16
- type: map_at_10
value: 45.593
- type: map_at_100
value: 47.762
- type: map_at_1000
value: 47.899
- type: map_at_3
value: 39.237
- type: map_at_5
value: 42.970000000000006
- type: mrr_at_1
value: 52.623
- type: mrr_at_10
value: 62.637
- type: mrr_at_100
value: 63.169
- type: mrr_at_1000
value: 63.185
- type: mrr_at_3
value: 59.928000000000004
- type: mrr_at_5
value: 61.702999999999996
- type: ndcg_at_1
value: 52.623
- type: ndcg_at_10
value: 54.701
- type: ndcg_at_100
value: 61.263
- type: ndcg_at_1000
value: 63.134
- type: ndcg_at_3
value: 49.265
- type: ndcg_at_5
value: 51.665000000000006
- type: precision_at_1
value: 52.623
- type: precision_at_10
value: 15.185
- type: precision_at_100
value: 2.202
- type: precision_at_1000
value: 0.254
- type: precision_at_3
value: 32.767
- type: precision_at_5
value: 24.722
- type: recall_at_1
value: 27.16
- type: recall_at_10
value: 63.309000000000005
- type: recall_at_100
value: 86.722
- type: recall_at_1000
value: 97.505
- type: recall_at_3
value: 45.045
- type: recall_at_5
value: 54.02400000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA
revision: ab518f4d6fcca38d87c25209f94beba119d02014
split: test
type: mteb/hotpotqa
metrics:
- type: map_at_1
value: 42.573
- type: map_at_10
value: 59.373
- type: map_at_100
value: 60.292
- type: map_at_1000
value: 60.358999999999995
- type: map_at_3
value: 56.159000000000006
- type: map_at_5
value: 58.123999999999995
- type: mrr_at_1
value: 85.14500000000001
- type: mrr_at_10
value: 89.25999999999999
- type: mrr_at_100
value: 89.373
- type: mrr_at_1000
value: 89.377
- type: mrr_at_3
value: 88.618
- type: mrr_at_5
value: 89.036
- type: ndcg_at_1
value: 85.14500000000001
- type: ndcg_at_10
value: 68.95
- type: ndcg_at_100
value: 71.95
- type: ndcg_at_1000
value: 73.232
- type: ndcg_at_3
value: 64.546
- type: ndcg_at_5
value: 66.945
- type: precision_at_1
value: 85.14500000000001
- type: precision_at_10
value: 13.865
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 39.703
- type: precision_at_5
value: 25.718000000000004
- type: recall_at_1
value: 42.573
- type: recall_at_10
value: 69.325
- type: recall_at_100
value: 80.932
- type: recall_at_1000
value: 89.446
- type: recall_at_3
value: 59.553999999999995
- type: recall_at_5
value: 64.294
task:
type: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 95.8336
- type: ap
value: 93.78862962194073
- type: f1
value: 95.83192650728371
task:
type: Classification
- dataset:
config: default
name: MTEB MSMARCO
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
metrics:
- type: map_at_1
value: 23.075000000000003
- type: map_at_10
value: 36.102000000000004
- type: map_at_100
value: 37.257
- type: map_at_1000
value: 37.3
- type: map_at_3
value: 32.144
- type: map_at_5
value: 34.359
- type: mrr_at_1
value: 23.711
- type: mrr_at_10
value: 36.671
- type: mrr_at_100
value: 37.763999999999996
- type: mrr_at_1000
value: 37.801
- type: mrr_at_3
value: 32.775
- type: mrr_at_5
value: 34.977000000000004
- type: ndcg_at_1
value: 23.711
- type: ndcg_at_10
value: 43.361
- type: ndcg_at_100
value: 48.839
- type: ndcg_at_1000
value: 49.88
- type: ndcg_at_3
value: 35.269
- type: ndcg_at_5
value: 39.224
- type: precision_at_1
value: 23.711
- type: precision_at_10
value: 6.866999999999999
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 15.096000000000002
- type: precision_at_5
value: 11.083
- type: recall_at_1
value: 23.075000000000003
- type: recall_at_10
value: 65.756
- type: recall_at_100
value: 90.88199999999999
- type: recall_at_1000
value: 98.739
- type: recall_at_3
value: 43.691
- type: recall_at_5
value: 53.15800000000001
task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 97.69493844049248
- type: f1
value: 97.55048089616261
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 88.75968992248062
- type: f1
value: 72.26321223399123
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 82.40080699394754
- type: f1
value: 79.62590029057968
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 84.49562878278414
- type: f1
value: 84.0040193313333
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure
value: 39.386760057101945
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: v_measure
value: 37.89687154075537
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 33.94151656057482
- type: mrr
value: 35.32684700746953
task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
metrics:
- type: map_at_1
value: 6.239999999999999
- type: map_at_10
value: 14.862
- type: map_at_100
value: 18.955
- type: map_at_1000
value: 20.694000000000003
- type: map_at_3
value: 10.683
- type: map_at_5
value: 12.674
- type: mrr_at_1
value: 50.15500000000001
- type: mrr_at_10
value: 59.697
- type: mrr_at_100
value: 60.095
- type: mrr_at_1000
value: 60.129999999999995
- type: mrr_at_3
value: 58.35900000000001
- type: mrr_at_5
value: 58.839
- type: ndcg_at_1
value: 48.452
- type: ndcg_at_10
value: 39.341
- type: ndcg_at_100
value: 35.866
- type: ndcg_at_1000
value: 45.111000000000004
- type: ndcg_at_3
value: 44.527
- type: ndcg_at_5
value: 42.946
- type: precision_at_1
value: 50.15500000000001
- type: precision_at_10
value: 29.536
- type: precision_at_100
value: 9.142
- type: precision_at_1000
value: 2.2849999999999997
- type: precision_at_3
value: 41.899
- type: precision_at_5
value: 37.647000000000006
- type: recall_at_1
value: 6.239999999999999
- type: recall_at_10
value: 19.278000000000002
- type: recall_at_100
value: 36.074
- type: recall_at_1000
value: 70.017
- type: recall_at_3
value: 12.066
- type: recall_at_5
value: 15.254000000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
- type: map_at_1
value: 39.75
- type: map_at_10
value: 56.443
- type: map_at_100
value: 57.233999999999995
- type: map_at_1000
value: 57.249
- type: map_at_3
value: 52.032999999999994
- type: map_at_5
value: 54.937999999999995
- type: mrr_at_1
value: 44.728
- type: mrr_at_10
value: 58.939
- type: mrr_at_100
value: 59.489000000000004
- type: mrr_at_1000
value: 59.499
- type: mrr_at_3
value: 55.711999999999996
- type: mrr_at_5
value: 57.89
- type: ndcg_at_1
value: 44.728
- type: ndcg_at_10
value: 63.998999999999995
- type: ndcg_at_100
value: 67.077
- type: ndcg_at_1000
value: 67.40899999999999
- type: ndcg_at_3
value: 56.266000000000005
- type: ndcg_at_5
value: 60.88
- type: precision_at_1
value: 44.728
- type: precision_at_10
value: 10.09
- type: precision_at_100
value: 1.1809999999999998
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.145
- type: precision_at_5
value: 17.822
- type: recall_at_1
value: 39.75
- type: recall_at_10
value: 84.234
- type: recall_at_100
value: 97.055
- type: recall_at_1000
value: 99.517
- type: recall_at_3
value: 64.851
- type: recall_at_5
value: 75.343
task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: mteb/quora
metrics:
- type: map_at_1
value: 72.085
- type: map_at_10
value: 86.107
- type: map_at_100
value: 86.727
- type: map_at_1000
value: 86.74
- type: map_at_3
value: 83.21
- type: map_at_5
value: 85.06
- type: mrr_at_1
value: 82.94
- type: mrr_at_10
value: 88.845
- type: mrr_at_100
value: 88.926
- type: mrr_at_1000
value: 88.927
- type: mrr_at_3
value: 87.993
- type: mrr_at_5
value: 88.62299999999999
- type: ndcg_at_1
value: 82.97
- type: ndcg_at_10
value: 89.645
- type: ndcg_at_100
value: 90.717
- type: ndcg_at_1000
value: 90.78
- type: ndcg_at_3
value: 86.99900000000001
- type: ndcg_at_5
value: 88.52600000000001
- type: precision_at_1
value: 82.97
- type: precision_at_10
value: 13.569
- type: precision_at_100
value: 1.539
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.043
- type: precision_at_5
value: 24.992
- type: recall_at_1
value: 72.085
- type: recall_at_10
value: 96.262
- type: recall_at_100
value: 99.77000000000001
- type: recall_at_1000
value: 99.997
- type: recall_at_3
value: 88.652
- type: recall_at_5
value: 93.01899999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 55.82153952668092
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 62.094465801879295
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: mteb/scidocs
metrics:
- type: map_at_1
value: 5.688
- type: map_at_10
value: 15.201999999999998
- type: map_at_100
value: 18.096
- type: map_at_1000
value: 18.481
- type: map_at_3
value: 10.734
- type: map_at_5
value: 12.94
- type: mrr_at_1
value: 28.000000000000004
- type: mrr_at_10
value: 41.101
- type: mrr_at_100
value: 42.202
- type: mrr_at_1000
value: 42.228
- type: mrr_at_3
value: 37.683
- type: mrr_at_5
value: 39.708
- type: ndcg_at_1
value: 28.000000000000004
- type: ndcg_at_10
value: 24.976000000000003
- type: ndcg_at_100
value: 35.129
- type: ndcg_at_1000
value: 40.77
- type: ndcg_at_3
value: 23.787
- type: ndcg_at_5
value: 20.816000000000003
- type: precision_at_1
value: 28.000000000000004
- type: precision_at_10
value: 13.04
- type: precision_at_100
value: 2.761
- type: precision_at_1000
value: 0.41000000000000003
- type: precision_at_3
value: 22.6
- type: precision_at_5
value: 18.52
- type: recall_at_1
value: 5.688
- type: recall_at_10
value: 26.43
- type: recall_at_100
value: 56.02
- type: recall_at_1000
value: 83.21
- type: recall_at_3
value: 13.752
- type: recall_at_5
value: 18.777
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
metrics:
- type: cos_sim_pearson
value: 85.15084859283178
- type: cos_sim_spearman
value: 80.49030614009419
- type: euclidean_pearson
value: 81.84574978672468
- type: euclidean_spearman
value: 79.89787150656818
- type: manhattan_pearson
value: 81.63076538567131
- type: manhattan_spearman
value: 79.69867352121841
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cos_sim_pearson
value: 84.64097921490992
- type: cos_sim_spearman
value: 77.25370084896514
- type: euclidean_pearson
value: 82.71210826468788
- type: euclidean_spearman
value: 78.50445584994826
- type: manhattan_pearson
value: 82.92580164330298
- type: manhattan_spearman
value: 78.69686891301019
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cos_sim_pearson
value: 87.24596417308994
- type: cos_sim_spearman
value: 87.79454220555091
- type: euclidean_pearson
value: 87.40242561671164
- type: euclidean_spearman
value: 88.25955597373556
- type: manhattan_pearson
value: 87.25160240485849
- type: manhattan_spearman
value: 88.155794979818
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cos_sim_pearson
value: 84.44914233422564
- type: cos_sim_spearman
value: 82.91015471820322
- type: euclidean_pearson
value: 84.7206656630327
- type: euclidean_spearman
value: 83.86408872059216
- type: manhattan_pearson
value: 84.72816725158454
- type: manhattan_spearman
value: 84.01603388572788
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cos_sim_pearson
value: 87.6168026237477
- type: cos_sim_spearman
value: 88.45414278092397
- type: euclidean_pearson
value: 88.57023240882022
- type: euclidean_spearman
value: 89.04102190922094
- type: manhattan_pearson
value: 88.66695535796354
- type: manhattan_spearman
value: 89.19898476680969
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cos_sim_pearson
value: 84.27925826089424
- type: cos_sim_spearman
value: 85.45291099550461
- type: euclidean_pearson
value: 83.63853036580834
- type: euclidean_spearman
value: 84.33468035821484
- type: manhattan_pearson
value: 83.72778773251596
- type: manhattan_spearman
value: 84.51583132445376
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 89.67375185692552
- type: cos_sim_spearman
value: 90.32542469203855
- type: euclidean_pearson
value: 89.63513717951847
- type: euclidean_spearman
value: 89.87760271003745
- type: manhattan_pearson
value: 89.28381452982924
- type: manhattan_spearman
value: 89.53568197785721
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 66.24644693819846
- type: cos_sim_spearman
value: 66.09889420525377
- type: euclidean_pearson
value: 63.72551583520747
- type: euclidean_spearman
value: 63.01385470780679
- type: manhattan_pearson
value: 64.09258157214097
- type: manhattan_spearman
value: 63.080517752822594
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cos_sim_pearson
value: 86.27321463839989
- type: cos_sim_spearman
value: 86.37572865993327
- type: euclidean_pearson
value: 86.36268020198149
- type: euclidean_spearman
value: 86.31089339478922
- type: manhattan_pearson
value: 86.4260445761947
- type: manhattan_spearman
value: 86.45885895320457
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 86.52456702387798
- type: mrr
value: 96.34556529164372
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: map_at_1
value: 61.99400000000001
- type: map_at_10
value: 73.38799999999999
- type: map_at_100
value: 73.747
- type: map_at_1000
value: 73.75
- type: map_at_3
value: 70.04599999999999
- type: map_at_5
value: 72.095
- type: mrr_at_1
value: 65.0
- type: mrr_at_10
value: 74.42800000000001
- type: mrr_at_100
value: 74.722
- type: mrr_at_1000
value: 74.725
- type: mrr_at_3
value: 72.056
- type: mrr_at_5
value: 73.60600000000001
- type: ndcg_at_1
value: 65.0
- type: ndcg_at_10
value: 78.435
- type: ndcg_at_100
value: 79.922
- type: ndcg_at_1000
value: 80.00500000000001
- type: ndcg_at_3
value: 73.05199999999999
- type: ndcg_at_5
value: 75.98
- type: precision_at_1
value: 65.0
- type: precision_at_10
value: 10.5
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.555999999999997
- type: precision_at_5
value: 19.0
- type: recall_at_1
value: 61.99400000000001
- type: recall_at_10
value: 92.72200000000001
- type: recall_at_100
value: 99.333
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 78.739
- type: recall_at_5
value: 85.828
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_accuracy
value: 99.79009900990098
- type: cos_sim_ap
value: 95.3203137438653
- type: cos_sim_f1
value: 89.12386706948641
- type: cos_sim_precision
value: 89.75659229208925
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.67821782178218
- type: dot_ap
value: 89.94069840000675
- type: dot_f1
value: 83.45902463549521
- type: dot_precision
value: 83.9231547017189
- type: dot_recall
value: 83.0
- type: euclidean_accuracy
value: 99.78613861386138
- type: euclidean_ap
value: 95.10648259135526
- type: euclidean_f1
value: 88.77338877338877
- type: euclidean_precision
value: 92.42424242424242
- type: euclidean_recall
value: 85.39999999999999
- type: manhattan_accuracy
value: 99.7950495049505
- type: manhattan_ap
value: 95.29987661320946
- type: manhattan_f1
value: 89.21313183949972
- type: manhattan_precision
value: 93.14472252448314
- type: manhattan_recall
value: 85.6
- type: max_accuracy
value: 99.7950495049505
- type: max_ap
value: 95.3203137438653
- type: max_f1
value: 89.21313183949972
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 67.65446577183913
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 46.30749237193961
