modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mrp/SCT_Distillation_BERT_Base | 2023-10-20T09:59:21.000Z | [
"sentence-transformers",
"pytorch",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | sentence-similarity | mrp | null | null | mrp/SCT_Distillation_BERT_Base | 0 | 2 | sentence-transformers | 2023-09-13T07:45:57 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- en
---
This is a [SCT](https://github.com/mrpeerat/SCT) model: It maps sentences to a dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [SCT](https://github.com/mrpeerat/SCT) installed:
```
pip install -U git+https://github.com/mrpeerat/SCT
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('mrp/SCT_Distillation_BERT_Base')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/mrpeerat/SCT#main-results---sts)
## Citing & Authors
```bibtex
@article{limkonchotiwat-etal-2023-sct,
title = "An Efficient Self-Supervised Cross-View Training For Sentence Embedding",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
journal = "Transactions of the Association for Computational Linguistics",
year = "2023",
address = "Cambridge, MA",
publisher = "MIT Press",
}
``` | 1,500 | [
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SoyGema/english-georgian | 2023-09-13T16:37:41.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"translation",
"en",
"ka",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | translation | SoyGema | null | null | SoyGema/english-georgian | 0 | 2 | transformers | 2023-09-13T08:16:46 | ---
language:
- en
- ka
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: english-georgian
results:
- task:
name: Translation
type: translation
dataset:
name: opus100 en-ka
type: opus100
config: en-ka
split: validation
args: en-ka
metrics:
- name: Bleu
type: bleu
value: 44.7969
pipeline_tag: translation
---
<!-- 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. -->
# english-georgian
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 en-ka dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2662
- Bleu: 44.7969
- Gen Len: 22.625
## 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-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3 | 1,469 | [
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sahithya20/t5-small-people | 2023-09-13T10:21:56.000Z | [
"transformers",
"pytorch",
"t5",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | question-answering | sahithya20 | null | null | sahithya20/t5-small-people | 0 | 2 | transformers | 2023-09-13T08:46:49 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-people
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. -->
# t5-small-people
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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andreipb/roberta-poetry-happiness-crpo | 2023-09-13T15:32:31.000Z | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | andreipb | null | null | andreipb/roberta-poetry-happiness-crpo | 0 | 2 | transformers | 2023-09-13T09:31:42 | ---
license: mit
language:
- en
pipeline_tag: fill-mask
library_name: transformers
widget:
- text: "This morning, the CEO was <mask>."
example_title: "Example 1"
- text: "Yesterday, all the students were <mask> in the park."
example_title: "Example 2"
- text: "All the children seemed <mask>."
example_title: "Example 3"
- text: "I opened the door and found a <mask> behind it."
example_title: "Example 4"
- text: "We went to see the <mask> movie."
example_title: "Example 5"
---
# roberta-poetry-happiness-crpo
This model is based on the [RoBERTa base model](https://huggingface.co/roberta-base) (125 M parameters)
fine-tuned for 20 epochs on a poetry dataset of 51 MB (373k lines, 7.8M words). This dataset was extracted from
the [Gutenberg Poetry Corpus](https://github.com/aparrish/gutenberg-poetry-corpus) using an automatic classifier for **happiness**.
The model replaces a masked word, indicated by the `<mask>` tag, with a word associated with **happiness**, while preserving fluency.
Caution: the emotion (here, **happiness**) only biases the choice of words with respect to the base model, but do not expect to find
only words strongly associated to this emotion.
This model was trained by [Teo Ferrari](https://www.linkedin.com/in/teo-ferrari-0a4009176/)
as part of his Bachelor thesis at [HEIG-VD](https://gaps.heig-vd.ch/public/diplome/rapports.php?id=6763),
supervised by [Andrei Popescu-Belis](http://iict-space.heig-vd.ch/apu/).
The model is described in "[GPoeT: a Language Model Trained for Rhyme Generation on Synthetic Data](https://aclanthology.org/2023.latechclfl-1.2/)"
and is used in the [CR-PO](https://github.com/heig-iict-ida/crpo) system for [interactive poem generation](https://aclanthology.org/2022.lrec-1.377),
along with several other models for specific topics or emotions.
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phnghiapro/distilbert-base-uncased-fineturned-clinc | 2023-09-14T10:40:49.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | phnghiapro | null | null | phnghiapro/distilbert-base-uncased-fineturned-clinc | 0 | 2 | transformers | 2023-09-13T09:33:20 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-fineturned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9383870967741935
---
<!-- 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. -->
# distilbert-base-uncased-fineturned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0292
- Accuracy: 0.9384
## 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: 0.0004
- train_batch_size: 1280
- eval_batch_size: 1280
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0092 | 1.0 | 12 | 0.6032 | 0.4881 |
| 0.5561 | 2.0 | 24 | 0.2063 | 0.7877 |
| 0.2481 | 3.0 | 36 | 0.0843 | 0.8977 |
| 0.1194 | 4.0 | 48 | 0.0525 | 0.9223 |
| 0.0563 | 5.0 | 60 | 0.0398 | 0.9326 |
| 0.0474 | 6.0 | 72 | 0.0351 | 0.9365 |
| 0.0423 | 7.0 | 84 | 0.0318 | 0.9358 |
| 0.0397 | 8.0 | 96 | 0.0306 | 0.9377 |
| 0.0378 | 9.0 | 108 | 0.0297 | 0.9381 |
| 0.0359 | 10.0 | 120 | 0.0292 | 0.9384 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
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] |
AIDC-ai-business/Marcoroni-70B | 2023-09-19T08:41:55.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | text-generation | AIDC-ai-business | null | null | AIDC-ai-business/Marcoroni-70B | 21 | 2 | transformers | 2023-09-13T10:50:11 | ---
license: cc-by-nc-4.0
datasets:
- Open-Orca/OpenOrca
language:
- en
pipeline_tag: text-generation
---
# Marcoroni-70B
# Model Details
* **Trained by**: trained by AIDC AI-Business.
* **Model type:** **Marcoroni-70B** is an auto-regressive language model based on the Llama 2 transformer architecture.
* **Language(s)**: English
* **License for Marcoroni-70B base weights**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))
# Prompting
## Prompt Template for alpaca style
```
### Instruction:
<prompt> (without the <>)
### Response:
```
## Example
User:
Give me a brief introduction to Hangzhou and Aliexpress.
Response:
Hangzhou is the capital of Zhejiang Province in China, known for its picturesque West Lake, historic monuments, and thriving technology industries. It is a popular tourist destination due to its natural beauty, historical significance, and strong presence of various technology companies.
Alibaba Group, a multinational technology conglomerate founded in 1999 by Jack Ma, has its headquarters in Hangzhou. One of Alibaba's major businesses is AliExpress, an international online marketplace. It connects buyers from all over the world with sellers mainly from China, offering a wide variety of products at affordable prices. Launched in 2010, AliExpress facilitates small businesses to reach a global audience, and provides buyers with access to a large selection of items, including electronics, clothing, beauty products, and home goods. Its platform supports multiple languages and currencies, making it easier for customers to navigate and shop across the globe.
### Our Other Projects:
* [AIDC-ai-business/Marcoroni-7B](https://huggingface.co/AIDC-ai-business/Marcoroni-7B)
* [AIDC-ai-business/Marcoroni-13B](https://huggingface.co/AIDC-ai-business/Marcoroni-13B)
We achieved the top ranker among 70B models at Sep-14th 2023.
# Evulation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard))
| Metric | Value |
|-----------------------|-------|
| Avg. | 73.94 |
| ARC (25-shot) | 72.95 |
| HellaSwag (10-shot) | 87.51 |
| MMLU (5-shot) | 70.79 |
| TruthfulQA (0-shot) | 64.49 | | 2,300 | [
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kensvin/emotion_classification | 2023-09-16T14:18:33.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | kensvin | null | null | kensvin/emotion_classification | 0 | 2 | transformers | 2023-09-13T12:02:04 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.60625
---
<!-- 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. -->
# emotion_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2024
- Accuracy: 0.6062
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 1.3600 | 0.4938 |
| No log | 2.0 | 20 | 1.2908 | 0.4938 |
| No log | 3.0 | 30 | 1.2799 | 0.5 |
| No log | 4.0 | 40 | 1.2110 | 0.5312 |
| No log | 5.0 | 50 | 1.2178 | 0.5188 |
| No log | 6.0 | 60 | 1.2189 | 0.5188 |
| No log | 7.0 | 70 | 1.2566 | 0.5375 |
| No log | 8.0 | 80 | 1.1838 | 0.5687 |
| No log | 9.0 | 90 | 1.2730 | 0.55 |
| No log | 10.0 | 100 | 1.2329 | 0.575 |
| No log | 11.0 | 110 | 1.2224 | 0.5563 |
| No log | 12.0 | 120 | 1.2729 | 0.5563 |
| No log | 13.0 | 130 | 1.2678 | 0.5687 |
| No log | 14.0 | 140 | 1.2423 | 0.5687 |
| No log | 15.0 | 150 | 1.1704 | 0.6312 |
| No log | 16.0 | 160 | 1.2925 | 0.5625 |
| No log | 17.0 | 170 | 1.3557 | 0.5312 |
| No log | 18.0 | 180 | 1.2951 | 0.5687 |
| No log | 19.0 | 190 | 1.2594 | 0.5625 |
| No log | 20.0 | 200 | 1.2463 | 0.5687 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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margaretshark/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-13T13:06:01.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | margaretshark | null | null | margaretshark/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-13T13:05:21 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 615.50 +/- 211.89
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga margaretshark -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga margaretshark -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga margaretshark
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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LuisCarlosJP/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-13T14:12:57.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | LuisCarlosJP | null | null | LuisCarlosJP/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-13T14:06:46 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 496.50 +/- 176.11
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga LuisCarlosJP -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga LuisCarlosJP -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga LuisCarlosJP
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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] |
Siddharth63/bioul2-mini-nl8 | 2023-11-01T09:54:13.000Z | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"dataset:Siddharth63/biological_dataset",
"arxiv:1910.10683",
"license:artistic-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Siddharth63 | null | null | Siddharth63/bioul2-mini-nl8 | 0 | 2 | transformers | 2023-09-13T14:10:15 | ---
license: artistic-2.0
datasets:
- Siddharth63/biological_dataset
---
# Bioul2-mini-nl8
Pretrained T5 model on Biological dataset using a UL2 (Mixture-of-Denoisers) objective. T5 model was introduced in this paper and first released at this page. The UL2 objective was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released on [this page](https://github.com/google-research/text-to-text-transfer-transformer).
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on a specific downstream task to be useful in practice.
## Model description
T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format.
BioT5 is a transformers model pretrained on a very large corpus of biological data (25 million abstracts) in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and outputs from those texts.
This model used the T5 v1.1 improvements compared to the original T5 model during the pretraining:
GEGLU activation in feed-forward hidden layer, rather than ReLU - see here
Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning
Pretrained on self-supervised objective only without mixing in the downstream tasks
No parameter sharing between embedding and classifier layer
This model also used the "efficient" T5 architecture findings presented in this paper. In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count. To be more precise, model depth is defined as the number of transformer blocks that are stacked sequentially.