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 54.91481849959949
- type: mrr
value: 55.853506175197346
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cos_sim_pearson
value: 30.08196549170419
- type: cos_sim_spearman
value: 31.16661390597077
- type: dot_pearson
value: 29.892258410943466
- type: dot_spearman
value: 30.51328811965085
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: mteb/trec-covid
metrics:
- type: map_at_1
value: 0.23900000000000002
- type: map_at_10
value: 2.173
- type: map_at_100
value: 14.24
- type: map_at_1000
value: 35.309000000000005
- type: map_at_3
value: 0.7100000000000001
- type: map_at_5
value: 1.163
- type: mrr_at_1
value: 92.0
- type: mrr_at_10
value: 96.0
- type: mrr_at_100
value: 96.0
- type: mrr_at_1000
value: 96.0
- type: mrr_at_3
value: 96.0
- type: mrr_at_5
value: 96.0
- type: ndcg_at_1
value: 90.0
- type: ndcg_at_10
value: 85.382
- type: ndcg_at_100
value: 68.03
- type: ndcg_at_1000
value: 61.021
- type: ndcg_at_3
value: 89.765
- type: ndcg_at_5
value: 88.444
- type: precision_at_1
value: 92.0
- type: precision_at_10
value: 88.0
- type: precision_at_100
value: 70.02000000000001
- type: precision_at_1000
value: 26.984
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 92.80000000000001
- type: recall_at_1
value: 0.23900000000000002
- type: recall_at_10
value: 2.313
- type: recall_at_100
value: 17.049
- type: recall_at_1000
value: 57.489999999999995
- type: recall_at_3
value: 0.737
- type: recall_at_5
value: 1.221
task:
type: Retrieval
- dataset:
config: default
name: MTEB Touche2020
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
- type: map_at_1
value: 2.75
- type: map_at_10
value: 11.29
- type: map_at_100
value: 18.032999999999998
- type: map_at_1000
value: 19.746
- type: map_at_3
value: 6.555
- type: map_at_5
value: 8.706999999999999
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 50.55
- type: mrr_at_100
value: 51.659
- type: mrr_at_1000
value: 51.659
- type: mrr_at_3
value: 47.278999999999996
- type: mrr_at_5
value: 49.728
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 27.894000000000002
- type: ndcg_at_100
value: 39.769
- type: ndcg_at_1000
value: 51.495999999999995
- type: ndcg_at_3
value: 32.954
- type: ndcg_at_5
value: 31.502999999999997
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 23.265
- type: precision_at_100
value: 7.898
- type: precision_at_1000
value: 1.58
- type: precision_at_3
value: 34.694
- type: precision_at_5
value: 31.429000000000002
- type: recall_at_1
value: 2.75
- type: recall_at_10
value: 16.953
- type: recall_at_100
value: 48.68
- type: recall_at_1000
value: 85.18599999999999
- type: recall_at_3
value: 7.710999999999999
- type: recall_at_5
value: 11.484
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 82.66099999999999
- type: ap
value: 25.555698090238337
- type: f1
value: 66.48402012461622
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 72.94567062818335
- type: f1
value: 73.28139189595674
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 49.581627240203474
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_accuracy
value: 87.78089050485785
- type: cos_sim_ap
value: 79.64487116574168
- type: cos_sim_f1
value: 72.46563021970964
- type: cos_sim_precision
value: 70.62359128474831
- type: cos_sim_recall
value: 74.40633245382587
- type: dot_accuracy
value: 86.2609524944865
- type: dot_ap
value: 75.513046857613
- type: dot_f1
value: 68.58213616489695
- type: dot_precision
value: 65.12455516014235
- type: dot_recall
value: 72.42744063324538
- type: euclidean_accuracy
value: 87.6080348095607
- type: euclidean_ap
value: 79.00204933649795
- type: euclidean_f1
value: 72.14495342605589
- type: euclidean_precision
value: 69.85421299728193
- type: euclidean_recall
value: 74.5910290237467
- type: manhattan_accuracy
value: 87.59611372712642
- type: manhattan_ap
value: 78.78523756706264
- type: manhattan_f1
value: 71.86499137718648
- type: manhattan_precision
value: 67.39833641404806
- type: manhattan_recall
value: 76.96569920844327
- type: max_accuracy
value: 87.78089050485785
- type: max_ap
value: 79.64487116574168
- type: max_f1
value: 72.46563021970964
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cos_sim_accuracy
value: 89.98719292117825
- type: cos_sim_ap
value: 87.58146137353202
- type: cos_sim_f1
value: 80.28543232369239
- type: cos_sim_precision
value: 79.1735289714029
- type: cos_sim_recall
value: 81.42901139513397
- type: dot_accuracy
value: 88.9199363526992
- type: dot_ap
value: 84.98499998630417
- type: dot_f1
value: 78.21951400757969
- type: dot_precision
value: 75.58523624874336
- type: dot_recall
value: 81.04404065291038
- type: euclidean_accuracy
value: 89.77374160748244
- type: euclidean_ap
value: 87.35151562835209
- type: euclidean_f1
value: 79.92160922940393
- type: euclidean_precision
value: 76.88531587933979
- type: euclidean_recall
value: 83.20757622420696
- type: manhattan_accuracy
value: 89.72717041176699
- type: manhattan_ap
value: 87.34065592142515
- type: manhattan_f1
value: 79.85603419187943
- type: manhattan_precision
value: 77.82243332115455
- type: manhattan_recall
value: 81.99876809362489
- type: max_accuracy
value: 89.98719292117825
- type: max_ap
value: 87.58146137353202
- type: max_f1
value: 80.28543232369239
task:
type: PairClassification
- dataset:
config: default
name: MTEB AFQMC
revision: b44c3b011063adb25877c13823db83bb193913c4
split: validation
type: C-MTEB/AFQMC
metrics:
- type: cos_sim_pearson
value: 53.45954203592337
- type: cos_sim_spearman
value: 58.42154680418638
- type: euclidean_pearson
value: 56.41543791722753
- type: euclidean_spearman
value: 58.39328016640146
- type: manhattan_pearson
value: 56.318510356833876
- type: manhattan_spearman
value: 58.28423447818184
task:
type: STS
- dataset:
config: default
name: MTEB ATEC
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
split: test
type: C-MTEB/ATEC
metrics:
- type: cos_sim_pearson
value: 50.78356460675945
- type: cos_sim_spearman
value: 55.6530411663269
- type: euclidean_pearson
value: 56.50763660417816
- type: euclidean_spearman
value: 55.733823335669065
- type: manhattan_pearson
value: 56.45323093512866
- type: manhattan_spearman
value: 55.63248619032702
task:
type: STS
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 47.209999999999994
- type: f1
value: 46.08892432018655
task:
type: Classification
- dataset:
config: default
name: MTEB BQ
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
split: test
type: C-MTEB/BQ
metrics:
- type: cos_sim_pearson
value: 70.25573992001478
- type: cos_sim_spearman
value: 73.85247134951433
- type: euclidean_pearson
value: 72.60033082168442
- type: euclidean_spearman
value: 73.72445893756499
- type: manhattan_pearson
value: 72.59932284620231
- type: manhattan_spearman
value: 73.68002490614583
task:
type: STS
- dataset:
config: default
name: MTEB CLSClusteringP2P
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
split: test
type: C-MTEB/CLSClusteringP2P
metrics:
- type: v_measure
value: 45.21317724305628
task:
type: Clustering
- dataset:
config: default
name: MTEB CLSClusteringS2S
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
split: test
type: C-MTEB/CLSClusteringS2S
metrics:
- type: v_measure
value: 42.49825170976724
task:
type: Clustering
- dataset:
config: default
name: MTEB CMedQAv1
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
split: test
type: C-MTEB/CMedQAv1-reranking
metrics:
- type: map
value: 88.15661686810597
- type: mrr
value: 90.11222222222223
task:
type: Reranking
- dataset:
config: default
name: MTEB CMedQAv2
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
split: test
type: C-MTEB/CMedQAv2-reranking
metrics:
- type: map
value: 88.1204726064383
- type: mrr
value: 90.20142857142858
task:
type: Reranking
- dataset:
config: default
name: MTEB CmedqaRetrieval
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
split: dev
type: C-MTEB/CmedqaRetrieval
metrics:
- type: map_at_1
value: 27.224999999999998
- type: map_at_10
value: 40.169
- type: map_at_100
value: 42.0
- type: map_at_1000
value: 42.109
- type: map_at_3
value: 35.76
- type: map_at_5
value: 38.221
- type: mrr_at_1
value: 40.56
- type: mrr_at_10
value: 49.118
- type: mrr_at_100
value: 50.092999999999996
- type: mrr_at_1000
value: 50.133
- type: mrr_at_3
value: 46.507
- type: mrr_at_5
value: 47.973
- type: ndcg_at_1
value: 40.56
- type: ndcg_at_10
value: 46.972
- type: ndcg_at_100
value: 54.04
- type: ndcg_at_1000
value: 55.862
- type: ndcg_at_3
value: 41.36
- type: ndcg_at_5
value: 43.704
- type: precision_at_1
value: 40.56
- type: precision_at_10
value: 10.302999999999999
- type: precision_at_100
value: 1.606
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 23.064
- type: precision_at_5
value: 16.764000000000003
- type: recall_at_1
value: 27.224999999999998
- type: recall_at_10
value: 58.05200000000001
- type: recall_at_100
value: 87.092
- type: recall_at_1000
value: 99.099
- type: recall_at_3
value: 41.373
- type: recall_at_5
value: 48.453
task:
type: Retrieval
- dataset:
config: default
name: MTEB Cmnli
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
split: validation
type: C-MTEB/CMNLI
metrics:
- type: cos_sim_accuracy
value: 77.40228502705953
- type: cos_sim_ap
value: 86.22359172956327
- type: cos_sim_f1
value: 78.96328293736501
- type: cos_sim_precision
value: 73.36945615091311
- type: cos_sim_recall
value: 85.48047696983868
- type: dot_accuracy
value: 75.53818400481059
- type: dot_ap
value: 83.70164011305312
- type: dot_f1
value: 77.67298719348754
- type: dot_precision
value: 67.49482401656314
- type: dot_recall
value: 91.46598082768296
- type: euclidean_accuracy
value: 77.94347564642213
- type: euclidean_ap
value: 86.4652108728609
- type: euclidean_f1
value: 79.15555555555555
- type: euclidean_precision
value: 75.41816641964853
- type: euclidean_recall
value: 83.28267477203647
- type: manhattan_accuracy
value: 77.45039085989175
- type: manhattan_ap
value: 86.09986583900665
- type: manhattan_f1
value: 78.93669264438988
- type: manhattan_precision
value: 72.63261296660117
- type: manhattan_recall
value: 86.43909282207154
- type: max_accuracy
value: 77.94347564642213
- type: max_ap
value: 86.4652108728609
- type: max_f1
value: 79.15555555555555
task:
type: PairClassification
- dataset:
config: default
name: MTEB CovidRetrieval
revision: 1271c7809071a13532e05f25fb53511ffce77117
split: dev
type: C-MTEB/CovidRetrieval
metrics:
- type: map_at_1
value: 69.336
- type: map_at_10
value: 77.16
- type: map_at_100
value: 77.47500000000001
- type: map_at_1000
value: 77.482
- type: map_at_3
value: 75.42999999999999
- type: map_at_5
value: 76.468
- type: mrr_at_1
value: 69.44200000000001
- type: mrr_at_10
value: 77.132
- type: mrr_at_100
value: 77.43299999999999
- type: mrr_at_1000
value: 77.44
- type: mrr_at_3
value: 75.395
- type: mrr_at_5
value: 76.459
- type: ndcg_at_1
value: 69.547
- type: ndcg_at_10
value: 80.794
- type: ndcg_at_100
value: 82.245
- type: ndcg_at_1000
value: 82.40899999999999
- type: ndcg_at_3
value: 77.303
- type: ndcg_at_5
value: 79.168
- type: precision_at_1
value: 69.547
- type: precision_at_10
value: 9.305
- type: precision_at_100
value: 0.9979999999999999
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 27.749000000000002
- type: precision_at_5
value: 17.576
- type: recall_at_1
value: 69.336
- type: recall_at_10
value: 92.097
- type: recall_at_100
value: 98.736
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 82.64
- type: recall_at_5
value: 87.144
task:
type: Retrieval
- dataset:
config: default
name: MTEB DuRetrieval
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
split: dev
type: C-MTEB/DuRetrieval
metrics:
- type: map_at_1
value: 26.817999999999998
- type: map_at_10
value: 82.67
- type: map_at_100
value: 85.304
- type: map_at_1000
value: 85.334
- type: map_at_3
value: 57.336
- type: map_at_5
value: 72.474
- type: mrr_at_1
value: 91.45
- type: mrr_at_10
value: 94.272
- type: mrr_at_100
value: 94.318
- type: mrr_at_1000
value: 94.32000000000001
- type: mrr_at_3
value: 94.0
- type: mrr_at_5
value: 94.17699999999999
- type: ndcg_at_1
value: 91.45
- type: ndcg_at_10
value: 89.404
- type: ndcg_at_100
value: 91.724
- type: ndcg_at_1000
value: 91.973
- type: ndcg_at_3
value: 88.104
- type: ndcg_at_5
value: 87.25699999999999
- type: precision_at_1
value: 91.45
- type: precision_at_10
value: 42.585
- type: precision_at_100
value: 4.838
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 78.8
- type: precision_at_5
value: 66.66
- type: recall_at_1
value: 26.817999999999998
- type: recall_at_10
value: 90.67
- type: recall_at_100
value: 98.36200000000001
- type: recall_at_1000
value: 99.583
- type: recall_at_3
value: 59.614999999999995
- type: recall_at_5
value: 77.05199999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB EcomRetrieval
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
split: dev
type: C-MTEB/EcomRetrieval
metrics:
- type: map_at_1
value: 47.699999999999996
- type: map_at_10
value: 57.589999999999996
- type: map_at_100
value: 58.226
- type: map_at_1000
value: 58.251
- type: map_at_3
value: 55.233
- type: map_at_5
value: 56.633
- type: mrr_at_1
value: 47.699999999999996
- type: mrr_at_10
value: 57.589999999999996
- type: mrr_at_100
value: 58.226
- type: mrr_at_1000
value: 58.251
- type: mrr_at_3
value: 55.233
- type: mrr_at_5
value: 56.633
- type: ndcg_at_1
value: 47.699999999999996
- type: ndcg_at_10
value: 62.505
- type: ndcg_at_100
value: 65.517
- type: ndcg_at_1000
value: 66.19800000000001
- type: ndcg_at_3
value: 57.643
- type: ndcg_at_5
value: 60.181
- type: precision_at_1
value: 47.699999999999996
- type: precision_at_10
value: 7.8
- type: precision_at_100
value: 0.919
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 21.532999999999998
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 47.699999999999996
- type: recall_at_10
value: 78.0
- type: recall_at_100
value: 91.9
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 64.60000000000001
- type: recall_at_5
value: 70.8
task:
type: Retrieval
- dataset:
config: default
name: MTEB IFlyTek
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
split: validation
type: C-MTEB/IFlyTek-classification
metrics:
- type: accuracy
value: 44.84801846864178
- type: f1
value: 37.47347897956339
task:
type: Classification
- dataset:
config: default
name: MTEB JDReview
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
split: test
type: C-MTEB/JDReview-classification
metrics:
- type: accuracy
value: 85.81613508442777
- type: ap
value: 52.68244615477374
- type: f1
value: 80.0445640948843
task:
type: Classification
- dataset:
config: default
name: MTEB LCQMC
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