This model uses the t5-efficient-mini-nl8 architecture's layer depth which means both the encoder and the decoder have 8 transformer layers compared to the original T5 "mini" model's architecture of 4 transformer layers.
In total, this model has 72 million parameters.
## UL2 pretraining objective
This model was pretrained with the UL2's Mixture-of-Denoisers (MoD) objective, that combines diverse pre-training paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers that samples from a varied set of such objectives, each with different configurations. UL2 is trained using a mixture of three denoising tasks: (1) R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective; (2) X-denoising (or extreme span corruption); and (3) S-denoising (or sequential PrefixLM). During pre-training, we sample from the available denoising tasks based on user-specified ratios.
UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training denoising task. During the pretraining, a paradigm token is inserted to the input ([NLU] for R-denoising, [NLG] for X-denoising, or [S2S] for S-denoising) indicating the denoising task at hand. Then, during fine-tuning the same input token should be inserted to get the best performance for different downstream fine-tuning tasks.
Intended uses & limitations
This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. Note: You most likely need to fine-tune these T5/UL2 models without mixed precision so fine-tune them with full fp32 precision. You can also find more fine-tuning tips from here, for example.
Note: For fine-tuning, most likely you can get better results if you insert a prefix token of [NLU], [NLG], or [S2S] to your input texts. For general language understanding fine-tuning tasks, you could use the [NLU] token. For GPT-style causal language generation, you could use the [S2S] token. The token [NLG] of the X-denoising pretrain task is somewhat mix between the language understanding and causal language generation so the token [NLG] could maybe be used for language generation fine-tuning too.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the [Google TPU Research Cloud](https://sites.research.google/trc/about/). Thanks to the [Finnish-NLP](https://huggingface.co/Finnish-NLP) authors for releasing their code for the UL2 objective, associated task definitions and their guidance. Thanks to [Yeb Havinga](https://huggingface.co/yhavinga) for helping me get started with the t5x framework.
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ma3q1h/hubert-rinnna-jp-jdrtsp-fw07sp-12 | 2023-09-13T16:50:11.000Z | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | ma3q1h | null | null | ma3q1h/hubert-rinnna-jp-jdrtsp-fw07sp-12 | 0 | 2 | transformers | 2023-09-13T14:24:07 | ---
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-rinnna-jp-jdrtsp-fw07sp-12
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. -->
# hubert-rinnna-jp-jdrtsp-fw07sp-12
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1989
- Wer: 0.6801
- Cer: 0.5794
## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.0318 | 1.0 | 404 | 4.2999 | 0.9798 | 0.9889 |
| 3.5113 | 2.0 | 808 | 3.3289 | 0.9798 | 0.9889 |
| 2.7536 | 3.0 | 1212 | 2.7007 | 0.9798 | 0.9889 |
| 2.4826 | 4.0 | 1616 | 2.3732 | 0.9798 | 0.9889 |
| 2.0642 | 5.0 | 2020 | 1.9165 | 0.9798 | 0.9888 |
| 1.834 | 6.0 | 2424 | 1.6739 | 0.9504 | 0.9464 |
| 1.6869 | 7.0 | 2828 | 1.4651 | 0.8239 | 0.7865 |
| 1.5734 | 8.0 | 3232 | 1.3267 | 0.7440 | 0.6939 |
| 1.5052 | 9.0 | 3636 | 1.2331 | 0.7045 | 0.6231 |
| 1.4573 | 10.0 | 4040 | 1.1989 | 0.6801 | 0.5794 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Siddharth63/bioul2-tiny-nl6 | 2023-11-01T09:50:45.000Z | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"dataset:Siddharth63/biological_dataset",
"arxiv:1910.10683",
"license:artistic-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Siddharth63 | null | null | Siddharth63/bioul2-tiny-nl6 | 0 | 2 | transformers | 2023-09-13T14:25:06 | ---
datasets:
- Siddharth63/biological_dataset
license: artistic-2.0
---
# Bioul2-tiny-nl6
Pretrained T5 model on Biological dataset using a UL2 (Mixture-of-Denoisers) objective. T5 model was introduced in this paper and first released at this page. The UL2 objective was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released on [this page](https://github.com/google-research/text-to-text-transfer-transformer).
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on a specific downstream task to be useful in practice.
## Model description
T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format.
BioT5 is a transformers model pretrained on a very large corpus of biological data (25 million abstracts) in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and outputs from those texts.
This model used the T5 v1.1 improvements compared to the original T5 model during the pretraining:
GEGLU activation in feed-forward hidden layer, rather than ReLU - see here
Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning
Pretrained on self-supervised objective only without mixing in the downstream tasks
No parameter sharing between embedding and classifier layer
This model also used the "efficient" T5 architecture findings presented in this paper. In a nutshell, the paper indicates that a Deep-Narrow model architecture is favorable for downstream performance compared to other model architectures of similar parameter count. To be more precise, model depth is defined as the number of transformer blocks that are stacked sequentially.
This model uses the t5-efficient-tiny-nl6 architecture's layer depth which means both the encoder and the decoder have 6 transformer layers compared to the original T5 "tiny" model's architecture of 4 transformer layers.
In total, this model has 31 million parameters.
## UL2 pretraining objective
This model was pretrained with the UL2's Mixture-of-Denoisers (MoD) objective, that combines diverse pre-training paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers that samples from a varied set of such objectives, each with different configurations. UL2 is trained using a mixture of three denoising tasks: (1) R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective; (2) X-denoising (or extreme span corruption); and (3) S-denoising (or sequential PrefixLM). During pre-training, we sample from the available denoising tasks based on user-specified ratios.
UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training denoising task. During the pretraining, a paradigm token is inserted to the input ([NLU] for R-denoising, [NLG] for X-denoising, or [S2S] for S-denoising) indicating the denoising task at hand. Then, during fine-tuning the same input token should be inserted to get the best performance for different downstream fine-tuning tasks.
Intended uses & limitations
This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. Note: You most likely need to fine-tune these T5/UL2 models without mixed precision so fine-tune them with full fp32 precision. You can also find more fine-tuning tips from here, for example.
Note: For fine-tuning, most likely you can get better results if you insert a prefix token of [NLU], [NLG], or [S2S] to your input texts. For general language understanding fine-tuning tasks, you could use the [NLU] token. For GPT-style causal language generation, you could use the [S2S] token. The token [NLG] of the X-denoising pretrain task is somewhat mix between the language understanding and causal language generation so the token [NLG] could maybe be used for language generation fine-tuning too.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the [Google TPU Research Cloud](https://sites.research.google/trc/about/). Thanks to the [Finnish-NLP](https://huggingface.co/Finnish-NLP) authors for releasing their code for the UL2 objective, associated task definitions and their guidance. Thanks to [Yeb Havinga](https://huggingface.co/yhavinga) for helping me get started with the t5x framework.
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minhbtc/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-14T05:46:16.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | minhbtc | null | null | minhbtc/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-13T14:58:36 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 562.50 +/- 146.60
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga minhbtc -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga minhbtc -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga minhbtc
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.2),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.00015),
('learning_starts', 100000),
('n_timesteps', 3000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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Sachin16/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-13T16:00:45.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Sachin16 | null | null | Sachin16/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-13T16:00:15 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 257.00 +/- 38.81
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga Sachin16 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga Sachin16 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Sachin16
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ma3q1h/hubert-rinnna-jp-jdrtsp-fw07sp-13 | 2023-09-13T22:19:45.000Z | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | ma3q1h | null | null | ma3q1h/hubert-rinnna-jp-jdrtsp-fw07sp-13 | 0 | 2 | transformers | 2023-09-13T17:29:06 | ---
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-rinnna-jp-jdrtsp-fw07sp-13
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. -->
# hubert-rinnna-jp-jdrtsp-fw07sp-13
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1606
- Wer: 0.3004
- Cer: 0.1786
## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.352 | 1.0 | 404 | 0.9913 | 0.6021 | 0.4479 |
| 0.9044 | 2.0 | 808 | 0.5053 | 0.4261 | 0.2774 |
| 0.9001 | 3.0 | 1212 | 0.8458 | 0.4848 | 0.3267 |
| 0.8425 | 4.0 | 1616 | 0.5311 | 0.4577 | 0.3053 |
| 0.8408 | 5.0 | 2020 | 0.4328 | 0.4075 | 0.2776 |
| 0.7759 | 6.0 | 2424 | 0.4736 | 0.4394 | 0.3363 |
| 0.7228 | 7.0 | 2828 | 0.4667 | 0.4173 | 0.2862 |
| 0.6755 | 8.0 | 3232 | 0.4190 | 0.4114 | 0.2611 |
| 0.634 | 9.0 | 3636 | 0.4252 | 0.3993 | 0.2612 |
| 0.6267 | 10.0 | 4040 | 0.3275 | 0.3734 | 0.2362 |
| 0.6199 | 11.0 | 4444 | 0.2786 | 0.3543 | 0.2222 |
| 0.5396 | 12.0 | 4848 | 0.2851 | 0.3501 | 0.2146 |
| 0.5343 | 13.0 | 5252 | 0.2527 | 0.3448 | 0.2106 |
| 0.5488 | 14.0 | 5656 | 0.2725 | 0.3431 | 0.2100 |
| 0.4606 | 15.0 | 6060 | 0.2293 | 0.3259 | 0.1962 |
| 0.4229 | 16.0 | 6464 | 0.2043 | 0.3172 | 0.1914 |
| 0.4078 | 17.0 | 6868 | 0.1891 | 0.3128 | 0.1862 |
| 0.4017 | 18.0 | 7272 | 0.1785 | 0.3075 | 0.1833 |
| 0.3618 | 19.0 | 7676 | 0.1673 | 0.3035 | 0.1803 |
| 0.3739 | 20.0 | 8080 | 0.1606 | 0.3004 | 0.1786 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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winglian/phi-1_5-2x-fib | 2023-09-13T20:05:39.000Z | [
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"custom_code",
"region:us"
] | text-generation | winglian | null | null | winglian/phi-1_5-2x-fib | 2 | 2 | transformers | 2023-09-13T19:15:22 | # 2.6B Phi
This model was created by duplicating hidden layers from Microsoft's Phi 1.5.
Join us on the OpenAccess AI Collective Discord: https://discord.gg/jb763J4Q | 168 | [
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phnghiapro/distilbert-base-uncased-distilled-clinc | 2023-09-14T10:43:25.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | phnghiapro | null | null | phnghiapro/distilbert-base-uncased-distilled-clinc | 0 | 2 | transformers | 2023-09-14T02:59:03 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9490322580645161
---
<!-- 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. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1852
- Accuracy: 0.9490
## 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: 0.0004
- train_batch_size: 1280
- eval_batch_size: 1280
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9692 | 1.0 | 12 | 1.3486 | 0.6574 |
| 1.1867 | 2.0 | 24 | 0.5409 | 0.8884 |
| 0.5614 | 3.0 | 36 | 0.2845 | 0.9387 |
| 0.295 | 4.0 | 48 | 0.2234 | 0.9471 |
| 0.1729 | 5.0 | 60 | 0.2021 | 0.9487 |
| 0.1574 | 6.0 | 72 | 0.1942 | 0.9513 |
| 0.1477 | 7.0 | 84 | 0.1895 | 0.9510 |
| 0.1446 | 8.0 | 96 | 0.1870 | 0.9497 |
| 0.1405 | 9.0 | 108 | 0.1856 | 0.9494 |
| 0.1382 | 10.0 | 120 | 0.1852 | 0.9490 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
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] |
guydebruyn/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-14T03:31:42.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | guydebruyn | null | null | guydebruyn/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-14T03:31:03 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 616.00 +/- 136.58
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga guydebruyn -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga guydebruyn -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga guydebruyn
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ronit33/xlm-roberta-base-finetuned-panx-de | 2023-09-14T05:28:03.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | ronit33 | null | null | ronit33/xlm-roberta-base-finetuned-panx-de | 0 | 2 | transformers | 2023-09-14T05:21:01 | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8606487530534567
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1409
- F1: 0.8606
## 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-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2572 | 1.0 | 525 | 0.1538 | 0.8187 |
| 0.1233 | 2.0 | 1050 | 0.1475 | 0.8492 |
| 0.0796 | 3.0 | 1575 | 0.1409 | 0.8606 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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dini-r-a/emotion_classification | 2023-09-17T15:01:58.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | dini-r-a | null | null | dini-r-a/emotion_classification | 0 | 2 | transformers | 2023-09-14T05:43:05 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: FastJobs--Visual_Emotional_Analysis
split: train[:-1]
args: FastJobs--Visual_Emotional_Analysis
metrics:
- name: Accuracy
type: accuracy
value: 0.5625
---
<!-- 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. -->
# emotion_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6256
- Accuracy: 0.5625
## 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: 0.00025
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 1.7794 | 0.4875 |
| No log | 2.0 | 20 | 1.6813 | 0.4938 |
| 0.2276 | 3.0 | 30 | 1.7602 | 0.4875 |
| 0.2276 | 4.0 | 40 | 1.9172 | 0.4562 |
| 0.2048 | 5.0 | 50 | 1.9316 | 0.4625 |
| 0.2048 | 6.0 | 60 | 1.8285 | 0.5 |
| 0.2048 | 7.0 | 70 | 1.6341 | 0.5687 |
| 0.1617 | 8.0 | 80 | 1.7461 | 0.5375 |
| 0.1617 | 9.0 | 90 | 1.6544 | 0.5312 |
| 0.1766 | 10.0 | 100 | 1.9449 | 0.4875 |
| 0.1766 | 11.0 | 110 | 1.7565 | 0.5125 |
| 0.1766 | 12.0 | 120 | 1.8936 | 0.5 |
| 0.1979 | 13.0 | 130 | 1.6812 | 0.5687 |
| 0.1979 | 14.0 | 140 | 1.7619 | 0.5188 |
| 0.1694 | 15.0 | 150 | 1.6903 | 0.55 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
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AbstractQbit/electra_large_imdb_regression_htsplice | 2023-09-14T09:02:13.000Z | [
"transformers",
"pytorch",
"electra",
"text-classification",
"arxiv:1905.05583",
"endpoints_compatible",
"has_space",
"region:us"
] | text-classification | AbstractQbit | null | null | AbstractQbit/electra_large_imdb_regression_htsplice | 0 | 2 | transformers | 2023-09-14T08:45:42 | `google/electra-large-discriminator` finetuned for regression on imdb dataset ratings for 3 epoches.
Large examples tokenized with head and tail parts of a review, as described in [How to Fine-Tune BERT for Text Classification?](https://arxiv.org/abs/1905.05583)
```python
def preprocess_function(example):
tokens = tokenizer(example["text"], truncation=False)
if len(tokens['input_ids']) > 512:
tokens['input_ids'] = tokens['input_ids'][:129] + \
[102] + tokens['input_ids'][-382:]
tokens['token_type_ids'] = [0]*512
tokens['attention_mask'] = [1]*512
return tokens
``` | 620 | [
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nnisbett/cc-narratives_robertamodel2 | 2023-09-14T11:12:52.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | nnisbett | null | null | nnisbett/cc-narratives_robertamodel2 | 0 | 2 | transformers | 2023-09-14T08:57:58 | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: cc_narratives_robertamodel2
results: []
widget:
- text: "I believe net zero target lacks legitimacy and without a referendum the current climate change policy lacks the explicit consent of the people."
- text: "Solar panels installed within new homes would be far cheaper than retro fitting after construction.This would help on home running costs and assist in our climate policy pledge on carbon emission"
- text: "It is environmentally irresponsible to allow garden space occupied by grass and other plant life (which processes CO2 and supports wildlife) to be replaced by plastic which does not biodegrade"
---
<!-- 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. -->
# base_model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9156
- F1: 0.7112
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0269 | 1.0 | 22 | 0.9767 | 0.3192 |
| 0.9372 | 2.0 | 44 | 0.9233 | 0.4689 |
| 0.7988 | 3.0 | 66 | 0.8628 | 0.5678 |
| 0.6139 | 4.0 | 88 | 0.8515 | 0.6001 |
| 0.4226 | 5.0 | 110 | 0.9094 | 0.6003 |
| 0.2551 | 6.0 | 132 | 1.0029 | 0.6192 |
| 0.1439 | 7.0 | 154 | 1.0345 | 0.6581 |
| 0.0872 | 8.0 | 176 | 1.1825 | 0.6431 |
| 0.0702 | 9.0 | 198 | 1.2059 | 0.6468 |
| 0.0497 | 10.0 | 220 | 1.2089 | 0.6403 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
cappuch/altcbert | 2023-09-14T09:16:35.000Z | [
"keras",
"text-classification",
"en",
"dataset:imdb",
"license:apache-2.0",
"region:us"
] | text-classification | cappuch | null | null | cappuch/altcbert | 0 | 2 | keras | 2023-09-14T09:11:51 | ---
license: apache-2.0
language:
- en
pipeline_tag: text-classification
datasets:
- imdb
---
# A Lite Text Classification Bidirectional Encoder Representations from Transformers
Trained on IMDB reviews, and is binary.
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TheBloke/Llama-2-13B-LoRA-Assemble-GGUF | 2023-09-27T12:49:12.000Z | [
"transformers",
"llama",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/Llama-2-13B-LoRA-Assemble-GGUF | 3 | 2 | transformers | 2023-09-14T10:24:22 | ---
license: llama2
model_name: Llama 2 13B LoRA Assemble
base_model: oh-yeontaek/llama-2-13b-LoRA-assemble
inference: false
model_creator: oh-yeontaek
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama 2 13B LoRA Assemble - GGUF
- Model creator: [oh-yeontaek](https://huggingface.co/oh-yeontaek)
- Original model: [Llama 2 13B LoRA Assemble](https://huggingface.co/oh-yeontaek/llama-2-13b-LoRA-assemble)
<!-- description start -->
## Description
This repo contains GGUF format model files for [oh-yeontaek's Llama 2 13B LoRA Assemble](https://huggingface.co/oh-yeontaek/llama-2-13b-LoRA-assemble).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF)
* [oh-yeontaek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/oh-yeontaek/llama-2-13b-LoRA-assemble)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-2-13b-lora-assemble.Q2_K.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-2-13b-lora-assemble.Q3_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [llama-2-13b-lora-assemble.Q3_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [llama-2-13b-lora-assemble.Q3_K_L.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [llama-2-13b-lora-assemble.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-2-13b-lora-assemble.Q4_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [llama-2-13b-lora-assemble.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [llama-2-13b-lora-assemble.Q5_0.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-2-13b-lora-assemble.Q5_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [llama-2-13b-lora-assemble.Q5_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [llama-2-13b-lora-assemble.Q6_K.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [llama-2-13b-lora-assemble.Q8_0.gguf](https://huggingface.co/TheBloke/Llama-2-13B-LoRA-Assemble-GGUF/blob/main/llama-2-13b-lora-assemble.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Llama-2-13B-LoRA-Assemble-GGUF and below it, a specific filename to download, such as: llama-2-13b-lora-assemble.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Llama-2-13B-LoRA-Assemble-GGUF llama-2-13b-lora-assemble.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Llama-2-13B-LoRA-Assemble-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Llama-2-13B-LoRA-Assemble-GGUF llama-2-13b-lora-assemble.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m llama-2-13b-lora-assemble.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-13B-LoRA-Assemble-GGUF", model_file="llama-2-13b-lora-assemble.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: oh-yeontaek's Llama 2 13B LoRA Assemble
No original model card was available.
<!-- original-model-card end -->
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] |
hananeChab/english_darija | 2023-09-14T15:39:08.000Z | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | hananeChab | null | null | hananeChab/english_darija | 0 | 2 | transformers | 2023-09-14T11:39:35 | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-ar
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: english_darija
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# english_darija
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0719
- Bleu: 9.6191
- Gen Len: 13.542
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 4.1808 | 1.0 | 732 | 2.9129 | 3.2505 | 15.1565 |
| 3.0353 | 2.0 | 1464 | 2.4851 | 6.6089 | 13.4928 |
| 2.1867 | 3.0 | 2196 | 2.2985 | 7.5619 | 13.5455 |
| 2.0138 | 4.0 | 2928 | 2.1924 | 8.0542 | 13.6172 |
| 1.6726 | 5.0 | 3660 | 2.1360 | 9.3411 | 13.337 |
| 1.5354 | 6.0 | 4392 | 2.1047 | 9.363 | 13.4962 |
| 1.392 | 7.0 | 5124 | 2.0912 | 9.9693 | 13.432 |
| 1.2942 | 8.0 | 5856 | 2.0768 | 9.7611 | 13.4833 |
| 1.2139 | 9.0 | 6588 | 2.0732 | 9.6065 | 13.5666 |
| 1.1894 | 10.0 | 7320 | 2.0719 | 9.6191 | 13.542 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
kaitchup/OPT-350M-RM-DSChat | 2023-10-19T13:12:37.000Z | [
"transformers",
"pytorch",
"safetensors",
"opt",
"text-generation",
"en",
"dataset:Dahoas/rm-static",
"dataset:Dahoas/synthetic-instruct-gptj-pairwise",
"dataset:Anthropic/hh-rlhf",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | kaitchup | null | null | kaitchup/OPT-350M-RM-DSChat | 0 | 2 | transformers | 2023-09-14T15:09:05 | ---
license: cc-by-nc-sa-4.0
datasets:
- Dahoas/rm-static
- Dahoas/synthetic-instruct-gptj-pairwise
- Anthropic/hh-rlhf
language:
- en
---
# Model Card for Model ID
This a model is a reward model for RLHF fine-tuned using DeepSpeed Chat.
It is based on OPT-350M.