split: test
type: C-MTEB/LCQMC
metrics:
- type: cos_sim_pearson
value: 69.57786502217138
- type: cos_sim_spearman
value: 75.39106054489906
- type: euclidean_pearson
value: 73.72082954602402
- type: euclidean_spearman
value: 75.14421475913619
- type: manhattan_pearson
value: 73.62463076633642
- type: manhattan_spearman
value: 75.01301565104112
task:
type: STS
- dataset:
config: default
name: MTEB MMarcoReranking
revision: None
split: dev
type: C-MTEB/Mmarco-reranking
metrics:
- type: map
value: 29.143797057999134
- type: mrr
value: 28.08174603174603
task:
type: Reranking
- dataset:
config: default
name: MTEB MMarcoRetrieval
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
split: dev
type: C-MTEB/MMarcoRetrieval
metrics:
- type: map_at_1
value: 70.492
- type: map_at_10
value: 79.501
- type: map_at_100
value: 79.728
- type: map_at_1000
value: 79.735
- type: map_at_3
value: 77.77
- type: map_at_5
value: 78.851
- type: mrr_at_1
value: 72.822
- type: mrr_at_10
value: 80.001
- type: mrr_at_100
value: 80.19
- type: mrr_at_1000
value: 80.197
- type: mrr_at_3
value: 78.484
- type: mrr_at_5
value: 79.42099999999999
- type: ndcg_at_1
value: 72.822
- type: ndcg_at_10
value: 83.013
- type: ndcg_at_100
value: 84.013
- type: ndcg_at_1000
value: 84.20400000000001
- type: ndcg_at_3
value: 79.728
- type: ndcg_at_5
value: 81.542
- type: precision_at_1
value: 72.822
- type: precision_at_10
value: 9.917
- type: precision_at_100
value: 1.042
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 29.847
- type: precision_at_5
value: 18.871
- type: recall_at_1
value: 70.492
- type: recall_at_10
value: 93.325
- type: recall_at_100
value: 97.822
- type: recall_at_1000
value: 99.319
- type: recall_at_3
value: 84.636
- type: recall_at_5
value: 88.93100000000001
task:
type: Retrieval
- dataset:
config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 76.88298587760592
- type: f1
value: 73.89001762017176
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 80.76328177538669
- type: f1
value: 80.24718532423358
task:
type: Classification
- dataset:
config: default
name: MTEB MedicalRetrieval
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
split: dev
type: C-MTEB/MedicalRetrieval
metrics:
- type: map_at_1
value: 49.6
- type: map_at_10
value: 55.620999999999995
- type: map_at_100
value: 56.204
- type: map_at_1000
value: 56.251
- type: map_at_3
value: 54.132999999999996
- type: map_at_5
value: 54.933
- type: mrr_at_1
value: 49.7
- type: mrr_at_10
value: 55.67100000000001
- type: mrr_at_100
value: 56.254000000000005
- type: mrr_at_1000
value: 56.301
- type: mrr_at_3
value: 54.18300000000001
- type: mrr_at_5
value: 54.983000000000004
- type: ndcg_at_1
value: 49.6
- type: ndcg_at_10
value: 58.645
- type: ndcg_at_100
value: 61.789
- type: ndcg_at_1000
value: 63.219
- type: ndcg_at_3
value: 55.567
- type: ndcg_at_5
value: 57.008
- type: precision_at_1
value: 49.6
- type: precision_at_10
value: 6.819999999999999
- type: precision_at_100
value: 0.836
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 19.900000000000002
- type: precision_at_5
value: 12.64
- type: recall_at_1
value: 49.6
- type: recall_at_10
value: 68.2
- type: recall_at_100
value: 83.6
- type: recall_at_1000
value: 95.3
- type: recall_at_3
value: 59.699999999999996
- type: recall_at_5
value: 63.2
task:
type: Retrieval
- dataset:
config: default
name: MTEB MultilingualSentiment
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
split: validation
type: C-MTEB/MultilingualSentiment-classification
metrics:
- type: accuracy
value: 74.45666666666666
- type: f1
value: 74.32582402190089
task:
type: Classification
- dataset:
config: default
name: MTEB Ocnli
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
split: validation
type: C-MTEB/OCNLI
metrics:
- type: cos_sim_accuracy
value: 80.67135896047645
- type: cos_sim_ap
value: 87.60421240712051
- type: cos_sim_f1
value: 82.1304131408661
- type: cos_sim_precision
value: 77.68361581920904
- type: cos_sim_recall
value: 87.11721224920802
- type: dot_accuracy
value: 79.04710341093666
- type: dot_ap
value: 85.6370059719336
- type: dot_f1
value: 80.763723150358
- type: dot_precision
value: 73.69337979094077
- type: dot_recall
value: 89.33474128827878
- type: euclidean_accuracy
value: 81.05035192203573
- type: euclidean_ap
value: 87.7880240053663
- type: euclidean_f1
value: 82.50244379276637
- type: euclidean_precision
value: 76.7970882620564
- type: euclidean_recall
value: 89.1235480464625
- type: manhattan_accuracy
value: 80.61721710882512
- type: manhattan_ap
value: 87.43568120591175
- type: manhattan_f1
value: 81.89526184538653
- type: manhattan_precision
value: 77.5992438563327
- type: manhattan_recall
value: 86.6948257655755
- type: max_accuracy
value: 81.05035192203573
- type: max_ap
value: 87.7880240053663
- type: max_f1
value: 82.50244379276637
task:
type: PairClassification
- dataset:
config: default
name: MTEB OnlineShopping
revision: e610f2ebd179a8fda30ae534c3878750a96db120
split: test
type: C-MTEB/OnlineShopping-classification
metrics:
- type: accuracy
value: 93.5
- type: ap
value: 91.31357903446782
- type: f1
value: 93.48088994006616
task:
type: Classification
- dataset:
config: default
name: MTEB PAWSX
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
split: test
type: C-MTEB/PAWSX
metrics:
- type: cos_sim_pearson
value: 36.93293453538077
- type: cos_sim_spearman
value: 42.45972506308574
- type: euclidean_pearson
value: 42.34945133152159
- type: euclidean_spearman
value: 42.331610303674644
- type: manhattan_pearson
value: 42.31455070249498
- type: manhattan_spearman
value: 42.19887982891834
task:
type: STS
- dataset:
config: default
name: MTEB QBQTC
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
split: test
type: C-MTEB/QBQTC
metrics:
- type: cos_sim_pearson
value: 33.683290790043785
- type: cos_sim_spearman
value: 35.149171171202994
- type: euclidean_pearson
value: 32.33806561267862
- type: euclidean_spearman
value: 34.483576387347966
- type: manhattan_pearson
value: 32.47629754599608
- type: manhattan_spearman
value: 34.66434471867615
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 66.46322760516104
- type: cos_sim_spearman
value: 67.398478319726
- type: euclidean_pearson
value: 64.7223480293625
- type: euclidean_spearman
value: 66.83118568812951
- type: manhattan_pearson
value: 64.88440039828305
- type: manhattan_spearman
value: 66.80429458952257
task:
type: STS
- dataset:
config: default
name: MTEB STSB
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
split: test
type: C-MTEB/STSB
metrics:
- type: cos_sim_pearson
value: 79.08991383232105
- type: cos_sim_spearman
value: 79.39715677296854
- type: euclidean_pearson
value: 78.63201279320496
- type: euclidean_spearman
value: 79.40262660785731
- type: manhattan_pearson
value: 78.98138363146906
- type: manhattan_spearman
value: 79.79968413014194
task:
type: STS
- dataset:
config: default
name: MTEB T2Reranking
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
split: dev
type: C-MTEB/T2Reranking
metrics:
- type: map
value: 67.43289278789972
- type: mrr
value: 77.53012460908535
task:
type: Reranking
- dataset:
config: default
name: MTEB T2Retrieval
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
split: dev
type: C-MTEB/T2Retrieval
metrics:
- type: map_at_1
value: 27.733999999999998
- type: map_at_10
value: 78.24799999999999
- type: map_at_100
value: 81.765
- type: map_at_1000
value: 81.824
- type: map_at_3
value: 54.92
- type: map_at_5
value: 67.61399999999999
- type: mrr_at_1
value: 90.527
- type: mrr_at_10
value: 92.843
- type: mrr_at_100
value: 92.927
- type: mrr_at_1000
value: 92.93
- type: mrr_at_3
value: 92.45100000000001
- type: mrr_at_5
value: 92.693
- type: ndcg_at_1
value: 90.527
- type: ndcg_at_10
value: 85.466
- type: ndcg_at_100
value: 88.846
- type: ndcg_at_1000
value: 89.415
- type: ndcg_at_3
value: 86.768
- type: ndcg_at_5
value: 85.46000000000001
- type: precision_at_1
value: 90.527
- type: precision_at_10
value: 42.488
- type: precision_at_100
value: 5.024
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 75.907
- type: precision_at_5
value: 63.727000000000004
- type: recall_at_1
value: 27.733999999999998
- type: recall_at_10
value: 84.346
- type: recall_at_100
value: 95.536
- type: recall_at_1000
value: 98.42999999999999
- type: recall_at_3
value: 56.455
- type: recall_at_5
value: 70.755
task:
type: Retrieval
- dataset:
config: default
name: MTEB TNews
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
split: validation
type: C-MTEB/TNews-classification
metrics:
- type: accuracy
value: 49.952000000000005
- type: f1
value: 48.264617195258054
task:
type: Classification
- dataset:
config: default
name: MTEB ThuNewsClusteringP2P
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
split: test
type: C-MTEB/ThuNewsClusteringP2P
metrics:
- type: v_measure
value: 68.23769904483508
task:
type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringS2S
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
split: test
type: C-MTEB/ThuNewsClusteringS2S
metrics:
- type: v_measure
value: 62.50294403136556
task:
type: Clustering
- dataset:
config: default
name: MTEB VideoRetrieval
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
split: dev
type: C-MTEB/VideoRetrieval
metrics:
- type: map_at_1
value: 54.0
- type: map_at_10
value: 63.668
- type: map_at_100
value: 64.217
- type: map_at_1000
value: 64.23100000000001
- type: map_at_3
value: 61.7
- type: map_at_5
value: 62.870000000000005
- type: mrr_at_1
value: 54.0
- type: mrr_at_10
value: 63.668
- type: mrr_at_100
value: 64.217
- type: mrr_at_1000
value: 64.23100000000001
- type: mrr_at_3
value: 61.7
- type: mrr_at_5
value: 62.870000000000005
- type: ndcg_at_1
value: 54.0
- type: ndcg_at_10
value: 68.11399999999999
- type: ndcg_at_100
value: 70.723
- type: ndcg_at_1000
value: 71.123
- type: ndcg_at_3
value: 64.074
- type: ndcg_at_5
value: 66.178
- type: precision_at_1
value: 54.0
- type: precision_at_10
value: 8.200000000000001
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 23.633000000000003
- type: precision_at_5
value: 15.2
- type: recall_at_1
value: 54.0
- type: recall_at_10
value: 82.0
- type: recall_at_100
value: 94.1
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 70.89999999999999
- type: recall_at_5
value: 76.0
task:
type: Retrieval
- dataset:
config: default
name: MTEB Waimai
revision: 339287def212450dcaa9df8c22bf93e9980c7023
split: test
type: C-MTEB/waimai-classification
metrics:
- type: accuracy
value: 86.63000000000001
- type: ap
value: 69.99457882599567
- type: f1
value: 85.07735617998541
task:
type: Classification
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 44.594104491193555
task:
type: Clustering
- dataset:
config: default
name: MTEB AllegroReviews
revision: None
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 63.97614314115309
- type: f1
value: 52.15634261679283
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna-PL
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
split: test
type: clarin-knext/arguana-pl
metrics:
- type: map_at_1
value: 32.646
- type: map_at_10
value: 47.963
- type: map_at_100
value: 48.789
- type: map_at_1000
value: 48.797000000000004
- type: map_at_3
value: 43.196
- type: map_at_5
value: 46.016
- type: mrr_at_1
value: 33.073
- type: mrr_at_10
value: 48.126000000000005
- type: mrr_at_100
value: 48.946
- type: mrr_at_1000
value: 48.953
- type: mrr_at_3
value: 43.374
- type: mrr_at_5
value: 46.147
- type: ndcg_at_1
value: 32.646
- type: ndcg_at_10
value: 56.481
- type: ndcg_at_100
value: 59.922
- type: ndcg_at_1000
value: 60.07
- type: ndcg_at_3
value: 46.675
- type: ndcg_at_5
value: 51.76500000000001
- type: precision_at_1
value: 32.646
- type: precision_at_10
value: 8.371
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.919
- type: precision_at_5
value: 13.825999999999999
- type: recall_at_1
value: 32.646
- type: recall_at_10
value: 83.71300000000001
- type: recall_at_100
value: 98.578
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 56.757000000000005
- type: recall_at_5
value: 69.132
task:
type: Retrieval
- dataset:
config: default
name: MTEB CBD
revision: None
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 68.56
- type: ap
value: 23.310493680488513
- type: f1
value: 58.85369533105693
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: None
split: test
type: PL-MTEB/cdsce-pairclassification
metrics:
- type: cos_sim_accuracy
value: 88.5
- type: cos_sim_ap
value: 72.42140924378361
- type: cos_sim_f1
value: 66.0919540229885
- type: cos_sim_precision
value: 72.78481012658227
- type: cos_sim_recall
value: 60.526315789473685
- type: dot_accuracy
value: 88.5
- type: dot_ap
value: 72.42140924378361
- type: dot_f1
value: 66.0919540229885
- type: dot_precision
value: 72.78481012658227
- type: dot_recall
value: 60.526315789473685
- type: euclidean_accuracy
value: 88.5
- type: euclidean_ap
value: 72.42140924378361
- type: euclidean_f1
value: 66.0919540229885
- type: euclidean_precision
value: 72.78481012658227
- type: euclidean_recall
value: 60.526315789473685
- type: manhattan_accuracy
value: 88.5
- type: manhattan_ap
value: 72.49745515311696
- type: manhattan_f1
value: 66.0968660968661
- type: manhattan_precision
value: 72.04968944099379
- type: manhattan_recall
value: 61.05263157894737
- type: max_accuracy
value: 88.5
- type: max_ap
value: 72.49745515311696
- type: max_f1
value: 66.0968660968661
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: None
split: test
type: PL-MTEB/cdscr-sts
metrics:
- type: cos_sim_pearson
value: 90.32269765590145
- type: cos_sim_spearman
value: 89.73666311491672
- type: euclidean_pearson
value: 88.2933868516544
- type: euclidean_spearman
value: 89.73666311491672
- type: manhattan_pearson
value: 88.33474590219448
- type: manhattan_spearman
value: 89.8548364866583
task:
type: STS
- dataset:
config: default
name: MTEB DBPedia-PL
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
split: test
type: clarin-knext/dbpedia-pl
metrics:
- type: map_at_1
value: 7.632999999999999
- type: map_at_10
value: 16.426
- type: map_at_100
value: 22.651
- type: map_at_1000
value: 24.372
- type: map_at_3
value: 11.706
- type: map_at_5
value: 13.529
- type: mrr_at_1
value: 60.75000000000001
- type: mrr_at_10
value: 68.613
- type: mrr_at_100
value: 69.001
- type: mrr_at_1000
value: 69.021
- type: mrr_at_3
value: 67.0
- type: mrr_at_5
value: 67.925
- type: ndcg_at_1
value: 49.875
- type: ndcg_at_10
value: 36.978
- type: ndcg_at_100
value: 40.031
- type: ndcg_at_1000
value: 47.566
- type: ndcg_at_3
value: 41.148
- type: ndcg_at_5
value: 38.702
- type: precision_at_1
value: 60.75000000000001
- type: precision_at_10
value: 29.7
- type: precision_at_100
value: 9.278
- type: precision_at_1000
value: 2.099
- type: precision_at_3
value: 44.0
- type: precision_at_5
value: 37.6
- type: recall_at_1
value: 7.632999999999999
- type: recall_at_10
value: 22.040000000000003
- type: recall_at_100
value: 44.024
- type: recall_at_1000
value: 67.848
- type: recall_at_3
value: 13.093
- type: recall_at_5
value: 15.973
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA-PL
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
split: test
type: clarin-knext/fiqa-pl
metrics:
- type: map_at_1
value: 15.473
- type: map_at_10
value: 24.579
- type: map_at_100
value: 26.387
- type: map_at_1000
value: 26.57
- type: map_at_3
value: 21.278
- type: map_at_5
value: 23.179
- type: mrr_at_1
value: 30.709999999999997
- type: mrr_at_10
value: 38.994
- type: mrr_at_100
value: 39.993
- type: mrr_at_1000
value: 40.044999999999995
- type: mrr_at_3
value: 36.342999999999996
- type: mrr_at_5
value: 37.846999999999994
- type: ndcg_at_1
value: 30.709999999999997
- type: ndcg_at_10
value: 31.608999999999998
- type: ndcg_at_100
value: 38.807
- type: ndcg_at_1000
value: 42.208
- type: ndcg_at_3
value: 28.086
- type: ndcg_at_5
value: 29.323
- type: precision_at_1
value: 30.709999999999997
- type: precision_at_10
value: 8.688
- type: precision_at_100
value: 1.608
- type: precision_at_1000
value: 0.22100000000000003
- type: precision_at_3
value: 18.724
- type: precision_at_5
value: 13.950999999999999
- type: recall_at_1
value: 15.473
- type: recall_at_10
value: 38.361000000000004
- type: recall_at_100
value: 65.2
- type: recall_at_1000
value: 85.789
- type: recall_at_3
value: 25.401
- type: recall_at_5
value: 30.875999999999998
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA-PL
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
split: test
type: clarin-knext/hotpotqa-pl
metrics:
- type: map_at_1
value: 38.096000000000004
- type: map_at_10
value: 51.44499999999999
- type: map_at_100
value: 52.325
- type: map_at_1000
value: 52.397000000000006
- type: map_at_3
value: 48.626999999999995
- type: map_at_5
value: 50.342
- type: mrr_at_1
value: 76.19200000000001
- type: mrr_at_10
value: 81.191
- type: mrr_at_100
value: 81.431
- type: mrr_at_1000
value: 81.443
- type: mrr_at_3
value: 80.30199999999999
- type: mrr_at_5
value: 80.85900000000001
- type: ndcg_at_1
value: 76.19200000000001
- type: ndcg_at_10
value: 60.9
- type: ndcg_at_100
value: 64.14699999999999
- type: ndcg_at_1000
value: 65.647
- type: ndcg_at_3
value: 56.818000000000005
- type: ndcg_at_5
value: 59.019999999999996
- type: precision_at_1
value: 76.19200000000001
- type: precision_at_10
value: 12.203
- type: precision_at_100
value: 1.478
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 34.616
- type: precision_at_5
value: 22.515
- type: recall_at_1
value: 38.096000000000004
- type: recall_at_10
value: 61.013
- type: recall_at_100
value: 73.90299999999999
- type: recall_at_1000
value: 83.91
- type: recall_at_3
value: 51.92400000000001
- type: recall_at_5
value: 56.286
task:
type: Retrieval
- dataset:
config: default
name: MTEB MSMARCO-PL
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
split: test
type: clarin-knext/msmarco-pl
metrics:
- type: map_at_1
value: 1.548
- type: map_at_10
value: 11.049000000000001
- type: map_at_100
value: 28.874
- type: map_at_1000
value: 34.931
- type: map_at_3
value: 4.162
- type: map_at_5
value: 6.396
- type: mrr_at_1
value: 90.69800000000001
- type: mrr_at_10
value: 92.093
- type: mrr_at_100
value: 92.345
- type: mrr_at_1000
value: 92.345
- type: mrr_at_3
value: 91.86
- type: mrr_at_5
value: 91.86
- type: ndcg_at_1
value: 74.031
- type: ndcg_at_10
value: 63.978
- type: ndcg_at_100
value: 53.101
- type: ndcg_at_1000
value: 60.675999999999995
- type: ndcg_at_3
value: 71.421
- type: ndcg_at_5
value: 68.098
- type: precision_at_1
value: 90.69800000000001
- type: precision_at_10
value: 71.86
- type: precision_at_100
value: 31.395
- type: precision_at_1000
value: 5.981
- type: precision_at_3
value: 84.49600000000001
- type: precision_at_5
value: 79.07
- type: recall_at_1
value: 1.548
- type: recall_at_10
value: 12.149000000000001
- type: recall_at_100
value: 40.794999999999995
- type: recall_at_1000
value: 67.974
- type: recall_at_3
value: 4.244
- type: recall_at_5
value: 6.608
task:
type: Retrieval
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 73.55413584398119
- type: f1
value: 69.65610882318181
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 76.37188971082716
- type: f1
value: 75.64847309941361
task:
type: Classification
- dataset:
config: default
name: MTEB NFCorpus-PL
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
split: test
type: clarin-knext/nfcorpus-pl
metrics:
- type: map_at_1
value: 4.919
- type: map_at_10
value: 10.834000000000001
- type: map_at_100
value: 13.38
- type: map_at_1000
value: 14.581
- type: map_at_3
value: 8.198
- type: map_at_5
value: 9.428
- type: mrr_at_1
value: 41.176
- type: mrr_at_10
value: 50.083
- type: mrr_at_100
value: 50.559
- type: mrr_at_1000
value: 50.604000000000006
- type: mrr_at_3
value: 47.936
- type: mrr_at_5
value: 49.407000000000004
- type: ndcg_at_1
value: 39.628
- type: ndcg_at_10
value: 30.098000000000003
- type: ndcg_at_100
value: 27.061
- type: ndcg_at_1000
value: 35.94
- type: ndcg_at_3
value: 35.135
- type: ndcg_at_5
value: 33.335
- type: precision_at_1
value: 41.176
- type: precision_at_10
value: 22.259999999999998
- type: precision_at_100
value: 6.712
- type: precision_at_1000
value: 1.9060000000000001
- type: precision_at_3
value: 33.23
- type: precision_at_5
value: 29.04
- type: recall_at_1
value: 4.919
- type: recall_at_10
value: 14.196
- type: recall_at_100
value: 26.948
- type: recall_at_1000
value: 59.211000000000006
- type: recall_at_3
value: 9.44
- type: recall_at_5
value: 11.569
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ-PL
revision: f171245712cf85dd4700b06bef18001578d0ca8d
split: test
type: clarin-knext/nq-pl
metrics:
- type: map_at_1
value: 25.35
- type: map_at_10
value: 37.884
- type: map_at_100
value: 38.955
- type: map_at_1000
value: 39.007999999999996
- type: map_at_3
value: 34.239999999999995
- type: map_at_5
value: 36.398
- type: mrr_at_1
value: 28.737000000000002
- type: mrr_at_10
value: 39.973
- type: mrr_at_100
value: 40.844
- type: mrr_at_1000
value: 40.885
- type: mrr_at_3
value: 36.901
- type: mrr_at_5
value: 38.721
- type: ndcg_at_1
value: 28.708
- type: ndcg_at_10
value: 44.204
- type: ndcg_at_100
value: 48.978
- type: ndcg_at_1000
value: 50.33
- type: ndcg_at_3
value: 37.36
- type: ndcg_at_5
value: 40.912
- type: precision_at_1
value: 28.708
- type: precision_at_10
value: 7.367
- type: precision_at_100
value: 1.0030000000000001
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 17.034
- type: precision_at_5
value: 12.293999999999999
- type: recall_at_1
value: 25.35
- type: recall_at_10
value: 61.411
- type: recall_at_100
value: 82.599
- type: recall_at_1000
value: 92.903
- type: recall_at_3
value: 43.728
- type: recall_at_5
value: 51.854
task:
type: Retrieval
- dataset:
config: default
name: MTEB PAC
revision: None
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 69.04141326382856
- type: ap
value: 77.49422763833996
- type: f1
value: 66.73472657783407
task:
type: Classification
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 81.0
- type: cos_sim_ap
value: 91.47194213011349
- type: cos_sim_f1
value: 84.73767885532592
- type: cos_sim_precision
value: 81.49847094801224
- type: cos_sim_recall
value: 88.24503311258279
- type: dot_accuracy
value: 81.0
- type: dot_ap
value: 91.47194213011349
- type: dot_f1
value: 84.73767885532592
- type: dot_precision
value: 81.49847094801224
- type: dot_recall
value: 88.24503311258279
- type: euclidean_accuracy
value: 81.0
- type: euclidean_ap
value: 91.47194213011349
- type: euclidean_f1
value: 84.73767885532592
- type: euclidean_precision
value: 81.49847094801224
- type: euclidean_recall
value: 88.24503311258279
- type: manhattan_accuracy
value: 81.0
- type: manhattan_ap
value: 91.46464475050571
- type: manhattan_f1
value: 84.48687350835321
- type: manhattan_precision
value: 81.31699846860643
- type: manhattan_recall
value: 87.91390728476821
- type: max_accuracy
value: 81.0
- type: max_ap
value: 91.47194213011349
- type: max_f1
value: 84.73767885532592
task:
type: PairClassification
- dataset:
config: default
name: MTEB PSC
revision: None
split: test
type: PL-MTEB/psc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 97.6808905380334
- type: cos_sim_ap
value: 99.27948611836348
- type: cos_sim_f1
value: 96.15975422427034
- type: cos_sim_precision
value: 96.90402476780186
- type: cos_sim_recall
value: 95.42682926829268
- type: dot_accuracy
value: 97.6808905380334
- type: dot_ap
value: 99.2794861183635
- type: dot_f1
value: 96.15975422427034
- type: dot_precision
value: 96.90402476780186
- type: dot_recall
value: 95.42682926829268
- type: euclidean_accuracy
value: 97.6808905380334
- type: euclidean_ap
value: 99.2794861183635
- type: euclidean_f1
value: 96.15975422427034
- type: euclidean_precision
value: 96.90402476780186
- type: euclidean_recall
value: 95.42682926829268
- type: manhattan_accuracy
value: 97.6808905380334
- type: manhattan_ap
value: 99.28715055268721
- type: manhattan_f1
value: 96.14791987673343
- type: manhattan_precision
value: 97.19626168224299
- type: manhattan_recall
value: 95.1219512195122
- type: max_accuracy
value: 97.6808905380334
- type: max_ap
value: 99.28715055268721
- type: max_f1
value: 96.15975422427034
task:
type: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: None
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 86.16343490304708
- type: f1
value: 83.3442579486744
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: None
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 68.40080971659918
- type: f1
value: 53.13720751142237
task:
type: Classification
- dataset:
config: default
name: MTEB Quora-PL
revision: 0be27e93455051e531182b85e85e425aba12e9d4
split: test
type: clarin-knext/quora-pl
metrics:
- type: map_at_1
value: 63.322
- type: map_at_10
value: 76.847
- type: map_at_100
value: 77.616
- type: map_at_1000
value: 77.644
- type: map_at_3
value: 73.624
- type: map_at_5
value: 75.603
- type: mrr_at_1
value: 72.88
- type: mrr_at_10
value: 80.376
- type: mrr_at_100
value: 80.604
- type: mrr_at_1000
value: 80.61
- type: mrr_at_3
value: 78.92
- type: mrr_at_5
value: 79.869
- type: ndcg_at_1
value: 72.89999999999999
- type: ndcg_at_10
value: 81.43
- type: ndcg_at_100
value: 83.394
- type: ndcg_at_1000
value: 83.685
- type: ndcg_at_3
value: 77.62599999999999
- type: ndcg_at_5
value: 79.656
- type: precision_at_1
value: 72.89999999999999
- type: precision_at_10
value: 12.548
- type: precision_at_100
value: 1.4869999999999999
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 34.027
- type: precision_at_5
value: 22.654
- type: recall_at_1
value: 63.322
- type: recall_at_10
value: 90.664
- type: recall_at_100
value: 97.974
- type: recall_at_1000
value: 99.636
- type: recall_at_3
value: 80.067
- type: recall_at_5
value: 85.526
task:
type: Retrieval
- dataset:
config: default
name: MTEB SCIDOCS-PL
revision: 45452b03f05560207ef19149545f168e596c9337
split: test
type: clarin-knext/scidocs-pl
metrics:
- type: map_at_1
value: 3.95
- type: map_at_10
value: 9.658999999999999
- type: map_at_100
value: 11.384
- type: map_at_1000
value: 11.677
- type: map_at_3
value: 7.055
- type: map_at_5
value: 8.244
- type: mrr_at_1
value: 19.5
- type: mrr_at_10
value: 28.777
- type: mrr_at_100
value: 29.936
- type: mrr_at_1000
value: 30.009999999999998
- type: mrr_at_3
value: 25.55
- type: mrr_at_5
value: 27.284999999999997
- type: ndcg_at_1
value: 19.5
- type: ndcg_at_10
value: 16.589000000000002
- type: ndcg_at_100
value: 23.879
- type: ndcg_at_1000
value: 29.279
- type: ndcg_at_3
value: 15.719
- type: ndcg_at_5
value: 13.572000000000001
- type: precision_at_1
value: 19.5
- type: precision_at_10
value: 8.62
- type: precision_at_100
value: 1.924
- type: precision_at_1000
value: 0.322
- type: precision_at_3
value: 14.6
- type: precision_at_5
value: 11.78
- type: recall_at_1
value: 3.95
- type: recall_at_10
value: 17.477999999999998
- type: recall_at_100
value: 38.99
- type: recall_at_1000
value: 65.417
- type: recall_at_3
value: 8.883000000000001
- type: recall_at_5
value: 11.933
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-E-PL
revision: None
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics:
- type: cos_sim_accuracy
value: 83.48960456583775
- type: cos_sim_ap
value: 76.31522115825375
- type: cos_sim_f1
value: 70.35573122529645
- type: cos_sim_precision
value: 70.9934735315446
- type: cos_sim_recall
value: 69.72934472934473
- type: dot_accuracy
value: 83.48960456583775
- type: dot_ap
value: 76.31522115825373
- type: dot_f1
value: 70.35573122529645
- type: dot_precision
value: 70.9934735315446
- type: dot_recall
value: 69.72934472934473
- type: euclidean_accuracy
value: 83.48960456583775
- type: euclidean_ap
value: 76.31522115825373
- type: euclidean_f1
value: 70.35573122529645
- type: euclidean_precision
value: 70.9934735315446
- type: euclidean_recall
value: 69.72934472934473
- type: manhattan_accuracy
value: 83.46922136159804
- type: manhattan_ap
value: 76.18474601388084
- type: manhattan_f1
value: 70.34779490856937
- type: manhattan_precision
value: 70.83032490974729
- type: manhattan_recall
value: 69.87179487179486
- type: max_accuracy
value: 83.48960456583775
- type: max_ap
value: 76.31522115825375
- type: max_f1
value: 70.35573122529645
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: None
split: test
type: PL-MTEB/sickr-pl-sts
metrics:
- type: cos_sim_pearson
value: 77.95374883876302
- type: cos_sim_spearman
value: 73.77630219171942
- type: euclidean_pearson
value: 75.81927069594934
- type: euclidean_spearman
value: 73.7763211303831
- type: manhattan_pearson
value: 76.03126859057528
- type: manhattan_spearman
value: 73.96528138013369
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 37.388282764841826
- type: cos_sim_spearman
value: 40.83477184710897
- type: euclidean_pearson
value: 26.754737044177805
- type: euclidean_spearman
value: 40.83477184710897
- type: manhattan_pearson
value: 26.760453110872458
- type: manhattan_spearman
value: 41.034477441383856
task:
type: STS
- dataset:
config: default
name: MTEB SciFact-PL
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
split: test
type: clarin-knext/scifact-pl
metrics:
- type: map_at_1
value: 49.15
- type: map_at_10
value: 61.690999999999995
- type: map_at_100
value: 62.348000000000006
- type: map_at_1000
value: 62.38
- type: map_at_3
value: 58.824
- type: map_at_5
value: 60.662000000000006
- type: mrr_at_1