## Model Details
### Model Description
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **Model type:** Reward model
- **Language(s) (NLP):** English
- **License:** cc-by-nc-sa-4.0
- **Finetuned from model:** [facebook/opt-350m](https://huggingface.co/facebook/opt-350m)
### Model Sources
The model has been trained with the procedure described in this article:
[Train Instruct LLMs On Your GPU with DeepSpeed Chat — Step #2: Training a Reward Model](https://kaitchup.substack.com/p/train-instruct-llms-on-your-gpu-with-1e1) | 815 | [
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] |
agonh/phi-1_5 | 2023-09-15T06:31:09.000Z | [
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"microsoft",
"phi",
"custom_code",
"en",
"license:other",
"region:us"
] | text-generation | agonh | null | null | agonh/phi-1_5 | 3 | 2 | transformers | 2023-09-14T15:23:30 | ---
license: other
language:
- en
pipeline_tag: text-generation
model_creator: microsoft
model_link: https://huggingface.co/microsoft/phi-1_5
model_name: phi-1_5
edited_by: agonh
tags:
- microsoft
- phi
---
# Phi-1_5
- Model creator: [Microsoft](https://huggingface.co/microsoft)
- Original model: [phi-1_5](https://huggingface.co/microsoft/phi-1_5)
## Description
This repo contains files for [microsoft's phi-1_5](https://huggingface.co/microsoft/phi-1_5).
### License
The model is licensed under the "Research License" | 525 | [
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loupzeur/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-14T17:01:42.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | loupzeur | null | null | loupzeur/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-14T15:35:36 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 599.50 +/- 239.78
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga loupzeur -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga loupzeur -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga loupzeur
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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softcatala/paraphrase-ca | 2023-09-15T04:40:32.000Z | [
"transformers",
"pytorch",
"ca",
"license:apache-2.0",
"region:us"
] | null | softcatala | null | null | softcatala/paraphrase-ca | 0 | 2 | transformers | 2023-09-14T15:54:25 | ---
language:
- ca
license: apache-2.0
inference: false
---
## Model description
This is a model based on [Google's MT5](https://huggingface.co/google/mt5-base) small finetuned for paraphrasing in Catalan language.
Sample output:
Original:
- Aquesta és una associació sense ànim de lucre amb la missió de fomentar la presència i l'ús del català.
Proposals:
- Aquesta és una organització sense ànim de lucre amb la finalitat de promoure la presència i l'ús del català.
- Aquesta és una organització sense ànim de lucre que té com a objectiu promoure la presència i l'ús del català.
## Warnings
This is an experimental model not suited for production environments.
It's shared as it is as the outcome of an initial effort not completed yet.
## Inference
To run inference check the [inference.py](inference.py) file in the repository.
## Additional information
Contact: Jordi Mas <jmas@softcatala.org>
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] |
miteshkotak7/my-awesome-setfit-model | 2023-10-15T21:27:08.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | miteshkotak7 | null | null | miteshkotak7/my-awesome-setfit-model | 0 | 2 | sentence-transformers | 2023-09-14T16:42:36 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# miteshkotak7/my-awesome-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique 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 Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("miteshkotak7/my-awesome-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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] |
fetiska/AItari | 2023-09-14T17:58:34.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | fetiska | null | null | fetiska/AItari | 0 | 2 | stable-baselines3 | 2023-09-14T17:57:58 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 532.50 +/- 65.24
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga fetiska -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga fetiska -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga fetiska
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 900000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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Severus27/BeingWell_llama2_7b | 2023-09-14T21:01:31.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"medical",
"conversational",
"en",
"dataset:shibing624/medical",
"dataset:GBaker/MedQA-USMLE-4-options",
"license:openrail",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | conversational | Severus27 | null | null | Severus27/BeingWell_llama2_7b | 2 | 2 | transformers | 2023-09-14T18:32:43 | ---
license: openrail
datasets:
- shibing624/medical
- GBaker/MedQA-USMLE-4-options
language:
- en
pipeline_tag: conversational
tags:
- medical
arxiv: 2303.14070
---
This model is a fine-tuned model based on the Llama 2_7b architecture. It has been specifically trained on a dataset comprising USMLE (United States Medical Licensing Examination) questions and answers, as well as conversations between doctors and patients. | 426 | [
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DriveMyScream/Facial_Beauty_Classification | 2023-09-14T19:47:47.000Z | [
"keras",
"region:us"
] | null | DriveMyScream | null | null | DriveMyScream/Facial_Beauty_Classification | 0 | 2 | keras | 2023-09-14T19:47:07 | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 840 | [
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] |
FedeBerto/Griffith-Emotion | 2023-09-18T13:03:06.000Z | [
"keras",
"region:us"
] | null | FedeBerto | null | null | FedeBerto/Griffith-Emotion | 0 | 2 | keras | 2023-09-14T20:11:41 | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | AdamW |
| weight_decay | 0.01 |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 4.4932880882697646e-06 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-08 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 842 | [
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serge-wilson/whisper-base-wolof | 2023-09-15T03:01:20.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"multilingual",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | serge-wilson | null | null | serge-wilson/whisper-base-wolof | 0 | 2 | transformers | 2023-09-14T20:42:38 | ---
language:
- multilingual
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Base Wolof
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 Base Wolof
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2902
- Wer: 32.8385
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5632 | 1.14 | 1000 | 0.4672 | 48.8263 |
| 0.3464 | 2.29 | 2000 | 0.3461 | 34.6403 |
| 0.2514 | 3.43 | 3000 | 0.3013 | 32.1406 |
| 0.1957 | 4.57 | 4000 | 0.2902 | 32.8385 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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nikhilwani/question_answering | 2023-09-14T21:01:57.000Z | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | question-answering | nikhilwani | null | null | nikhilwani/question_answering | 0 | 2 | transformers | 2023-09-14T20:53:35 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: question_answering
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. -->
# question_answering
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6922
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 1.6142 |
| 0.9008 | 2.0 | 500 | 1.6113 |
| 0.9008 | 3.0 | 750 | 1.6922 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Mag-al/dqn_SpaceInvaders-v4 | 2023-09-14T22:48:18.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Mag-al | null | null | Mag-al/dqn_SpaceInvaders-v4 | 0 | 2 | stable-baselines3 | 2023-09-14T22:47:45 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 468.00 +/- 150.88
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga Mag-al -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga Mag-al -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Mag-al
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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sakshamkhatwani/reactCodeGenerationModel2 | 2023-09-15T03:57:20.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | sakshamkhatwani | null | null | sakshamkhatwani/reactCodeGenerationModel2 | 0 | 2 | transformers | 2023-09-15T02:52:36 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: reactCodeGenerationModel2
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. -->
# reactCodeGenerationModel2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0699
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2084 | 1.0 | 54 | 1.7980 |
| 1.7205 | 2.0 | 108 | 1.5156 |
| 1.4968 | 3.0 | 162 | 1.3346 |
| 1.3237 | 4.0 | 216 | 1.2177 |
| 1.225 | 5.0 | 270 | 1.1444 |
| 1.1086 | 6.0 | 324 | 1.1011 |
| 1.1496 | 7.0 | 378 | 1.0795 |
| 1.0855 | 8.0 | 432 | 1.0699 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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leonhardhennig/copious_ner | 2023-09-15T12:46:27.000Z | [
"transformers",
"pytorch",
"safetensors",
"TransformerTokenClassificationModel",
"en",
"dataset:copious",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | leonhardhennig | null | null | leonhardhennig/copious_ner | 0 | 2 | transformers | 2023-09-15T07:26:32 | ---
language:
- en
license: "mit"
datasets:
- copious
metrics:
- f1
---
# Model Card for copious_ner
<!-- Provide a quick summary of what the model is/does. [Optional] -->
NER on Copious Biodiversity dataset
# Table of Contents
- [Model Card for copious_ner](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use [Optional]](#downstream-use-optional)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
- [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
- [Testing Data](#testing-data)
- [Factors](#factors)
- [Metrics](#metrics)
- [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
NER on Copious Biodiversity dataset
- **Developed by:** More information needed
- **Shared by [Optional]:** More information needed
- **Model type:** Language model
- **Language(s) (NLP):** en
- **License:** mit
- **Parent Model:** More information needed
- **Resources for more information:** 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. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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 -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
# Training Details
## Training Data
<!-- This should link to a Data 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 on training data 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
More information needed
### Speeds, Sizes, Times
<!-- 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 Data 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
# Model Examination
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 -->
- **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
<!-- 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]
<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
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# Model Card Contact
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# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
More information needed
</details> | 6,253 | [
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] |
gshields/translate_model_error_v0.4 | 2023-10-25T11:51:26.000Z | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | gshields | null | null | gshields/translate_model_error_v0.4 | 0 | 2 | transformers | 2023-09-15T08:47:01 | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-hi
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: translate_model_error_v0.4
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. -->
# translate_model_error_v0.4
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9509
- Bleu: 9.6073
- Gen Len: 10.2667
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 8 | 5.0268 | 9.7039 | 10.5333 |
| No log | 2.0 | 16 | 4.9509 | 9.6073 | 10.2667 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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gshields/translate_model_fixed_v0.4 | 2023-10-25T11:53:31.000Z | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | gshields | null | null | gshields/translate_model_fixed_v0.4 | 0 | 2 | transformers | 2023-09-15T08:47:43 | ---
license: apache-2.0
base_model: gshields/translate_model_error_v0.4
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: translate_model_fixed_v0.4
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. -->
# translate_model_fixed_v0.4
This model is a fine-tuned version of [gshields/translate_model_error_v0.4](https://huggingface.co/gshields/translate_model_error_v0.4) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4079
- Bleu: 17.9043
- Gen Len: 10.9
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 8 | 3.4248 | 20.3396 | 11.0333 |
| No log | 2.0 | 16 | 3.4079 | 17.9043 | 10.9 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Txinplas/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-15T09:00:40.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Txinplas | null | null | Txinplas/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-15T09:00:00 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 730.00 +/- 276.05
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga Txinplas -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga Txinplas -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Txinplas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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DmatryMakeev/ponteleich-v3 | 2023-09-15T10:22:36.000Z | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | DmatryMakeev | null | null | DmatryMakeev/ponteleich-v3 | 0 | 2 | diffusers | 2023-09-15T10:18:19 | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### PONTELEICH_V3 Dreambooth model trained by DmatryMakeev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| 507 | [
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kbbabu/flanT5_grammerly_ft | 2023-09-18T09:36:15.000Z | [
"transformers",
"generated_from_trainer",
"dataset:grammarly/coedit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | kbbabu | null | null | kbbabu/flanT5_grammerly_ft | 0 | 2 | transformers | 2023-09-15T10:57:08 | ---
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
model-index:
- name: coedit-finetuned
results: []
datasets:
- grammarly/coedit
---
<!-- 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. -->
# coedit-finetuned
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an CoEdit 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3 | 1,197 | [
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] |
ernestum/ppo-seals-MountainCar-v0 | 2023-09-18T07:43:55.000Z | [
"stable-baselines3",
"seals/MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/ppo-seals-MountainCar-v0 | 0 | 2 | stable-baselines3 | 2023-09-15T11:50:03 | ---
library_name: stable-baselines3
tags:
- seals/MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/MountainCar-v0
type: seals/MountainCar-v0
metrics:
- type: mean_reward
value: -97.00 +/- 8.26
name: mean_reward
verified: false
---
# **PPO** Agent playing **seals/MountainCar-v0**
This is a trained model of a **PPO** agent playing **seals/MountainCar-v0**
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
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 seals/MountainCar-v0 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-v0 -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 seals/MountainCar-v0 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env seals/MountainCar-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/MountainCar-v0 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('clip_range', 0.2),
('ent_coef', 6.4940755116195606e-06),
('gae_lambda', 0.98),
('gamma', 0.99),
('learning_rate', 0.0004476103728105138),
('max_grad_norm', 1),
('n_envs', 16),
('n_epochs', 20),
('n_steps', 256),
('n_timesteps', 1000000.0),
('normalize',
{'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.Tanh'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
('vf_coef', 0.25988158989488963),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.99,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ernestum/ppo-seals-Ant-v1 | 2023-09-18T07:44:45.000Z | [
"stable-baselines3",
"seals/Ant-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/ppo-seals-Ant-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:50:19 | ---
library_name: stable-baselines3
tags:
- seals/Ant-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Ant-v1
type: seals/Ant-v1
metrics:
- type: mean_reward
value: 2461.22 +/- 674.80
name: mean_reward
verified: false
---
# **PPO** Agent playing **seals/Ant-v1**
This is a trained model of a **PPO** agent playing **seals/Ant-v1**
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
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 seals/Ant-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Ant-v1 -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 seals/Ant-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Ant-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env seals/Ant-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/Ant-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 16),
('clip_range', 0.3),
('ent_coef', 3.1441389214159857e-06),
('gae_lambda', 0.8),
('gamma', 0.995),
('learning_rate', 0.00017959211641976886),
('max_grad_norm', 0.9),
('n_epochs', 10),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize',
{'gamma': 0.995, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.Tanh'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
('vf_coef', 0.4351450387648799),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.995,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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] |
ernestum/ppo-seals-Swimmer-v1 | 2023-09-18T07:45:33.000Z | [
"stable-baselines3",
"seals/Swimmer-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/ppo-seals-Swimmer-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:50:49 | ---
library_name: stable-baselines3
tags:
- seals/Swimmer-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Swimmer-v1
type: seals/Swimmer-v1
metrics:
- type: mean_reward
value: 292.84 +/- 3.69
name: mean_reward
verified: false
---
# **PPO** Agent playing **seals/Swimmer-v1**
This is a trained model of a **PPO** agent playing **seals/Swimmer-v1**
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
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 seals/Swimmer-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Swimmer-v1 -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 seals/Swimmer-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Swimmer-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env seals/Swimmer-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/Swimmer-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 8),
('clip_range', 0.1),
('ent_coef', 5.167107294612664e-08),
('gae_lambda', 0.95),
('gamma', 0.999),
('learning_rate', 0.0001214437022727675),
('max_grad_norm', 2),
('n_epochs', 20),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize',
{'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.Tanh'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
('vf_coef', 0.6162112311062333),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.999,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ernestum/ppo-seals-Hopper-v1 | 2023-09-18T07:46:30.000Z | [
"stable-baselines3",
"seals/Hopper-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/ppo-seals-Hopper-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:51:22 | ---
library_name: stable-baselines3
tags:
- seals/Hopper-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Hopper-v1
type: seals/Hopper-v1
metrics:
- type: mean_reward
value: 203.45 +/- 1.19
name: mean_reward
verified: false
---
# **PPO** Agent playing **seals/Hopper-v1**
This is a trained model of a **PPO** agent playing **seals/Hopper-v1**
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
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 seals/Hopper-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Hopper-v1 -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 seals/Hopper-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Hopper-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env seals/Hopper-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/Hopper-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('clip_range', 0.1),
('ent_coef', 0.0010159833764878474),
('gae_lambda', 0.98),
('gamma', 0.995),
('learning_rate', 0.0003904770450788824),
('max_grad_norm', 0.9),
('n_envs', 1),
('n_epochs', 20),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize',
{'gamma': 0.995, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.ReLU'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
('vf_coef', 0.20315938606555833),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.995,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ernestum/ppo-seals-Walker2d-v1 | 2023-09-18T07:48:56.000Z | [
"stable-baselines3",
"seals/Walker2d-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/ppo-seals-Walker2d-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:51:52 | ---
library_name: stable-baselines3
tags:
- seals/Walker2d-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Walker2d-v1
type: seals/Walker2d-v1
metrics:
- type: mean_reward
value: 2465.56 +/- 272.31
name: mean_reward
verified: false
---
# **PPO** Agent playing **seals/Walker2d-v1**
This is a trained model of a **PPO** agent playing **seals/Walker2d-v1**
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
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 seals/Walker2d-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Walker2d-v1 -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 seals/Walker2d-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Walker2d-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env seals/Walker2d-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/Walker2d-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 8),
('clip_range', 0.4),
('ent_coef', 0.00013057334805552262),
('gae_lambda', 0.92),
('gamma', 0.98),
('learning_rate', 3.791707778339674e-05),
('max_grad_norm', 0.6),
('n_envs', 1),
('n_epochs', 5),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize',
{'gamma': 0.98, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.ReLU'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [256, 256], 'vf': [256, 256]}]}),
('vf_coef', 0.6167177795726859),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.98,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ernestum/ppo-seals-HalfCheetah-v1 | 2023-09-18T07:57:37.000Z | [
"stable-baselines3",
"seals/HalfCheetah-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/ppo-seals-HalfCheetah-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:52:21 | ---
library_name: stable-baselines3
tags:
- seals/HalfCheetah-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/HalfCheetah-v1
type: seals/HalfCheetah-v1
metrics:
- type: mean_reward
value: 1675.76 +/- 45.89
name: mean_reward
verified: false
---
# **PPO** Agent playing **seals/HalfCheetah-v1**
This is a trained model of a **PPO** agent playing **seals/HalfCheetah-v1**
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
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 seals/HalfCheetah-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/HalfCheetah-v1 -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 seals/HalfCheetah-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/HalfCheetah-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env seals/HalfCheetah-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/HalfCheetah-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('clip_range', 0.1),
('ent_coef', 3.794797423594763e-06),
('gae_lambda', 0.95),
('gamma', 0.95),
('learning_rate', 0.0003286871805949382),
('max_grad_norm', 0.8),
('n_envs', 1),
('n_epochs', 5),
('n_steps', 512),
('n_timesteps', 1000000.0),
('normalize',
{'gamma': 0.95, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.Tanh'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
('vf_coef', 0.11483689492120866),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.95,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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] |
ernestum/ppo-Pendulum-v1 | 2023-09-18T07:50:42.000Z | [
"stable-baselines3",
"Pendulum-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/ppo-Pendulum-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:52:43 | ---
library_name: stable-baselines3
tags:
- Pendulum-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pendulum-v1
type: Pendulum-v1
metrics:
- type: mean_reward
value: -189.25 +/- 66.36
name: mean_reward
verified: false
---
# **PPO** Agent playing **Pendulum-v1**
This is a trained model of a **PPO** agent playing **Pendulum-v1**
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
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 Pendulum-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env Pendulum-v1 -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 Pendulum-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env Pendulum-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env Pendulum-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('clip_range', 0.2),
('ent_coef', 0.0),
('gae_lambda', 0.95),
('gamma', 0.9),
('learning_rate', 0.001),
('n_envs', 4),
('n_epochs', 10),
('n_steps', 1024),
('n_timesteps', 100000.0),
('policy', 'MlpPolicy'),
('sde_sample_freq', 4),
('use_sde', True),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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] |
ernestum/sac-seals-Hopper-v1 | 2023-09-18T07:52:51.000Z | [
"stable-baselines3",
"seals/Hopper-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/sac-seals-Hopper-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:53:03 | ---
library_name: stable-baselines3
tags:
- seals/Hopper-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Hopper-v1
type: seals/Hopper-v1
metrics:
- type: mean_reward
value: 2279.30 +/- 124.09
name: mean_reward
verified: false
---
# **SAC** Agent playing **seals/Hopper-v1**
This is a trained model of a **SAC** agent playing **seals/Hopper-v1**
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
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 sac --env seals/Hopper-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Hopper-v1 -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 sac --env seals/Hopper-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Hopper-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env seals/Hopper-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/Hopper-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 100000),