value: 51.333
- type: mrr_at_10
value: 62.731
- type: mrr_at_100
value: 63.245
- type: mrr_at_1000
value: 63.275000000000006
- type: mrr_at_3
value: 60.667
- type: mrr_at_5
value: 61.93300000000001
- type: ndcg_at_1
value: 51.333
- type: ndcg_at_10
value: 67.168
- type: ndcg_at_100
value: 69.833
- type: ndcg_at_1000
value: 70.56700000000001
- type: ndcg_at_3
value: 62.40599999999999
- type: ndcg_at_5
value: 65.029
- type: precision_at_1
value: 51.333
- type: precision_at_10
value: 9.333
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.333
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 49.15
- type: recall_at_10
value: 82.533
- type: recall_at_100
value: 94.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 69.917
- type: recall_at_5
value: 76.356
task:
type: Retrieval
- dataset:
config: default
name: MTEB TRECCOVID-PL
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
split: test
type: clarin-knext/trec-covid-pl
metrics:
- type: map_at_1
value: 0.261
- type: map_at_10
value: 2.1260000000000003
- type: map_at_100
value: 12.171999999999999
- type: map_at_1000
value: 26.884999999999998
- type: map_at_3
value: 0.695
- type: map_at_5
value: 1.134
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 96.952
- type: mrr_at_100
value: 96.952
- type: mrr_at_1000
value: 96.952
- type: mrr_at_3
value: 96.667
- type: mrr_at_5
value: 96.667
- type: ndcg_at_1
value: 92.0
- type: ndcg_at_10
value: 81.193
- type: ndcg_at_100
value: 61.129
- type: ndcg_at_1000
value: 51.157
- type: ndcg_at_3
value: 85.693
- type: ndcg_at_5
value: 84.129
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 85.39999999999999
- type: precision_at_100
value: 62.03999999999999
- type: precision_at_1000
value: 22.224
- type: precision_at_3
value: 88.0
- type: precision_at_5
value: 88.0
- type: recall_at_1
value: 0.261
- type: recall_at_10
value: 2.262
- type: recall_at_100
value: 14.981
- type: recall_at_1000
value: 46.837
- type: recall_at_3
value: 0.703
- type: recall_at_5
value: 1.172
task:
type: Retrieval
- dataset:
config: default
name: MTEB AlloProfClusteringP2P
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 70.55290063940157
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloProfClusteringS2S
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 55.41500719337263
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloprofReranking
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
split: test
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
metrics:
- type: map
value: 73.48697375332002
- type: mrr
value: 75.01836585523822
task:
type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: map_at_1
value: 38.454
- type: map_at_10
value: 51.605000000000004
- type: map_at_100
value: 52.653000000000006
- type: map_at_1000
value: 52.697
- type: map_at_3
value: 48.304
- type: map_at_5
value: 50.073
- type: mrr_at_1
value: 43.307
- type: mrr_at_10
value: 54.400000000000006
- type: mrr_at_100
value: 55.147999999999996
- type: mrr_at_1000
value: 55.174
- type: mrr_at_3
value: 51.77
- type: mrr_at_5
value: 53.166999999999994
- type: ndcg_at_1
value: 43.307
- type: ndcg_at_10
value: 57.891000000000005
- type: ndcg_at_100
value: 62.161
- type: ndcg_at_1000
value: 63.083
- type: ndcg_at_3
value: 51.851
- type: ndcg_at_5
value: 54.605000000000004
- type: precision_at_1
value: 43.307
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.172
- type: precision_at_1000
value: 0.127
- type: precision_at_3
value: 22.798
- type: precision_at_5
value: 15.492
- type: recall_at_1
value: 38.454
- type: recall_at_10
value: 74.166
- type: recall_at_100
value: 92.43599999999999
- type: recall_at_1000
value: 99.071
- type: recall_at_3
value: 58.087
- type: recall_at_5
value: 64.568
task:
type: Retrieval
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 53.474
- type: f1
value: 50.38275392350236
task:
type: Classification
- dataset:
config: default
name: MTEB BSARDRetrieval
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
metrics:
- type: map_at_1
value: 2.252
- type: map_at_10
value: 4.661
- type: map_at_100
value: 5.271
- type: map_at_1000
value: 5.3629999999999995
- type: map_at_3
value: 3.604
- type: map_at_5
value: 4.3020000000000005
- type: mrr_at_1
value: 2.252
- type: mrr_at_10
value: 4.661
- type: mrr_at_100
value: 5.271
- type: mrr_at_1000
value: 5.3629999999999995
- type: mrr_at_3
value: 3.604
- type: mrr_at_5
value: 4.3020000000000005
- type: ndcg_at_1
value: 2.252
- type: ndcg_at_10
value: 6.3020000000000005
- type: ndcg_at_100
value: 10.342
- type: ndcg_at_1000
value: 13.475999999999999
- type: ndcg_at_3
value: 4.0649999999999995
- type: ndcg_at_5
value: 5.344
- type: precision_at_1
value: 2.252
- type: precision_at_10
value: 1.171
- type: precision_at_100
value: 0.333
- type: precision_at_1000
value: 0.059000000000000004
- type: precision_at_3
value: 1.802
- type: precision_at_5
value: 1.712
- type: recall_at_1
value: 2.252
- type: recall_at_10
value: 11.712
- type: recall_at_100
value: 33.333
- type: recall_at_1000
value: 59.458999999999996
- type: recall_at_3
value: 5.405
- type: recall_at_5
value: 8.559
task:
type: Retrieval
- dataset:
config: default
name: MTEB HALClusteringS2S
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
split: test
type: lyon-nlp/clustering-hal-s2s
metrics:
- type: v_measure
value: 28.301882091023288
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringP2P
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 45.26992995191701
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringS2S
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 42.773174876871145
task:
type: Clustering
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 93.47635452552458
- type: f1
value: 93.19922617577213
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 80.2317569683683
- type: f1
value: 56.18060418621901
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClassification (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: accuracy
value: 85.18957345971565
- type: f1
value: 80.829981537394
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringP2P (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 71.04138999801822
task:
type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringS2S (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 71.7056263158008
task:
type: Clustering
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 76.65097511768661
- type: f1
value: 73.82441070598712
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 79.09885675857431
- type: f1
value: 78.28407777434224
task:
type: Classification
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
metrics:
- type: map_at_1
value: 25.307000000000002
- type: map_at_10
value: 36.723
- type: map_at_100
value: 37.713
- type: map_at_1000
value: 37.769000000000005
- type: map_at_3
value: 33.77
- type: map_at_5
value: 35.463
- type: mrr_at_1
value: 25.307000000000002
- type: mrr_at_10
value: 36.723
- type: mrr_at_100
value: 37.713
- type: mrr_at_1000
value: 37.769000000000005
- type: mrr_at_3
value: 33.77
- type: mrr_at_5
value: 35.463
- type: ndcg_at_1
value: 25.307000000000002
- type: ndcg_at_10
value: 42.559999999999995
- type: ndcg_at_100
value: 47.457
- type: ndcg_at_1000
value: 49.162
- type: ndcg_at_3
value: 36.461
- type: ndcg_at_5
value: 39.504
- type: precision_at_1
value: 25.307000000000002
- type: precision_at_10
value: 6.106
- type: precision_at_100
value: 0.8420000000000001
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 14.741999999999999
- type: precision_at_5
value: 10.319
- type: recall_at_1
value: 25.307000000000002
- type: recall_at_10
value: 61.056999999999995
- type: recall_at_100
value: 84.152
- type: recall_at_1000
value: 98.03399999999999
- type: recall_at_3
value: 44.226
- type: recall_at_5
value: 51.597
task:
type: Retrieval
- dataset:
config: fr
name: MTEB OpusparcusPC (fr)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test
type: GEM/opusparcus
metrics:
- type: cos_sim_accuracy
value: 99.90069513406156
- type: cos_sim_ap
value: 100.0
- type: cos_sim_f1
value: 99.95032290114257
- type: cos_sim_precision
value: 100.0
- type: cos_sim_recall
value: 99.90069513406156
- type: dot_accuracy
value: 99.90069513406156
- type: dot_ap
value: 100.0
- type: dot_f1
value: 99.95032290114257
- type: dot_precision
value: 100.0
- type: dot_recall
value: 99.90069513406156
- type: euclidean_accuracy
value: 99.90069513406156
- type: euclidean_ap
value: 100.0
- type: euclidean_f1
value: 99.95032290114257
- type: euclidean_precision
value: 100.0
- type: euclidean_recall
value: 99.90069513406156
- type: manhattan_accuracy
value: 99.90069513406156
- type: manhattan_ap
value: 100.0
- type: manhattan_f1
value: 99.95032290114257
- type: manhattan_precision
value: 100.0
- type: manhattan_recall
value: 99.90069513406156
- type: max_accuracy
value: 99.90069513406156
- type: max_ap
value: 100.0
- type: max_f1
value: 99.95032290114257
task:
type: PairClassification
- dataset:
config: fr
name: MTEB PawsX (fr)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: paws-x
metrics:
- type: cos_sim_accuracy
value: 70.8
- type: cos_sim_ap
value: 73.7671529695957
- type: cos_sim_f1
value: 68.80964339527875
- type: cos_sim_precision
value: 62.95955882352941
- type: cos_sim_recall
value: 75.85825027685493
- type: dot_accuracy
value: 70.8
- type: dot_ap
value: 73.78345265366947
- type: dot_f1
value: 68.80964339527875
- type: dot_precision
value: 62.95955882352941
- type: dot_recall
value: 75.85825027685493
- type: euclidean_accuracy
value: 70.8
- type: euclidean_ap
value: 73.7671529695957
- type: euclidean_f1
value: 68.80964339527875
- type: euclidean_precision
value: 62.95955882352941
- type: euclidean_recall
value: 75.85825027685493
- type: manhattan_accuracy
value: 70.75
- type: manhattan_ap
value: 73.78996383615953
- type: manhattan_f1
value: 68.79432624113475
- type: manhattan_precision
value: 63.39869281045751
- type: manhattan_recall
value: 75.1937984496124
- type: max_accuracy
value: 70.8
- type: max_ap
value: 73.78996383615953
- type: max_f1
value: 68.80964339527875
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICKFr
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
metrics:
- type: cos_sim_pearson
value: 84.03253762760392
- type: cos_sim_spearman
value: 79.68280105762004
- type: euclidean_pearson
value: 80.98265050044444
- type: euclidean_spearman
value: 79.68233242682867
- type: manhattan_pearson
value: 80.9678911810704
- type: manhattan_spearman
value: 79.70264097683109
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 80.56896987572884
- type: cos_sim_spearman
value: 81.84352499523287
- type: euclidean_pearson
value: 80.40831759421305
- type: euclidean_spearman
value: 81.84352499523287
- type: manhattan_pearson
value: 80.74333857561238
- type: manhattan_spearman
value: 82.41503246733892
task:
type: STS
- dataset:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
split: test
type: stsb_multi_mt
metrics:
- type: cos_sim_pearson
value: 82.71826762276979
- type: cos_sim_spearman
value: 82.25433354916042
- type: euclidean_pearson
value: 81.87115571724316
- type: euclidean_spearman
value: 82.25322342890107
- type: manhattan_pearson
value: 82.11174867527224
- type: manhattan_spearman
value: 82.55905365203084
task:
type: STS
- dataset:
config: default
name: MTEB SummEvalFr
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
split: test
type: lyon-nlp/summarization-summeval-fr-p2p
metrics:
- type: cos_sim_pearson
value: 30.659441623392887
- type: cos_sim_spearman
value: 30.501134097353315
- type: dot_pearson
value: 30.659444768851056
- type: dot_spearman
value: 30.501134097353315
task:
type: Summarization
- dataset:
config: default
name: MTEB SyntecReranking
revision: b205c5084a0934ce8af14338bf03feb19499c84d
split: test
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
metrics:
- type: map
value: 94.03333333333333
- type: mrr
value: 94.03333333333333
task:
type: Reranking
- dataset:
config: default
name: MTEB SyntecRetrieval
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
split: test
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
metrics:
- type: map_at_1
value: 79.0
- type: map_at_10
value: 87.61
- type: map_at_100
value: 87.655
- type: map_at_1000
value: 87.655
- type: map_at_3
value: 87.167
- type: map_at_5
value: 87.36699999999999
- type: mrr_at_1
value: 79.0
- type: mrr_at_10
value: 87.61
- type: mrr_at_100
value: 87.655
- type: mrr_at_1000
value: 87.655
- type: mrr_at_3
value: 87.167
- type: mrr_at_5
value: 87.36699999999999
- type: ndcg_at_1
value: 79.0
- type: ndcg_at_10
value: 90.473
- type: ndcg_at_100
value: 90.694
- type: ndcg_at_1000
value: 90.694
- type: ndcg_at_3
value: 89.464
- type: ndcg_at_5
value: 89.851
- type: precision_at_1
value: 79.0
- type: precision_at_10
value: 9.9
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 32.0
- type: precision_at_5
value: 19.400000000000002
- type: recall_at_1
value: 79.0
- type: recall_at_10
value: 99.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 96.0
- type: recall_at_5
value: 97.0
task:
type: Retrieval
- dataset:
config: fr
name: MTEB XPQARetrieval (fr)
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
split: test
type: jinaai/xpqa
metrics:
- type: map_at_1
value: 39.395
- type: map_at_10
value: 59.123999999999995
- type: map_at_100
value: 60.704
- type: map_at_1000
value: 60.760000000000005
- type: map_at_3
value: 53.187
- type: map_at_5
value: 56.863
- type: mrr_at_1
value: 62.083
- type: mrr_at_10
value: 68.87299999999999
- type: mrr_at_100
value: 69.46900000000001
- type: mrr_at_1000
value: 69.48299999999999
- type: mrr_at_3
value: 66.8
- type: mrr_at_5
value: 67.928
- type: ndcg_at_1
value: 62.083
- type: ndcg_at_10
value: 65.583
- type: ndcg_at_100
value: 70.918
- type: ndcg_at_1000
value: 71.72800000000001
- type: ndcg_at_3
value: 60.428000000000004
- type: ndcg_at_5
value: 61.853
- type: precision_at_1
value: 62.083
- type: precision_at_10
value: 15.033
- type: precision_at_100
value: 1.9529999999999998
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 36.315
- type: precision_at_5
value: 25.955000000000002
- type: recall_at_1
value: 39.395
- type: recall_at_10
value: 74.332
- type: recall_at_100
value: 94.729
- type: recall_at_1000
value: 99.75500000000001
- type: recall_at_3
value: 57.679
- type: recall_at_5
value: 65.036
task:
type: Retrieval
---
## gte-Qwen2-1.5B-instruct
**gte-Qwen2-1.5B-instruct** is the latest model in the gte (General Text Embedding) model family. The model is built on [Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) LLM model and use the same training data and strategies as the [gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) model.