('gamma', 0.98),
('learning_rate', 0.001709807687567946),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'log_std_init': -1.6829391077276037,
'net_arch': [256, 256],
'use_sde': False}),
('tau', 0.08),
('train_freq', 32),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ernestum/sac-seals-HalfCheetah-v1 | 2023-09-18T07:53:35.000Z | [
"stable-baselines3",
"seals/HalfCheetah-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/sac-seals-HalfCheetah-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:53:34 | ---
library_name: stable-baselines3
tags:
- seals/HalfCheetah-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/HalfCheetah-v1
type: seals/HalfCheetah-v1
metrics:
- type: mean_reward
value: 1183.52 +/- 22.65
name: mean_reward
verified: false
---
# **SAC** Agent playing **seals/HalfCheetah-v1**
This is a trained model of a **SAC** agent playing **seals/HalfCheetah-v1**
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
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 sac --env seals/HalfCheetah-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/HalfCheetah-v1 -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 sac --env seals/HalfCheetah-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/HalfCheetah-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env seals/HalfCheetah-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/HalfCheetah-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 2048),
('buffer_size', 100000),
('gamma', 0.95),
('learning_rate', 0.000884624878315995),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'log_std_init': -0.6932709443503001,
'net_arch': [64, 64],
'use_sde': False}),
('tau', 0.01),
('train_freq', 64),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ernestum/sac-seals-Ant-v1 | 2023-09-18T07:54:23.000Z | [
"stable-baselines3",
"seals/Ant-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/sac-seals-Ant-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:54:00 | ---
library_name: stable-baselines3
tags:
- seals/Ant-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Ant-v1
type: seals/Ant-v1
metrics:
- type: mean_reward
value: 1004.15 +/- 26.60
name: mean_reward
verified: false
---
# **SAC** Agent playing **seals/Ant-v1**
This is a trained model of a **SAC** agent playing **seals/Ant-v1**
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
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 sac --env seals/Ant-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Ant-v1 -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 sac --env seals/Ant-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Ant-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env seals/Ant-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/Ant-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('buffer_size', 1000000),
('gamma', 0.98),
('learning_rate', 0.0018514039303149058),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'log_std_init': -2.2692589009754176,
'net_arch': [256, 256],
'use_sde': False}),
('tau', 0.05),
('train_freq', 64),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ernestum/sac-seals-Humanoid-v1 | 2023-09-18T07:55:21.000Z | [
"stable-baselines3",
"seals/Humanoid-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/sac-seals-Humanoid-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:54:37 | ---
library_name: stable-baselines3
tags:
- seals/Humanoid-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Humanoid-v1
type: seals/Humanoid-v1
metrics:
- type: mean_reward
value: 367.48 +/- 59.61
name: mean_reward
verified: false
---
# **SAC** Agent playing **seals/Humanoid-v1**
This is a trained model of a **SAC** agent playing **seals/Humanoid-v1**
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
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 sac --env seals/Humanoid-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Humanoid-v1 -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 sac --env seals/Humanoid-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Humanoid-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env seals/Humanoid-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/Humanoid-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('gamma', 0.98),
('learning_rate', 4.426351861707874e-05),
('learning_starts', 20000),
('n_timesteps', 2000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'log_std_init': -0.1034412732183072,
'net_arch': [400, 300],
'use_sde': False}),
('tau', 0.08),
('train_freq', 8),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ernestum/sac-seals-Swimmer-v1 | 2023-09-18T07:56:09.000Z | [
"stable-baselines3",
"seals/Swimmer-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/sac-seals-Swimmer-v1 | 0 | 2 | stable-baselines3 | 2023-09-15T11:55:26 | ---
library_name: stable-baselines3
tags:
- seals/Swimmer-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Swimmer-v1
type: seals/Swimmer-v1
metrics:
- type: mean_reward
value: 28.90 +/- 1.67
name: mean_reward
verified: false
---
# **SAC** Agent playing **seals/Swimmer-v1**
This is a trained model of a **SAC** agent playing **seals/Swimmer-v1**
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
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 sac --env seals/Swimmer-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Swimmer-v1 -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 sac --env seals/Swimmer-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Swimmer-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env seals/Swimmer-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/Swimmer-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 100000),
('gamma', 0.995),
('learning_rate', 0.00039981805535514633),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'log_std_init': -2.689958330139309,
'net_arch': [400, 300],
'use_sde': False}),
('tau', 0.01),
('train_freq', 256),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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] |
ernestum/ppo-seals-CartPole-v0 | 2023-09-18T07:56:54.000Z | [
"stable-baselines3",
"seals/CartPole-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/ppo-seals-CartPole-v0 | 0 | 2 | stable-baselines3 | 2023-09-15T11:56:08 | ---
library_name: stable-baselines3
tags:
- seals/CartPole-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/CartPole-v0
type: seals/CartPole-v0
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **seals/CartPole-v0**
This is a trained model of a **PPO** agent playing **seals/CartPole-v0**
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
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 seals/CartPole-v0 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/CartPole-v0 -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 seals/CartPole-v0 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/CartPole-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env seals/CartPole-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/CartPole-v0 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 0.4),
('ent_coef', 0.008508727919228772),
('gae_lambda', 0.9),
('gamma', 0.9999),
('learning_rate', 0.0012403278189645594),
('max_grad_norm', 0.8),
('n_envs', 8),
('n_epochs', 10),
('n_steps', 512),
('n_timesteps', 100000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.ReLU'>,
'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
('vf_coef', 0.489343896591493),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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pszemraj/BL-pythia-31m-simpleRW-lite-2048-scratch | 2023-09-19T10:54:58.000Z | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"en",
"dataset:pszemraj/simpleRW-lite",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | pszemraj | null | null | pszemraj/BL-pythia-31m-simpleRW-lite-2048-scratch | 0 | 2 | transformers | 2023-09-15T13:12:34 | ---
base_model: EleutherAI/pythia-31m
tags:
- generated_from_trainer
metrics:
- accuracy
inference:
parameters:
max_new_tokens: 64
do_sample: true
repetition_penalty: 1.1
no_repeat_ngram_size: 5
eta_cutoff: 0.001
widget:
- text: My name is El Microondas the Wise and
example_title: El Microondas
- text: Kennesaw State University is a public
example_title: Kennesaw State University
- text: Bungie Studios is an American video game developer. They are most famous for developing the award winning Halo series of video games. They also made Destiny. The studio was founded
example_title: Bungie
- text: The Mona Lisa is a world-renowned painting created by
example_title: Mona Lisa
- text: >-
The Harry Potter series, written by J.K. Rowling, begins with the book titled
example_title: Harry Potter Series
- text: >-
Question: I have cities, but no houses. I have mountains, but no trees.
I have water, but no fish. What am I?
Answer:
example_title: Riddle
- text: The process of photosynthesis involves the conversion of
example_title: Photosynthesis
- text: >-
Jane went to the store to buy some groceries. She picked up apples, oranges, and a loaf of bread. When she got home, she realized she forgot
example_title: Story Continuation
- text: >-
Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph,
and another train leaves Station B at 10:00 AM and travels at 80 mph,
when will they meet if the distance between the stations is 300 miles?
To determine
example_title: Math Problem
- text: >-
In the context of computer programming, an algorithm is
example_title: Algorithm Definition
pipeline_tag: text-generation
license: apache-2.0
language:
- en
datasets:
- pszemraj/simpleRW-lite
---
# BL-pythia-31m-simpleRW-lite-2048-scratch
This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7136
- Accuracy: 0.2662
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
```
2040 ***** eval metrics *****
2041 epoch = 3.0
2042 eval_accuracy = 0.2668
2043 eval_loss = 4.7076
2044 eval_runtime = 0:00:21.04
2045 eval_samples = 500
2046 eval_samples_per_second = 23.759
2047 eval_steps_per_second = 11.88
2048 perplexity = 110.7897
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 80085
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 7.0159 | 0.13 | 100 | 7.1022 | 0.1180 |
| 6.2257 | 0.27 | 200 | 6.3526 | 0.1508 |
| 5.8611 | 0.4 | 300 | 5.9888 | 0.1735 |
| 5.5514 | 0.54 | 400 | 5.7552 | 0.1855 |
| 5.3824 | 0.67 | 500 | 5.5883 | 0.1948 |
| 5.344 | 0.81 | 600 | 5.4697 | 0.2017 |
| 5.1925 | 0.94 | 700 | 5.3717 | 0.2073 |
| 5.0814 | 1.08 | 800 | 5.2932 | 0.2121 |
| 5.0865 | 1.21 | 900 | 5.2280 | 0.2162 |
| 4.9602 | 1.35 | 1000 | 5.1672 | 0.2207 |
| 4.957 | 1.48 | 1100 | 5.1144 | 0.2247 |
| 4.8489 | 1.62 | 1200 | 5.0617 | 0.2299 |
| 4.79 | 1.75 | 1300 | 5.0122 | 0.2349 |
| 4.8005 | 1.89 | 1400 | 4.9637 | 0.2400 |
| 4.7409 | 2.02 | 1500 | 4.9216 | 0.2448 |
| 4.6674 | 2.16 | 1600 | 4.8815 | 0.2488 |
| 4.6729 | 2.29 | 1700 | 4.8475 | 0.2526 |
| 4.7071 | 2.43 | 1800 | 4.8156 | 0.2555 |
| 4.4937 | 2.56 | 1900 | 4.7841 | 0.2588 |
| 4.5153 | 2.7 | 2000 | 4.7573 | 0.2615 |
| 4.5512 | 2.83 | 2100 | 4.7345 | 0.2637 |
| 4.5153 | 2.96 | 2200 | 4.7136 | 0.2662 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.2.0.dev20230915+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3 | 4,625 | [
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anurag629/swin-tiny-patch4-window7-224-finetuned-eurosat | 2023-09-15T16:28:10.000Z | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | image-classification | anurag629 | null | null | anurag629/swin-tiny-patch4-window7-224-finetuned-eurosat | 1 | 2 | transformers | 2023-09-15T15:58:27 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0133
- Accuracy: 1.0
## 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-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8235 | 1.0 | 13 | 0.6034 | 0.9239 |
| 0.5091 | 2.0 | 26 | 0.1870 | 0.9728 |
| 0.273 | 3.0 | 39 | 0.0895 | 0.9946 |
| 0.1401 | 4.0 | 52 | 0.0543 | 0.9946 |
| 0.0936 | 5.0 | 65 | 0.0484 | 0.9891 |
| 0.091 | 6.0 | 78 | 0.0498 | 0.9891 |
| 0.0603 | 7.0 | 91 | 0.0133 | 1.0 |
| 0.0421 | 8.0 | 104 | 0.0196 | 0.9946 |
| 0.0557 | 9.0 | 117 | 0.0172 | 0.9946 |
| 0.0552 | 10.0 | 130 | 0.0103 | 1.0 |
| 0.045 | 11.0 | 143 | 0.0082 | 1.0 |
| 0.0355 | 12.0 | 156 | 0.0071 | 1.0 |
| 0.0491 | 13.0 | 169 | 0.0087 | 1.0 |
| 0.0384 | 14.0 | 182 | 0.0065 | 1.0 |
| 0.0324 | 15.0 | 195 | 0.0061 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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DriveMyScream/Face_Image_Segementation | 2023-09-15T20:18:08.000Z | [
"keras",
"region:us"
] | null | DriveMyScream | null | null | DriveMyScream/Face_Image_Segementation | 0 | 2 | keras | 2023-09-15T19:26:54 | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 840 | [
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vinben007/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-17T18:28:05.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | vinben007 | null | null | vinben007/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-15T22:30:49 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 630.50 +/- 184.14
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga vinben007 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga vinben007 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vinben007
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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] |
DriveMyScream/Speech_Recognition | 2023-09-15T22:41:09.000Z | [
"keras",
"region:us"
] | null | DriveMyScream | null | null | DriveMyScream/Speech_Recognition | 0 | 2 | keras | 2023-09-15T22:39:18 | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 9.999999747378752e-05 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 840 | [
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DriveMyScream/Pro_GAN_Image_Generator | 2023-09-15T23:24:43.000Z | [
"keras",
"region:us"
] | null | DriveMyScream | null | null | DriveMyScream/Pro_GAN_Image_Generator | 0 | 2 | keras | 2023-09-15T23:23:38 | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 292 | [
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om-ashish-soni/pos-ner-tagging-v2 | 2023-09-16T05:25:41.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | om-ashish-soni | null | null | om-ashish-soni/pos-ner-tagging-v2 | 0 | 2 | transformers | 2023-09-16T04:22:26 | ---
license: apache-2.0
base_model: om-ashish-soni/pos-ner-tagging-v2
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos-ner-tagging-v2
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9393653920267203
- name: Recall
type: recall
value: 0.9408358887483113
- name: F1
type: f1
value: 0.9401000653531749
- name: Accuracy
type: accuracy
value: 0.9270324365691411
---
<!-- 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. -->
# pos-ner-tagging-v2
This model is a fine-tuned version of [om-ashish-soni/pos-ner-tagging-v2](https://huggingface.co/om-ashish-soni/pos-ner-tagging-v2) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6442
- Precision: 0.9394
- Recall: 0.9408
- F1: 0.9401
- Accuracy: 0.9270
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3297 | 1.0 | 1756 | 0.4190 | 0.9189 | 0.9231 | 0.9210 | 0.9051 |
| 0.2521 | 2.0 | 3512 | 0.3836 | 0.9210 | 0.9300 | 0.9255 | 0.9114 |
| 0.1932 | 3.0 | 5268 | 0.4155 | 0.9295 | 0.9338 | 0.9316 | 0.9183 |
| 0.1325 | 4.0 | 7024 | 0.3969 | 0.9328 | 0.9356 | 0.9342 | 0.9211 |
| 0.0973 | 5.0 | 8780 | 0.4247 | 0.9332 | 0.9367 | 0.9349 | 0.9222 |
| 0.0799 | 6.0 | 10536 | 0.4606 | 0.9338 | 0.9374 | 0.9356 | 0.9229 |
| 0.0554 | 7.0 | 12292 | 0.4836 | 0.9333 | 0.9379 | 0.9356 | 0.9239 |
| 0.0415 | 8.0 | 14048 | 0.5271 | 0.9361 | 0.9391 | 0.9376 | 0.9245 |
| 0.0285 | 9.0 | 15804 | 0.5363 | 0.9366 | 0.9397 | 0.9381 | 0.9253 |
| 0.022 | 10.0 | 17560 | 0.5653 | 0.9377 | 0.9396 | 0.9387 | 0.9258 |
| 0.0146 | 11.0 | 19316 | 0.5962 | 0.9374 | 0.9400 | 0.9387 | 0.9259 |
| 0.0121 | 12.0 | 21072 | 0.6061 | 0.9385 | 0.9401 | 0.9393 | 0.9266 |
| 0.0085 | 13.0 | 22828 | 0.6263 | 0.9384 | 0.9403 | 0.9394 | 0.9261 |
| 0.0062 | 14.0 | 24584 | 0.6365 | 0.9381 | 0.9399 | 0.9390 | 0.9259 |
| 0.0053 | 15.0 | 26340 | 0.6386 | 0.9384 | 0.9402 | 0.9393 | 0.9264 |
| 0.0042 | 16.0 | 28096 | 0.6442 | 0.9394 | 0.9408 | 0.9401 | 0.9270 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
MouseTrap/StyleGen-Loopster-DL | 2023-09-16T05:57:46.000Z | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | MouseTrap | null | null | MouseTrap/StyleGen-Loopster-DL | 0 | 2 | diffusers | 2023-09-16T05:50:05 |
---
license: creativeml-openrail-m
base_model: riffusion/riffusion-model-v1
instance_prompt: Loopster style
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - MouseTrap/StyleGen-Looper
These are LoRA adaption weights for riffusion/riffusion-model-v1. The weights were trained on Loopster style using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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folflo/mt5-small-finetuned-HunSum-1_hvg_index | 2023-09-17T00:43:05.000Z | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | folflo | null | null | folflo/mt5-small-finetuned-HunSum-1_hvg_index | 0 | 2 | transformers | 2023-09-16T06:57:55 | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_keras_callback
model-index:
- name: folflo/mt5-small-finetuned-HunSum-1_hvg_index
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# folflo/mt5-small-finetuned-HunSum-1_hvg_index
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8198
- Validation Loss: 2.6379
- Epoch: 5
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 107952, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.1299 | 2.9515 | 0 |
| 3.1908 | 2.7868 | 1 |
| 3.0099 | 2.7283 | 2 |
| 2.9172 | 2.6738 | 3 |
| 2.8592 | 2.6477 | 4 |
| 2.8198 | 2.6379 | 5 |
### Framework versions
- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
| 1,816 | [
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kyuwon416/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-16T11:55:56.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | kyuwon416 | null | null | kyuwon416/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-16T11:55:19 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 527.00 +/- 171.86
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga kyuwon416 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga kyuwon416 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga kyuwon416
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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AlienKevin/whisper-small-jyutping-without-tones-full | 2023-09-16T12:29:11.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"yue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | AlienKevin | null | null | AlienKevin/whisper-small-jyutping-without-tones-full | 0 | 2 | transformers | 2023-09-16T12:27:24 | ---
language:
- yue
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Jyutping without Tones Full Version
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 Small Jyutping without Tones Full Version
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 14.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0473
- Wer: 4.5089
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- training_steps: 2400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0895 | 0.18 | 800 | 0.0864 | 8.1065 |
| 0.0622 | 0.35 | 1600 | 0.0576 | 5.4563 |
| 0.0555 | 0.53 | 2400 | 0.0473 | 4.5089 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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salim4n/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-16T15:29:24.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | salim4n | null | null | salim4n/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-16T15:28:53 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 385.00 +/- 136.99
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga salim4n -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga salim4n -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga salim4n
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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rafelsiregar/image_classification | 2023-09-18T03:35:49.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | rafelsiregar | null | null | rafelsiregar/image_classification | 0 | 2 | transformers | 2023-09-16T17:19:24 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5375
---
<!-- 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. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3341
- Accuracy: 0.5375
## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 80 | 1.3975 | 0.4062 |
| No log | 2.0 | 160 | 1.3917 | 0.4875 |
| No log | 3.0 | 240 | 1.2964 | 0.5 |
| No log | 4.0 | 320 | 1.2587 | 0.5312 |
| No log | 5.0 | 400 | 1.2705 | 0.5125 |
| No log | 6.0 | 480 | 1.2557 | 0.55 |
| 0.7469 | 7.0 | 560 | 1.3400 | 0.525 |
| 0.7469 | 8.0 | 640 | 1.3586 | 0.5687 |
| 0.7469 | 9.0 | 720 | 1.3317 | 0.5563 |
| 0.7469 | 10.0 | 800 | 1.2965 | 0.5687 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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ThuyNT03/PhoBERT-cls-detail-in-OCR | 2023-10-02T03:19:50.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | ThuyNT03 | null | null | ThuyNT03/PhoBERT-cls-detail-in-OCR | 0 | 2 | transformers | 2023-09-16T17:29:21 | ---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBERT-cls-detail-in-OCR
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. -->
# PhoBERT-cls-detail-in-OCR
This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3094
- Accuracy: 0.95
- F1: 0.9362
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5216 | 1.0 | 25 | 1.2500 | 0.55 | 0.4060 |
| 1.0902 | 2.0 | 50 | 0.8313 | 0.84 | 0.7876 |
| 0.7423 | 3.0 | 75 | 0.5513 | 0.91 | 0.8830 |
| 0.5438 | 4.0 | 100 | 0.4305 | 0.92 | 0.9021 |
| 0.4456 | 5.0 | 125 | 0.3661 | 0.95 | 0.9359 |
| 0.3774 | 6.0 | 150 | 0.3363 | 0.95 | 0.9362 |
| 0.3396 | 7.0 | 175 | 0.3161 | 0.95 | 0.9362 |
| 0.321 | 8.0 | 200 | 0.3094 | 0.95 | 0.9362 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
dima806/data-science-article-titles-engagement | 2023-09-17T10:25:04.000Z | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | dima806 | null | null | dima806/data-science-article-titles-engagement | 0 | 2 | transformers | 2023-09-16T19:35:30 | ---
license: apache-2.0
metrics:
- accuracy
---
See https://www.kaggle.com/code/dima806/medium-ds-article-engaging-titles for more details. | 139 | [
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antoinelouis/crossencoder-mMiniLMv2-L6-mmarcoFR | 2023-10-05T09:33:34.000Z | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"sentence-similarity",
"fr",
"dataset:unicamp-dl/mmarco",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | sentence-similarity | antoinelouis | null | null | antoinelouis/crossencoder-mMiniLMv2-L6-mmarcoFR | 0 | 2 | sentence-transformers | 2023-09-16T21:17:24 | ---
pipeline_tag: sentence-similarity
language: fr
license: apache-2.0
datasets:
- unicamp-dl/mmarco
metrics:
- recall
tags:
- sentence-similarity
library_name: sentence-transformers
---
# crossencoder-mMiniLMv2-L6-mmarcoFR
This is a [sentence-transformers](https://www.SBERT.net) model trained on the **French** portion of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset.
It performs cross-attention between a question-passage pair and outputs a relevance score between 0 and 1. The model can be used for tasks like clustering or [semantic search]((https://www.sbert.net/examples/applications/retrieve_rerank/README.html): given a query, encode the latter with some candidate passages -- e.g., retrieved with BM25 or a biencoder -- then sort the passages in a decreasing order of relevance according to the model's predictions.
## Usage
***
#### Sentence-Transformers
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```bash
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import CrossEncoder
pairs = [('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]
model = CrossEncoder('antoinelouis/crossencoder-mMiniLMv2-L6-mmarcoFR')
scores = model.predict(pairs)
print(scores)
```
#### 🤗 Transformers
Without [sentence-transformers](https://www.SBERT.net), you can use the model as follows:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('antoinelouis/crossencoder-mMiniLMv2-L6-mmarcoFR')
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/crossencoder-mMiniLMv2-L6-mmarcoFR')
pairs = [('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]
features = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt')
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Evaluation
***
We evaluated the model on 500 random queries from the mMARCO-fr train set (which were excluded from training). Each of these queries has at least one relevant and up to 200 irrelevant passages.
Below, we compare the model performance with other cross-encoder models fine-tuned on the same dataset. We report the R-precision (RP), mean reciprocal rank (MRR), and recall at various cut-offs (R@k).
| | model | Vocab. | #Param. | Size | RP | MRR@10 | R@10(↑) | R@20 | R@50 | R@100 |
|---:|:-----------------------------------------------------------------------------------------------------------------------------|:-------|--------:|------:|-------:|---------:|---------:|-------:|-------:|--------:|
| 1 | [crossencoder-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-camembert-base-mmarcoFR) | fr | 110M | 443MB | 35.65 | 50.44 | 82.95 | 91.50 | 96.80 | 98.80 |
| 2 | [crossencoder-mMiniLMv2-L12-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-mmarcoFR) | fr,99+ | 118M | 471MB | 34.37 | 51.01 | 82.23 | 90.60 | 96.45 | 98.40 |
| 3 | [crossencoder-mpnet-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mpnet-base-mmarcoFR) | en | 109M | 438MB | 29.68 | 46.13 | 80.45 | 87.90 | 93.15 | 96.60 |
| 4 | [crossencoder-distilcamembert-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-distilcamembert-mmarcoFR) | fr | 68M | 272MB | 27.28 | 43.71 | 80.30 | 89.10 | 95.55 | 98.60 |
| 5 | [crossencoder-electra-base-french-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-electra-base-french-mmarcoFR) | fr | 110M | 443MB | 28.32 | 45.28 | 79.22 | 87.15 | 93.15 | 95.75 |
| 6 | **crossencoder-mMiniLMv2-L6-mmarcoFR** | fr,99+ | 107M | 428MB | 33.92 | 49.33 | 79.00 | 88.35 | 94.80 | 98.20 |
## Training
***
#### Background
We used the [nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large) model and fine-tuned it with a binary cross-entropy loss function on 1M question-passage pairs in French with a positive-to-negative ratio of 4 (i.e., 25% of the pairs are relevant and 75% are irrelevant).
#### Hyperparameters
We trained the model on a single Tesla V100 GPU with 32GBs of memory during 10 epochs (i.e., 312.4k steps) using a batch size of 32. We used the adamw optimizer with an initial learning rate of 2e-05, weight decay of 0.01, learning rate warmup over the first 500 steps, and linear decay of the learning rate. The sequence length was limited to 512 tokens.