The model incorporates several key advancements:
- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
- Instruction tuning, applied solely on the query side for streamlined efficiency
- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
## Model Information
- Model Size: 1.5B
- Embedding Dimension: 1536
- Max Input Tokens: 32k
## Requirements
```
transformers>=4.39.2
flash_attn>=2.5.6
```
## Usage
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-1.5B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
```
Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice.
### Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
### infinity_emb
Usage via [infinity, MIT Licensed](https://github.com/michaelfeil/infinity).
```bash
docker run \
--gpus "0" -p "7997":"7997" \
michaelf34/infinity:0.0.68-trt-onnx \
v2 --model-id Alibaba-NLP/gte-Qwen2-1.5B-instruct --revision "refs/pr/20" --dtype bfloat16 --batch-size 16 --device cuda --engine torch --port 7997 --no-bettertransformer
```
## Evaluation
### MTEB & C-MTEB
You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-1.5B-instruct** on MTEB(English)/C-MTEB(Chinese):
| Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) |
|:----:|:---------:|:----------:|:----------:|:----------:|
| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | - | - |
| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | - | - |
| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | - | - |
| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | - | - |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | - | - |
| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | - | - |
| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | - | - |
| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | - | - |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | - | - |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | - | - |
| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | - | - |
| [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | - | - |
| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | - | - |
| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | **68.25** | **67.86** |
| [**gte-Qwen2-1.5B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | **67.16** | **67.65** | **66.60** | **64.04** |
### GTE Models
The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).
| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |
|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:|
| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB |
| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB |
| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB |
| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB |
| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB |
| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB |
| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB |
| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB |
| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB |
| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB |
| [GTE-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | Multilingual | 32000 | 1536 | 6.62GB |
## Cloud API Services
In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
## Community support
### Fine-tuning
GTE models can be fine-tuned with a third party framework SWIFT.
```shell
pip install ms-swift -U
```
```shell
# check: https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html
nproc_per_node=8
NPROC_PER_NODE=$nproc_per_node \
USE_HF=1 \
swift sft \
--model Alibaba-NLP/gte-Qwen2-1.5B-instruct \
--train_type lora \
--dataset 'sentence-transformers/stsb' \
--torch_dtype bfloat16 \
--num_train_epochs 10 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps $(expr 64 / $nproc_per_node) \
--eval_steps 100 \
--save_steps 100 \
--eval_strategy steps \
--use_chat_template false \
--save_total_limit 5 \
--logging_steps 5 \
--output_dir output \
--warmup_ratio 0.05 \
--learning_rate 5e-6 \
--deepspeed zero3 \
--dataloader_num_workers 4 \
--task_type embedding \
--loss_type cosine_similarity \
--dataloader_drop_last true
```
## Citation
If you find our paper or models helpful, please consider cite:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
```
|
eddieman78/litbank-coref-gemma-3-12b-it-4000-64-1e4-5 | eddieman78 | 2025-06-16T15:52:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/gemma-3-12b-pt-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-pt-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T15:52:17Z | ---
base_model: unsloth/gemma-3-12b-pt-unsloth-bnb-4bit
library_name: transformers
model_name: litbank-coref-gemma-3-12b-it-4000-64-1e4-5
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for litbank-coref-gemma-3-12b-it-4000-64-1e4-5
This model is a fine-tuned version of [unsloth/gemma-3-12b-pt-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-12b-pt-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="eddieman78/litbank-coref-gemma-3-12b-it-4000-64-1e4-5", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
marielalvs/hatespeech-bert | marielalvs | 2025-06-16T15:41:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:thefrankhsu/hate_speech_twitter",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-16T06:38:48Z | ---
license: mit
datasets:
- thefrankhsu/hate_speech_twitter
language:
- en
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: transformers
--- |
Rishu7can/new22-edulaw-model | Rishu7can | 2025-06-16T15:41:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T15:40:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
aieng-lab/Llama-3.2-1B_comment-type-java | aieng-lab | 2025-06-16T15:37:51Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"en",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T17:33:52Z | ---
library_name: transformers
license: llama3.2
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- meta-llama/Llama-3.2-1B
pipeline_tag: text-classification
---
# Llama 3.2 1b for classifying code comments (multi-label)
This model classifies comments in Java code as 'summary', 'ownership', 'expand', 'usage', 'pointer', 'deprecation' or rational'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** Llama 3.2 Community License Agreement
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
shihuai7189/ppo-LunarLander-v2-clip-coef0.2-1000000 | shihuai7189 | 2025-06-16T15:36:30Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-16T15:36:19Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -238.73 +/- 90.61
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'notebook'
'seed': 237
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'shihuai7189/ppo-LunarLander-v2-clip-coef0.2-1000000'
'batch_size': 512
'minibatch_size': 128}
```
|
Artificed/whisper-tiny-minds14 | Artificed | 2025-06-16T15:34:28Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-06-16T15:16:34Z | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- wer
model-index:
- name: whisper-tiny-minds14
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3742757821552723
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-minds14
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9486
- Wer Ortho: 37.2585
- Wer: 0.3743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|
| 0.009 | 17.8571 | 500 | 0.9486 | 37.2585 | 0.3743 |
### Framework versions
- Transformers 4.50.0.dev0
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.1
|
proxectonos/nos-coda_iacobus-en-gl | proxectonos | 2025-06-16T15:26:18Z | 0 | 0 | null | [
"en",
"gl",
"license:mit",
"region:us"
] | null | 2025-06-16T12:15:48Z | ---
license: mit
language:
- en
- gl
metrics:
- bleu (Flores_dev): 21.2
- bleu (Flores_devtest): 26.3
- bleu (Ntrex): 27.6
---
**Descrición do Modelo / Descrição do Modelo**
Modelo feito con OpenNMT-py 3.2 para o par inglés-galego utilizando unha arquitectura transformer. Este modelo foi creado no ámbito dunha colaboración entre o Proxecto Nós (Universidade de Santiago de Compostela) e o CODA (Centre for Digital Culture and Innovation) da Faculdade de Letras na Universidade do Porto
Modelo desenvolvido com o OpenNMT-py 3.2 para o par inglês-galego, utilizando uma arquitetura transformer. Este modelo foi criado no âmbito de uma colaboração entre o Projeto Nós (Universidade de Santiago de Compostela) e o CODA (Centre for Digital Culture and Innovation) da Faculdade de Letras na Universidade do Porto.
**Como traducir con este Modelo / Como traduzir com este modelo**
### 🔧 Requisitos e Instalación / Requisitos e Instalação
1. Instale o [Python 3.9](https://www.python.org/downloads/release/python-390/).
2. Instale o OpenNMT-py 3.2 seguindo as instrucións da [documentação oficial](https://opennmt.net/OpenNMT-py/).
2. Instale o OpenNMT-py 3.2 seguindo as instruções da [documentação oficial](https://opennmt.net/OpenNMT-py/).
---
### 📄 Como traduzir un ficheiro con este modelo / Como traduzir um ficheiro com este modelo
Estas instrucións asumen que o ficheiro de entrada se chama `eng_Latn.dev` (extraído do dataset FLoRes)
Estas instruções assumem que o ficheiro de entrada se chama `eng_Latn.dev` (extraído do dataset FLoRes).
1. **Aplicar BPE (Byte Pair Encoding)**:
```bash
subword-nmt apply-bpe -c en_35k.code < eng_Latn.dev > eng_Latn.dev.35k.bpe
```
2. **Executar a tradución co modelo / Executar a tradução com o modelo**:
```bash
onmt_translate -model nos-coda_iacobus-en-gl.pt -src eng_Latn.dev.35k.bpe -output translation.txt -gpu 0 -verbose -replace_unk
```
3. **Limpar os marcadores BPE (@)**:
```bash
sed 's/@\s*//g' translation.txt > translation.tok
```
4. **Detokenizar o texto traducido/traduzido**:
```bash
perl detokenizer.perl < translation.tok > translation.translation
```
**Adestramento / Treino**
No adestramento, utilizamos córpora auténticos e sintéticos obtidos a partir do [OPUS](https://opus.nlpl.eu) e do [ProxectoNós](https://github.com/proxectonos/corpora) para os pares lingüísticos relacionados co galego e o portugués.
No treino, utilizámos corpora autênticos e sintéticos obtidos a partir do [OPUS](https://opus.nlpl.eu) e do [ProjetoNós](https://github.com/proxectonos/corpora), para os pares linguísticos relacionados com o galego e o português.
**Procedemento de adestramento / Procedimento de treino**
### ⚙️ Preparación dos datos / Preparação dos dados
* A tokenización dos *datasets* foi realizada co tokenizador (`tokenizer.pl`) do [Linguakit](https://github.com/citiususc/Linguakit), modificado para evitar a inserción de quebras de liña após cada *token* no ficheiro orixinal.
* A tokenização dos *datasets* foi realizada com o tokenizador (`tokenizer.pl`) do [Linguakit](https://github.com/citiususc/Linguakit), modificado para evitar a inserção de quebras de linha após cada *token* no ficheiro original.
* O vocabulario BPE utilizado nos modelos foi xerado co script [`learn_bpe.py`](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT.
* O vocabulário BPE utilizado nos modelos foi gerado com o script [`learn_bpe.py`](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT.
### 🧪 Reproducir o adestramento / Reproduzir o treino
Para obter os mesmos resultados, deben ser executadas as seguintes instrucións:
Para obter os mesmos resultados, devem ser executadas as seguintes instruções:
1. **Construír o vocabulario / Construir o vocabulário**:
```bash
onmt_build_vocab -config en-gl.yaml -n_sample 35000
```
2. **Adestrar o modelo NMT / Treinar o modelo NMT**:
```bash
onmt_train -config en-gl.yaml
```
**Avaliação / Avaliación**
A avaliación BLEU dos modelos é realizada nos seguintes datasets: Flores, Floresdevtest, Ntrex.
A avaliação BLEU dos modelos é realizada nos seguintes datasets: Flores, Floresdevtest e Ntrex.
| Flores_dev | Flores_devtes | Ntrex |
| ------------- |:-------------:| -------:|
| 21.2 | 26.3 | 27.6 |
**Licenzas do Modelo**
MIT License
Copyright (c) 2023 Proxecto Nós
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
**Financiamento**
This model was developed during a research stay at FLUP (Faculdade de Letras da Universidade do Porto) in May and June 2024, as part of the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública and co-financed by the European Union – NextGenerationEU, within the framework of the ILENIA Project (reference: 2022/TL22/00215336).
The stay at the University of Porto (CODA-FLUP) was supported by the IACOBUS Programme (2024), a Galician–Portuguese initiative that facilitates, among other activities, research exchanges between Galician scholars and universities or technological centers in Northern Portugal, and vice versa. The programme is managed by the AECT Galicia–North of Portugal.
|
aieng-lab/CodeLlama-7b-hf_tone-bearing | aieng-lab | 2025-06-16T15:25:12Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"en",
"base_model:meta-llama/CodeLlama-7b-hf",
"base_model:finetune:meta-llama/CodeLlama-7b-hf",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T06:41:02Z | ---
library_name: transformers
license: llama2
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- meta-llama/CodeLlama-7b-hf
pipeline_tag: text-classification
---
# CodeLlama 7b for classifying non-technical communications
This model classifies developer interactions (e.g., GitHub issues, mailing lists) as 'non-technical' or 'technical'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [meta-llama/CodeLlama-7b-hf](https://huggingface.co/meta-llama/CodeLlama-7b-hf)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** Llama 2 Community License Agreement
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
CIMAI/finetuned-f8808709 | CIMAI | 2025-06-16T15:16:07Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"new",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:7085",
"loss:OnlineContrastiveLoss",
"custom_code",
"arxiv:1908.10084",
"base_model:Alibaba-NLP/gte-multilingual-base",
"base_model:finetune:Alibaba-NLP/gte-multi... | sentence-similarity | 2025-06-16T15:15:32Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7085
- loss:OnlineContrastiveLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
- source_sentence: What shape is located on the left side of the Motorola logo?
sentences:
- 'Notes
· <index> may be already occupied, in which case, the command will overwrite it.
· If <index> is missing, the MT will add the entry at the first free entry if
any (if no entry is available, it will return error code 20).
· If <index> is present and the other parameters are not present, this will erase/delete
the entry at <index> if it was non-empty.
· If <type> and <text> parameter are omitted in the command, by default they <type>
will set to 0 and <text> will be empty what cause that existing contact name will
be deleted.
· +CPBW operations are compliant to Address Book Restriction flag
· If <text> is not bounded in '' '' then spaces will be removed.'
- 'éé
Heb je meerdere winkels? Maar je wil van één specifieke locatie het transactieoverzicht
inzien. Dat kan via MyCCV binnen een paar klikken.
Selecteer bij Locaties, de juiste winkel(s) . Klik op Toepassen. De resultaten
worden direct getoond.'
- Not Applicable
- source_sentence: How do the arrows appear in relation to each other?
sentences:
- Press the Menu key and select Networks → Direct Mode or press the Menu key and
select Options → Direct Mode.
- The arrows are slightly curved and appear to be slightly overlapping each other.
- The purpose of the ITEM Service's history management function is to save item
history.
- source_sentence: What color is the circular logo described in the image?
sentences:
- 'Warnung:
· Lesen Sie vor dem Einbau elektronischer Geräte das Handbuch des Fahrzeugherstellers,
um Störungen der Airbagverdrahtung zu vermeiden.
· Platzieren Sie tragbare Funkgeräte NIE im Bereich über dem Airbag bzw. im Einsatzbereich
des Airbags. Ein Airbag kommt mit großer Wucht zum Einsatz. Wenn ein tragbares
Funkgerät sich im Auslösebereich des Airbags befindet und der Airbag ausgelöst
wird, kann das Funkgerät mit großer Kraft ins Fahrzeuginnere geschleudert werden
und möglicherweise schwere Verletzungen für die Insassen verursachen.'
- '2.8.4.3
Derived Cipher Key and Common Cipher Keys Encryption (Class 3)
Derived Cipher Key and Common Cipher Keys Encryption (DCK/CCK) are required to
prevent overexposure of key material. Existing encryption systems use Static Cipher
Keys (SCK), where one key is used for all radios and all communications.
Key material is often exposed and SCK logistics of changing keys consist in programming
all radios and Base Stations. DCK is used for individually addressed TM-SDU (Service
Data Unit). DCK/CCK encryption provides Derived Cipher Key (DCK) for uplink (from
the radio to the BTS) communication and Common Cipher Key (CCK) for downlink (from
the BTS to the radios) group communication. The DCK is derived from either the
one way or mutual authentication process and the CCK is received during registry.
The radios supporting the dynamic key encryption identify themselves to the system
as Class 3 radios during registry and attempt to negotiate Class 3 encryption.
A Class 3 radio supports group addressed signaling and group communication traffic
encryption using CCKs as well as encryption of uplink and down link individually
addressed signaling messages and individual call traffic (private or phone) using
its DCKs. The radios support Over-the-Air-Rekeying (OTAR) of the CCK by the system.
A clear radio can transmit and receive from encrypted radios. The system informs
the encrypted radios that the communication is with a clear radio and they switch
to clear operation. Class 2 and 3 radios can only act as described if they are
allowed to operate in a lower class.'
- 'NOTE
To interpret all the details of the test results as shown in the ''log'', you
need to have some general knowledge of (D)COM and/or standard OPC foundation deliverables.
However, in most cases this knowledge is not necessary. It is enough to verify
that:
· Category OPC Common Definitions:
All cases (with one possible exception, see below) have passed the test.
The OPC AE proxy test is a not relevant situation. This proxy is not necessary
for the current implementation of the CI Server OPC DA interface.
· Category Server Specific Checks: All cases have passed the test.'
- source_sentence: What is the specified operating temperature range for the equipment
according to the table?
sentences:
- '| ステ-タス | 充電器の ディスプレイ | LED インジケ-タ |
| --- | --- | --- |
| | | |
| 充電器の電源投入 | IMPRES 2 CHARGER | 約 1 秒間緑色になる |
| バッテリが検出された | IMPRES 2 BATTERY または IMPRES BATTERY | 黄色の点灯 |
| バッテリ放電中 | STORAGE DISCHRGE xx% Rated Cap | 黄色の点灯 |
| 長期間保管が完了 | LONGTERM STORAGE xx% Rated Cap | 緑色の点灯 |'
- 'n Properties
This section describes the spinner component properties.
Continues on the next page'
- 30 to +60
- source_sentence: What does the 'Get alarms' function do in the ASPA tool?
sentences:
- '3.5 IT environment settings
This section describes the Windows security functions that are applicable to the
CI Server.
There are cases where it is not possible to implement certain security functions
depending on the conditions of each system. Therefore, before implementing the
security functions, analyze whether it is possible to implement the security function
to the CI Server.'
- "Time stamp\tValue\n10:00:03\t2"
- '5.6.3.4
Removing the SIM Dust Cover
Procedure:
1 Remove the dust cover screw using a Phillips screwdriver.
2 Remove the dust cover from the chassis.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
"What does the 'Get alarms' function do in the ASPA tool?",
'3.5 IT environment settings\nThis section describes the Windows security functions that are applicable to the CI Server.\nThere are cases where it is not possible to implement certain security functions depending on the conditions of each system. Therefore, before implementing the security functions, analyze whether it is possible to implement the security function to the CI Server.',
'Time stamp\tValue\n10:00:03\t2',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 7,085 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 10 tokens</li><li>mean: 17.16 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 118.19 tokens</li><li>max: 2487 tokens</li></ul> | <ul><li>0: ~79.50%</li><li>1: ~20.50%</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>What is the type of equipment covered by the EC-Type Examination Certificate BVS 12 ATEX E 027 X?</code> | <code>DMO Calls<br>You can make and receive Group Calls when the CEP400 operates in Direct Mode (DMO). This includes Emergency Group and Private Calls.<br>The CEP400 returns to the Home display when there is no activity for a few seconds.</code> | <code>0</code> |
| <code>How can historical data be imported from external sources?</code> | <code>Historical data can be imported from external sources using the item history loader utility, which requires the data to be provided in a comma-separated value (CSV) format.</code> | <code>1</code> |
| <code>What does the solid icon indicate in DMO Gateway Communication Mode?</code> | <code>Menu d'étalonnage<br>Les sélections de la configuration de l'étalonnage permettent d'activer ou désactiver la phase de déchargement de l'étalonnage/remise en état. La phase de déchargement précède la charge pleine requise pour un étalonnage/remise en état réussi. Cette fonction est utile lorsque le chargeur est utilisé sur un site où il est nécessaire que la batterie soit chargée et prête pour l'utilisation le plus rapidement possible. Dans ces situations, attendre quelques heures de plus pour une décharge complète peut être contraignant.<br>Lors de l'insertion dans le chargeur d'une batterie IMPRES 2 ou IMPRES nécessitant un étalonnage et déjà déchargée, le chargeur effectuera la charge de la batterie en plus de l'étalonner.</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.5643 | 500 | 0.149 |
| 1.1287 | 1000 | 0.0959 |
| 1.6930 | 1500 | 0.0455 |
| 2.2573 | 2000 | 0.0347 |
| 2.8217 | 2500 | 0.0422 |
| 3.3860 | 3000 | 0.0349 |
| 3.9503 | 3500 | 0.027 |
| 4.5147 | 4000 | 0.0213 |
| 5.0790 | 4500 | 0.0211 |
| 5.6433 | 5000 | 0.0195 |
| 6.2077 | 5500 | 0.0237 |
| 6.7720 | 6000 | 0.0208 |
| 7.3363 | 6500 | 0.013 |
| 7.9007 | 7000 | 0.0228 |
| 8.4650 | 7500 | 0.0147 |
| 9.0293 | 8000 | 0.0186 |
| 9.5937 | 8500 | 0.0142 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.49.0
- PyTorch: 2.4.1+cu121
- Accelerate: 1.5.2
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
yinchenghust/nora_libero_cot | yinchenghust | 2025-06-16T15:09:29Z | 30 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-16T03:49:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Trentz/emotion-classification-ensemble | Trentz | 2025-06-16T15:08:45Z | 0 | 0 | null | [
"safetensors",
"text-classification",
"multi-label",
"emotion-classification",
"ensemble",
"deberta",
"roberta",
"en",
"license:mit",
"region:us"
] | text-classification | 2025-06-16T14:53:19Z |
---
license: mit
language: en
pipeline_tag: text-classification
tags:
- text-classification
- multi-label
- emotion-classification
- ensemble
- deberta
- roberta
---
# Ensemble Model untuk Klasifikasi Emosi Multi-Label
Ini adalah repositori untuk sistem model *ensemble* yang meraih peringkat pertama dalam tugas klasifikasi emosi multi-label.
Sistem ini menggabungkan dua model kuat, **DeBERTa-v3-Large** dan **RoBERTa-Large**, yang dilatih dengan teknik LLRD (Layer-wise Learning Rate Decay) dan Focal Loss.
## Komponen Ensemble
- **`deberta_model`**: Model `microsoft/deberta-v3-large` yang telah di-fine-tune.
- **`roberta_model`**: Model `roberta-large` yang telah di-fine-tune.
- **`best_thresholds.json`**: Array berisi 14 nilai *threshold* optimal untuk setiap label, yang digunakan pada hasil rata-rata probabilitas kedua model.
## Cara Menggunakan
Berikut adalah contoh kode untuk memuat semua komponen dan melakukan prediksi dengan *ensemble* ini:
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from scipy.special import expit as sigmoid
import json
import requests
import numpy as np
# -- Informasi Repositori --
REPO_ID = "Trentz/emotion-classification-ensemble"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# -- Label Mapping --
LABELS = ['amusement', 'anger', 'annoyance', 'caring', 'confusion', 'disappointment', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'joy', 'love', 'sadness']
class EmotionEnsemble:
def __init__(self, repo_id, device="cpu"):
self.device = device
print("Memuat semua komponen model...")
# Muat DeBERTa
self.deberta_tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="deberta_model")
self.deberta_model = AutoModelForSequenceClassification.from_pretrained(repo_id, subfolder="deberta_model").to(self.device).eval()
# Muat RoBERTa
self.roberta_tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="roberta_model")
self.roberta_model = AutoModelForSequenceClassification.from_pretrained(repo_id, subfolder="roberta_model").to(self.device).eval()
# Muat thresholds
thresholds_url = f"[https://huggingface.co/](https://huggingface.co/)Trentz/emotion-classification-ensemble/resolve/main/best_thresholds.json"
response = requests.get(thresholds_url)
self.thresholds = torch.tensor(response.json(), device=self.device)
print("Semua komponen berhasil dimuat.")
def predict(self, text: str):
with torch.no_grad():
# Prediksi DeBERTa
deberta_inputs = self.deberta_tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(self.device)
deberta_probs = torch.sigmoid(self.deberta_model(**deberta_inputs).logits).squeeze()
# Prediksi RoBERTa
roberta_inputs = self.roberta_tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(self.device)
roberta_probs = torch.sigmoid(self.roberta_model(**roberta_inputs).logits).squeeze()
# Rata-ratakan probabilitas
avg_probs = (deberta_probs + roberta_probs) / 2.0
# Terapkan threshold & logika "Best Guess"
preds = (avg_probs > self.thresholds).int()
if preds.sum() == 0:
best_guess_idx = torch.argmax(avg_probs).item()
final_labels = [LABELS[best_guess_idx]]
else:
final_labels = [LABELS[i] for i, pred in enumerate(preds) if pred == 1]
return { "text": text, "predicted_emotions": final_labels, "scores": avg_probs.cpu().tolist() }
# -- Contoh Penggunaan --
# Inisialisasi model ensemble
ensemble_model = EmotionEnsemble(REPO_ID, device=DEVICE)
# Prediksi teks
example_text = "This is amazing! Thank you so much for everything, I really love it."
result = ensemble_model.predict(example_text)
print(result)
# Diharapkan output mengandung: 'amusement', 'excitement', 'joy', 'love', 'gratitude'
example_text_2 = "I can't believe you would do that. It's so annoying and disappointing."
result_2 = ensemble_model.predict(example_text_2)
print(result_2)
# Diharapkan output mengandung: 'annoyance', 'disappointment', 'anger'
|
fabiancpl/nlbse25_python | fabiancpl | 2025-06-16T15:07:54Z | 10 | 0 | setfit | [
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"en",
"dataset:NLBSE/nlbse25-code-comment-classification",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"license... | text-classification | 2024-12-13T02:21:18Z | ---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget: []
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: text-classification
library_name: setfit
inference: true
license: mit
datasets:
- NLBSE/nlbse25-code-comment-classification
language:
- en
base_model:
- sentence-transformers/all-MiniLM-L6-v2
---
# Python comment classifier
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Python code comment classification.
The model has been trained using few-shot learning that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned model.
## Model Description
- **Model Type:** SetFit
- **Classification head:** [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
## Sources
- **Repository:** [GitHub](https://github.com/fabiancpl/sbert-comment-classification/)
- **Paper:** [Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification](https://ieeexplore.ieee.org/document/11029440)
- **Dataset:** [HF Dataset](https://huggingface.co/datasets/NLBSE/nlbse25-code-comment-classification)
## How to use it
First, install the depencies:
```bash
pip install setfit scikit-learn
```
Then, load the model and run inferences:
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("fabiancpl/nlbse25_python")
# Run inference
preds = model("This function sorts a list of numbers.")
```
## Cite as
```bibtex
@inproceedings{11029440,
author={Peña, Fabian C. and Herbold, Steffen},
booktitle={2025 IEEE/ACM International Workshop on Natural Language-Based Software Engineering (NLBSE)},
title={Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification},
year={2025},
pages={21-24},
doi={10.1109/NLBSE66842.2025.00010}}
```
|
sergioalves/4e0d911a-746e-4749-b1e0-c00c32db7d18 | sergioalves | 2025-06-16T15:02:43Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
"base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
"license:gemma",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-16T14:10:09Z | ---
library_name: peft
license: gemma
base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4e0d911a-746e-4749-b1e0-c00c32db7d18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- bea01d53a6178d17_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.8
group_by_length: false
hub_model_id: sergioalves/4e0d911a-746e-4749-b1e0-c00c32db7d18
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 300
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/bea01d53a6178d17_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fbed3039-989c-43a7-a6e5-e0769751d1d9
wandb_project: s56-7
wandb_run: your_name
wandb_runid: fbed3039-989c-43a7-a6e5-e0769751d1d9
warmup_steps: 30
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 4e0d911a-746e-4749-b1e0-c00c32db7d18
This model is a fine-tuned version of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6731 | 0.0005 | 1 | 1.3615 |
| 1.3601 | 0.0812 | 150 | 1.2488 |
| 1.2565 | 0.1624 | 300 | 1.1861 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
FatimaK6/breast-cancer-convnext-tiny | FatimaK6 | 2025-06-16T15:00:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"convnext",
"image-classification",
"medical",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-16T15:00:39Z | ---
library_name: transformers
tags:
- medical
- image-classification
- convnext
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
joanna302/Qwen3-4B-Base_fr_pt__8e-05_seed43 | joanna302 | 2025-06-16T14:54:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T12:28:59Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
SaraHe/aya_compress_Q1_Q4_16_no_attn_zero_layers | SaraHe | 2025-06-16T14:52:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:CohereLabs/aya-expanse-8b",
"base_model:finetune:CohereLabs/aya-expanse-8b",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T14:52:04Z | ---
base_model: CohereForAI/aya-expanse-8b
library_name: transformers
model_name: aya_compress_Q1_Q4_16_no_attn_zero_layers
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for aya_compress_Q1_Q4_16_no_attn_zero_layers
This model is a fine-tuned version of [CohereForAI/aya-expanse-8b](https://huggingface.co/CohereForAI/aya-expanse-8b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SaraHe/aya_compress_Q1_Q4_16_no_attn_zero_layers", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AKT47/Llama_3.1_1B_reasoning_sft_01 | AKT47 | 2025-06-16T14:51:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
... | text-generation | 2025-06-16T14:41:43Z | ---
base_model: unsloth/Llama-3.2-1B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** AKT47
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Ch0clat/roBERTa-Modul6 | Ch0clat | 2025-06-16T14:49:29Z | 0 | 0 | null | [
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"region:us"
] | null | 2025-06-16T14:40:34Z | ---
base_model:
- FacebookAI/roberta-base
--- |
FilipT/ltgbert-100m-2024-gender-ft | FilipT | 2025-06-16T14:22:12Z | 0 | 0 | null | [
"pytorch",
"safetensors",
"ltgbert",
"masked-lm",
"fine-tuned",
"custom_code",
"base_model:babylm/ltgbert-100m-2024",
"base_model:finetune:babylm/ltgbert-100m-2024",
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T14:21:36Z | ---
base_model: babylm/ltgbert-100m-2024
tags: [masked-lm, fine-tuned, ltgbert]
license: apache-2.0
---
|
avey-ai/mamba-dpa-0.5B-100BT | avey-ai | 2025-06-16T14:14:08Z | 4 | 0 | null | [
"pytorch",
"text-generation",
"en",
"dataset:HuggingFaceFW/fineweb",
"arxiv:2506.11305",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-04T15:24:27Z | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
language:
- en
pipeline_tag: text-generation
---
For use with https://github.avey.ai/avey-dpa. See https://arxiv.org/abs/2506.11305. |
Bonnief/afriberta-ti-hornmt | Bonnief | 2025-06-16T14:14:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"base_model:castorini/afriberta_small",
"base_model:finetune:castorini/afriberta_small",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-06-16T14:13:06Z | ---
library_name: transformers
base_model: castorini/afriberta_small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: afriberta-ti-hornmt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# afriberta-ti-hornmt
This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7774
- Accuracy: 0.3769
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
Tina94/3dmont | Tina94 | 2025-06-16T14:13:59Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-16T13:24:27Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: 3dmont
---
# 3Dmont
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `3dmont` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "3dmont",
"lora_weights": "https://huggingface.co/Tina94/3dmont/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Tina94/3dmont', weight_name='lora.safetensors')
image = pipeline('3dmont').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Tina94/3dmont/discussions) to add images that show off what you’ve made with this LoRA.
|
MariamBM/zephyr-lora-mariam | MariamBM | 2025-06-16T14:12:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"region:us"
] | null | 2025-06-16T14:06:55Z | ---
base_model: HuggingFaceH4/zephyr-7b-beta
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0 |
avey-ai/avey1-dpa-1.5B-100BT | avey-ai | 2025-06-16T14:10:31Z | 21 | 0 | null | [
"safetensors",
"avey",
"text-generation",
"en",
"dataset:HuggingFaceFW/fineweb",
"arxiv:2506.11305",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-04T14:53:58Z | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
language:
- en
pipeline_tag: text-generation
---
For use with https://github.avey.ai/avey-dpa. See https://arxiv.org/abs/2506.11305. |
avey-ai/avey1-dpa-0.1B-95BT | avey-ai | 2025-06-16T14:08:42Z | 2 | 0 | null | [
"safetensors",
"avey",
"text-generation",
"en",
"dataset:HuggingFaceFW/fineweb",
"arxiv:2506.11305",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-04T13:35:41Z | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
language:
- en
pipeline_tag: text-generation
---
For use with https://github.avey.ai/avey-dpa. See https://arxiv.org/abs/2506.11305. |
avey-ai/avey1-dpa-0.1B-100BT | avey-ai | 2025-06-16T14:08:25Z | 2 | 0 | null | [
"safetensors",
"avey",
"text-generation",
"en",
"dataset:HuggingFaceFW/fineweb",
"arxiv:2506.11305",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-04T13:33:07Z | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
language:
- en
pipeline_tag: text-generation
---
For use with https://github.avey.ai/avey-dpa. See https://arxiv.org/abs/2506.11305. |
ucmp137538/miqhybridmixed_simpo_grppol_concat_ckp_1k | ucmp137538 | 2025-06-16T14:06:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mamba-in-qwen",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T14:01:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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[More Information Needed]
### Results
[More Information Needed]
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## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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cragtmp/task2-noidk | cragtmp | 2025-06-16T14:05:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-11B-Vision-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct",
"region:us"
] | null | 2025-06-16T14:04:13Z | ---
base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0 |
chenhl19780810/llama3_2-3B-it-thinking-function_calling-V0 | chenhl19780810 | 2025-06-16T14:04:34Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T13:49:08Z | ---
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
model_name: llama3_2-3B-it-thinking-function_calling-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama3_2-3B-it-thinking-function_calling-V0
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chenhl19780810/llama3_2-3B-it-thinking-function_calling-V0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
yamaceay/phi-2-normalizer | yamaceay | 2025-06-16T14:01:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T13:34:01Z | ---
base_model: microsoft/phi-2
library_name: transformers
model_name: phi-2-normalizer
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for phi-2-normalizer
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="yamaceay/phi-2-normalizer", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
aieng-lab/codet5p-220m_se-entities | aieng-lab | 2025-06-16T13:53:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"token-classification",
"en",
"base_model:Salesforce/codet5p-220m",
"base_model:finetune:Salesforce/codet5p-220m",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-06-16T13:53:42Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- seqeval
base_model:
- Salesforce/codet5p-220m
pipeline_tag: token-classification
---
# CodeT5+ 220m for detecting software engineering terminology
This model detects software engineering terminology in developer forums (e.g., Stack Overflow) as 'Data_Structure', 'Application', 'Code_Block', 'Function", 'Data_Type', 'Language', 'Library', 'Variable', 'Device', 'User_Name', 'User_Interface_Element', 'Class', 'Website', 'Version', 'File_Name', 'File_Type', 'Operating_System', 'Output_Block', 'Algorithm' or 'HTML_XML_Tag'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
SaraHe/aya_compress_Q1_Q4_16_no_attn_layers | SaraHe | 2025-06-16T13:48:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:CohereLabs/aya-expanse-8b",
"base_model:finetune:CohereLabs/aya-expanse-8b",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T13:48:39Z | ---
base_model: CohereForAI/aya-expanse-8b
library_name: transformers
model_name: aya_compress_Q1_Q4_16_no_attn_layers
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for aya_compress_Q1_Q4_16_no_attn_layers
This model is a fine-tuned version of [CohereForAI/aya-expanse-8b](https://huggingface.co/CohereForAI/aya-expanse-8b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SaraHe/aya_compress_Q1_Q4_16_no_attn_layers", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
aieng-lab/ModernBERT-base_se-entities | aieng-lab | 2025-06-16T13:45:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"token-classification",
"en",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-06-16T13:45:41Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- seqeval
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: token-classification
---
# ModernBERT base for detecting software engineering terminology
This model detects software engineering terminology in developer forums (e.g., Stack Overflow) as 'Data_Structure', 'Application', 'Code_Block', 'Function", 'Data_Type', 'Language', 'Library', 'Variable', 'Device', 'User_Name', 'User_Interface_Element', 'Class', 'Website', 'Version', 'File_Name', 'File_Type', 'Operating_System', 'Output_Block', 'Algorithm' or 'HTML_XML_Tag'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_133512 | himedia | 2025-06-16T13:38:58Z | 0 | 0 | null | [
"safetensors",
"gemma",
"financial",
"credit-rating",
"korean",
"unsloth",
"fine-tuned",
"text-generation",
"conversational",
"ko",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-16T13:38:31Z | ---
language: ko
license: apache-2.0
base_model: unsloth/gemma-3-4b-it
tags:
- financial
- credit-rating
- korean
- gemma
- unsloth
- fine-tuned
model_name: FinCreditGemma-3-4B
pipeline_tag: text-generation
---
# FinCreditGemma-3-4B
## 모델 개요
FinCreditGemma-3-4B는 금융 신용 평가를 위해 특별히 설계된 한국어 언어 모델입니다.
**베이스 모델**: unsloth/gemma-3-4b-it
**모델 타입**: Gemma-3-4B
**데이터셋**: himedia/financial_dummy_data_v2
**학습 방법**: LoRA (Low-Rank Adaptation)
**학습 일시**: 20250616_133512
## 📊 학습 결과
- **Final Training Loss**: 2.3265
- **Final Validation Loss**: 2.3084
- **Best Validation Loss**: 2.3084 (step 10)
- **Overall Improvement**: -2.2%
- **Training Time**: 1.95 minutes
- **Vocab Size**: 256000
## 하이퍼파라미터
- **Learning Rate**: 5e-05
- **Max Steps**: 10
- **Batch Size**: 2
- **Gradient Accumulation**: 4
- **LoRA r**: 16
- **LoRA alpha**: 16
- **Max Sequence Length**: 2048
- **Warmup Steps**: 5
## 🔧 메모리 사용량
- **GPU**: Tesla T4
- **Peak Memory**: 5.57 GB
- **Memory Usage**: 37.8%
## 🔧 모델 구성
- **Hidden Size**: 4096
- **Attention Heads**: 32
- **Hidden Layers**: 32
- **Intermediate Size**: 14336
- **Key-Value Heads**: 16
## 사용 방법
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# 모델과 토크나이저 로드
tokenizer = AutoTokenizer.from_pretrained("himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_133512")
model = AutoModelForCausalLM.from_pretrained("himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_133512")
# 간단한 추론 예제
prompt = "고객의 신용등급을 평가해주세요:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 📊 학습 데이터 파일
이 레포지토리에는 다음 학습 관련 파일들이 포함되어 있습니다:
- `config.json`: Gemma 모델 설정 파일 (vocab_size 포함)
- `training_log.json`: 전체 학습 로그 (JSON 형식)
- `FinCreditGemma-3-4B_20250616_133512_training_curves.png`: 학습 곡선 시각화 이미지
## 레포지토리명 구성
```
fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_133512 = fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_133512
```
- `fincredit-gemma3-4b`: 모델 기본명
- `lr5e05`: Learning Rate
- `bs2`: Batch Size
- `r16`: LoRA rank
- `steps10`: 학습 스텝
- `20250616_133512`: 학습 시각
## 성능
이 모델은 한국어 금융 텍스트에 대해 파인튜닝되어 신용 평가 관련 질의응답에 특화되어 있습니다.
## 중요 사항
- Gemma 모델의 경우 config.json이 반드시 필요하며, vocab_size 값이 포함되어야 합니다.
- 현재 vocab_size: 256000
## 라이선스
Apache 2.0
|
sil-ai/madlad400-finetuned-eng-new | sil-ai | 2025-06-16T13:20:27Z | 8 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"eng",
"new",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-02-20T18:51:15Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-eng-new
results: []
language:
- eng
- new
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-eng-new
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from English to Newar.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** English (`eng`)
- **Target language:** Newar (`new`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
English:
- English back-translation of Newar Bible
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Newar:
- Newar Bible
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-eng-new")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-eng-new")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-eng-new
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0839
- Chrf: 80.9748
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5591 | 1.6529 | 1600 | 0.4669 | 54.3759 |
| 0.3654 | 3.3058 | 3200 | 0.2716 | 63.9375 |
| 0.3005 | 4.9587 | 4800 | 0.1920 | 68.7804 |
| 0.2228 | 6.6116 | 6400 | 0.1369 | 73.8741 |
| 0.1602 | 8.2645 | 8000 | 0.0995 | 78.4278 |
| 0.1437 | 9.9174 | 9600 | 0.0840 | 80.9463 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
cucucu666/zhoumei-6.16 | cucucu666 | 2025-06-16T13:17:08Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-Fill-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Fill-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-16T10:45:24Z | ---
base_model: black-forest-labs/FLUX.1-Fill-dev
library_name: diffusers
license: other
instance_prompt: labii face, Crayon Shin-chan style, frown expression, plain white
background
widget:
- text: labii face, Crayon Shin-chan style, frown expression, plain white background
output:
url: image_0.png
- text: labii face, Crayon Shin-chan style, frown expression, plain white background
output:
url: image_1.png
- text: labii face, Crayon Shin-chan style, frown expression, plain white background
output:
url: image_2.png
- text: labii face, Crayon Shin-chan style, frown expression, plain white background
output:
url: image_3.png
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux-Fill DreamBooth LoRA - cucucu666/zhoumei-6.16
<Gallery />
## Model description
These are cucucu666/zhoumei-6.16 DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `labii face, Crayon Shin-chan style, frown expression, plain white background` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cucucu666/zhoumei-6.16/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('cucucu666/zhoumei-6.16', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('labii face, Crayon Shin-chan style, frown expression, plain white background').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
ronald888/fused-mistral-7b-v0.3-4bit | ronald888 | 2025-06-16T13:14:23Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"gguf",
"mistral",
"text-generation",
"conversational",
"base_model:mlx-community/Mistral-7B-Instruct-v0.3-4bit",
"base_model:quantized:mlx-community/Mistral-7B-Instruct-v0.3-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:56:51Z | ---
license: apache-2.0
tags:
- mlx
library_name: mlx
pipeline_tag: text-generation
base_model: mlx-community/Mistral-7B-Instruct-v0.3-4bit
---
# ronald888/fused-mistral-7b-v0.3-4bit
This model [ronald888/fused-mistral-7b-v0.3-4bit](https://huggingface.co/ronald888/fused-mistral-7b-v0.3-4bit) was
converted to MLX format from [mlx-community/Mistral-7B-Instruct-v0.3-4bit](https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.3-4bit)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("ronald888/fused-mistral-7b-v0.3-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
tarundachepally/llama_1b_instruct | tarundachepally | 2025-06-16T13:08:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T13:08:51Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: transformers
model_name: llama_1b_instruct
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama_1b_instruct
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tarundachepally/llama_1b_instruct", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Sam-INFINTY/daliycookingrep | Sam-INFINTY | 2025-06-16T13:06:24Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"en",
"ta",
"dataset:SpeedyFrostfish/llama-3.2-3b-Cooking-ChatBot",
"base_model:google/gemma-3n-E4B-it-litert-preview",
"base_model:adapter:google/gemma-3n-E4B-it-litert-preview",
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T12:59:59Z | ---
license: apache-2.0
datasets:
- SpeedyFrostfish/llama-3.2-3b-Cooking-ChatBot
language:
- en
- ta
metrics:
- accuracy
base_model:
- google/gemma-3n-E4B-it-litert-preview
new_version: google/gemma-3n-E4B-it-litert-preview
library_name: adapter-transformers
--- |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.05_0.5_0.25_epoch1 | MinaMila | 2025-06-16T13:04:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T13:02:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
epidrone/Reinforce-Pixelcopter-PLE-v0 | epidrone | 2025-06-16T12:57:46Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-10T04:24:37Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 170.50 +/- 146.03
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Zack-Z/qwen3_4bi_cotsft_rs0_3_5cut_gem3all_indep_e2 | Zack-Z | 2025-06-16T12:47:03Z | 0 | 0 | transformers | [
"transformers",
"qwen3",
"feature-extraction",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-06-16T12:32:34Z | ---
base_model: unsloth/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Zack-Z
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
pang1203/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_thorny_tamarin | pang1203 | 2025-06-16T12:43:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am nasty thorny tamarin",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T17:56:47Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_thorny_tamarin
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am nasty thorny tamarin
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_thorny_tamarin
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="pang1203/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_thorny_tamarin", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Qwen3-0.6B-DPO-Peft-GGUF | mradermacher | 2025-06-16T12:28:48Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"ko",
"base_model:jaeyong2/Qwen3-0.6B-DPO-Peft",
"base_model:quantized:jaeyong2/Qwen3-0.6B-DPO-Peft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T12:22:31Z | ---
base_model: jaeyong2/Qwen3-0.6B-DPO-Peft
language:
- en
- ko
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/jaeyong2/Qwen3-0.6B-DPO-Peft
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-DPO-Peft-GGUF/resolve/main/Qwen3-0.6B-DPO-Peft.f16.gguf) | f16 | 1.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Varinder2110/2845b212-8112-4c67-8dc2-2adb6eded363 | Varinder2110 | 2025-06-16T12:22:26Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-16T11:38:28Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# 2845B212 8112 4C67 8Dc2 2Adb6Eded363
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/Varinder2110/2845b212-8112-4c67-8dc2-2adb6eded363/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Varinder2110/2845b212-8112-4c67-8dc2-2adb6eded363', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4000
- Learning rate: 0.0004
- LoRA rank: 12
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Varinder2110/2845b212-8112-4c67-8dc2-2adb6eded363/discussions) to add images that show off what you’ve made with this LoRA.
|
Kakwanzi-elizabeth-Viral-Video/Kakwanzi.elizabeth.uganda.trending.video.Original.Clip | Kakwanzi-elizabeth-Viral-Video | 2025-06-16T12:22:03Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T12:21:45Z | <a href="https://t.co/dTvnXACQMR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Trupthi/cnn_news_summary_model_trained_on_reduced_data | Trupthi | 2025-06-16T12:21:08Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-16T10:24:00Z | ---
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: cnn_news_summary_model_trained_on_reduced_data
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cnn_news_summary_model_trained_on_reduced_data
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:|
| No log | 1.0 | 144 | 1.9070 | 0.2375 | 0.0951 | 0.194 | 0.194 | 20.0 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
aieng-lab/gpt2-medium_story-points | aieng-lab | 2025-06-16T12:19:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"en",
"base_model:openai-community/gpt2-medium",
"base_model:finetune:openai-community/gpt2-medium",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-16T12:19:35Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- gpt2-medium
pipeline_tag: text-classification
---
# GPT-2 medium for estimating story points
This model estimates story points as a numerical value.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [gpt2-medium](https://huggingface.co/gpt2-medium)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
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