#### Data
We used the French version of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset to fine-tune our model. mMARCO is a multi-lingual machine-translated version of the MS MARCO dataset, a popular large-scale IR dataset.
## Citation
***
```bibtex
@online{louis2023,
author = 'Antoine Louis',
title = 'crossencoder-mMiniLMv2-L6-mmarcoFR: A Cross-Encoder Model Trained on 1M sentence pairs in French',
publisher = 'Hugging Face',
month = 'september',
year = '2023',
url = 'https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L6-mmarcoFR',
}
``` | 5,589 | [
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] |
Undi95/Storytelling-v2-13B-lora | 2023-10-25T18:34:09.000Z | [
"peft",
"license:other",
"region:us"
] | null | Undi95 | null | null | Undi95/Storytelling-v2-13B-lora | 7 | 2 | peft | 2023-09-16T22:41:51 | ---
license: other
library_name: peft
base_model: TheBloke/Llama-2-13B-fp16
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
I'm NOT the author of this work.
I cite anon :
```shell
Storytelling-V2 Qlora. Trained on base Llama-2-13B, works on every L2 13B.
150.5MB of books. Over ten thousand 4096 token samples.
*** for separating chapters, ⁂ for separating books.
```
Credit to "anon49" | 783 | [
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] |
m-aliabbas1/fine_tune_bert_output | 2023-09-17T05:26:49.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | m-aliabbas1 | null | null | m-aliabbas1/fine_tune_bert_output | 0 | 2 | transformers | 2023-09-17T05:22:48 | ---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
model-index:
- name: fine_tune_bert_output
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. -->
# fine_tune_bert_output
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0094
- Overall Precision: 0.9722
- Overall Recall: 0.9722
- Overall F1: 0.9722
- Overall Accuracy: 0.9963
- Number Of Employees F1: 0.9722
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Number Of Employees F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:----------------------:|
| 0.0011 | 50.0 | 1000 | 0.0046 | 0.9722 | 0.9722 | 0.9722 | 0.9963 | 0.9722 |
| 0.0003 | 100.0 | 2000 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 150.0 | 3000 | 0.0094 | 0.9722 | 0.9722 | 0.9722 | 0.9963 | 0.9722 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
### Labels IDs
- {0: 'O', 1: 'B-number_of_employees', 2: 'I-number_of_employees'}
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wooii/DQN-SpaceInvadersNoFrameskip-v4 | 2023-09-17T07:37:04.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | wooii | null | null | wooii/DQN-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-17T06:14:11 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 587.00 +/- 118.37
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga wooii -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga wooii -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga wooii
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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fahmiaziz/LayoutLMv3-wildreceipt | 2023-09-17T08:52:58.000Z | [
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | fahmiaziz | null | null | fahmiaziz/LayoutLMv3-wildreceipt | 0 | 2 | transformers | 2023-09-17T08:52:12 | ---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: track_traininglogs2
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. -->
# track_traininglogs2
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3388
- Precision: 0.8871
- Recall: 0.8800
- F1: 0.8835
- Accuracy: 0.9465
## 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-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6119 | 1.0 | 684 | 0.2890 | 0.8515 | 0.7911 | 0.8202 | 0.9172 |
| 0.2969 | 2.0 | 1368 | 0.2478 | 0.8720 | 0.8279 | 0.8494 | 0.9302 |
| 0.1876 | 3.0 | 2052 | 0.2418 | 0.8354 | 0.8737 | 0.8541 | 0.9332 |
| 0.1476 | 4.0 | 2736 | 0.2480 | 0.8697 | 0.8620 | 0.8658 | 0.9378 |
| 0.1302 | 5.0 | 3420 | 0.2707 | 0.8692 | 0.8697 | 0.8694 | 0.9405 |
| 0.0798 | 6.0 | 4104 | 0.2641 | 0.8755 | 0.8798 | 0.8776 | 0.9434 |
| 0.0642 | 7.0 | 4788 | 0.2694 | 0.8897 | 0.8661 | 0.8777 | 0.9449 |
| 0.0502 | 8.0 | 5472 | 0.3050 | 0.8878 | 0.8741 | 0.8809 | 0.9462 |
| 0.0267 | 9.0 | 6156 | 0.3379 | 0.8888 | 0.8750 | 0.8818 | 0.9453 |
| 0.0273 | 10.0 | 6840 | 0.3388 | 0.8871 | 0.8800 | 0.8835 | 0.9465 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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araffin/ppo-MountainCarContinuous-v0 | 2023-09-17T09:06:26.000Z | [
"stable-baselines3",
"MountainCarContinuous-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | araffin | null | null | araffin/ppo-MountainCarContinuous-v0 | 0 | 2 | stable-baselines3 | 2023-09-17T09:04:56 | ---
library_name: stable-baselines3
tags:
- MountainCarContinuous-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCarContinuous-v0
type: MountainCarContinuous-v0
metrics:
- type: mean_reward
value: -1.16 +/- 0.05
name: mean_reward
verified: false
---
# **PPO** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **PPO** agent playing **MountainCarContinuous-v0**
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
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 MountainCarContinuous-v0 -orga araffin -f logs/
python -m rl_zoo3.enjoy --algo ppo --env MountainCarContinuous-v0 -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 MountainCarContinuous-v0 -orga araffin -f logs/
python -m rl_zoo3.enjoy --algo ppo --env MountainCarContinuous-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env MountainCarContinuous-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env MountainCarContinuous-v0 -f logs/ -orga araffin
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 0.1),
('ent_coef', 0.00429),
('gae_lambda', 0.9),
('gamma', 0.9999),
('learning_rate', 7.77e-05),
('max_grad_norm', 5),
('n_envs', 1),
('n_epochs', 10),
('n_steps', 8),
('n_timesteps', 20000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-3.29, ortho_init=False)'),
('use_sde', True),
('vf_coef', 0.19),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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] |
ahyar002/emotion_classification | 2023-10-04T09:20:49.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | ahyar002 | null | null | ahyar002/emotion_classification | 0 | 2 | transformers | 2023-09-17T13:55:35 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.53125
---
<!-- 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. -->
# emotion_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2445
- Accuracy: 0.5312
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 1.9385 | 0.325 |
| No log | 2.0 | 20 | 1.7153 | 0.4188 |
| No log | 3.0 | 30 | 1.5905 | 0.3937 |
| No log | 4.0 | 40 | 1.4706 | 0.4625 |
| No log | 5.0 | 50 | 1.4078 | 0.5062 |
| No log | 6.0 | 60 | 1.3739 | 0.4813 |
| No log | 7.0 | 70 | 1.3108 | 0.5125 |
| No log | 8.0 | 80 | 1.2874 | 0.5312 |
| No log | 9.0 | 90 | 1.2810 | 0.5312 |
| No log | 10.0 | 100 | 1.2754 | 0.5437 |
| No log | 11.0 | 110 | 1.2380 | 0.5563 |
| No log | 12.0 | 120 | 1.1721 | 0.6125 |
| No log | 13.0 | 130 | 1.2242 | 0.5875 |
| No log | 14.0 | 140 | 1.2530 | 0.525 |
| No log | 15.0 | 150 | 1.2610 | 0.575 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
Abinesh/Llama-2_Vicuna_LoRA-13b | 2023-10-04T07:41:19.000Z | [
"peft",
"pytorch",
"text-generation",
"en",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"license:llama2",
"region:us"
] | text-generation | Abinesh | null | null | Abinesh/Llama-2_Vicuna_LoRA-13b | 2 | 2 | peft | 2023-09-17T14:14:33 | ---
language:
- en
license: llama2
library_name: peft
datasets:
- ehartford/wizard_vicuna_70k_unfiltered
pipeline_tag: text-generation
base_model: meta-llama/Llama-2-7b-chat-hf
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0 | 643 | [
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] |
DriveMyScream/Face_obstruction_removal | 2023-09-17T22:18:36.000Z | [
"keras",
"region:us"
] | null | DriveMyScream | null | null | DriveMyScream/Face_obstruction_removal | 0 | 2 | keras | 2023-09-17T22:17:28 | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 292 | [
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Wariano/bsc-bio-ehr-es-vih-juicio_anam_urgen | 2023-09-19T10:13:27.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Wariano | null | null | Wariano/bsc-bio-ehr-es-vih-juicio_anam_urgen | 0 | 2 | transformers | 2023-09-18T06:38:21 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bsc-bio-ehr-es-vih-juicio_anam_urgen
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. -->
# bsc-bio-ehr-es-vih-juicio_anam_urgen
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0364
- Positives Preds: 1040
- Negative Preds: 208738
- Positives Refs: 1961
- Negative Refs: 207817
- Tp: 826
- Fn: 1135
- Fp: 214
- Tn: 207603
- Accuracy: 0.9936
- Precision: 0.7942
- Recall: 0.4212
- F1: 0.5505
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Positives Preds | Negative Preds | Positives Refs | Negative Refs | Tp | Fn | Fp | Tn | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:------:|:---------------:|:---------------:|:--------------:|:--------------:|:-------------:|:---:|:----:|:---:|:------:|:--------:|:---------:|:------:|:------:|
| 0.0372 | 1.0 | 26223 | 0.0358 | 1276 | 208502 | 1961 | 207817 | 888 | 1073 | 388 | 207429 | 0.9930 | 0.6959 | 0.4528 | 0.5487 |
| 0.04 | 2.0 | 52446 | 0.0364 | 1223 | 208555 | 1961 | 207817 | 873 | 1088 | 350 | 207467 | 0.9931 | 0.7138 | 0.4452 | 0.5484 |
| 0.037 | 3.0 | 78669 | 0.0362 | 1251 | 208527 | 1961 | 207817 | 870 | 1091 | 381 | 207436 | 0.9930 | 0.6954 | 0.4437 | 0.5417 |
| 0.0368 | 4.0 | 104892 | 0.0361 | 1125 | 208653 | 1961 | 207817 | 848 | 1113 | 277 | 207540 | 0.9934 | 0.7538 | 0.4324 | 0.5496 |
| 0.0367 | 5.0 | 131115 | 0.0364 | 1040 | 208738 | 1961 | 207817 | 826 | 1135 | 214 | 207603 | 0.9936 | 0.7942 | 0.4212 | 0.5505 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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ernestum/sac-seals-Walker2d-v1 | 2023-09-18T07:51:58.000Z | [
"stable-baselines3",
"seals/Walker2d-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ernestum | null | null | ernestum/sac-seals-Walker2d-v1 | 0 | 2 | stable-baselines3 | 2023-09-18T07:50:56 | ---
library_name: stable-baselines3
tags:
- seals/Walker2d-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Walker2d-v1
type: seals/Walker2d-v1
metrics:
- type: mean_reward
value: 5665.26 +/- 225.00
name: mean_reward
verified: false
---
# **SAC** Agent playing **seals/Walker2d-v1**
This is a trained model of a **SAC** agent playing **seals/Walker2d-v1**
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
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 sac --env seals/Walker2d-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Walker2d-v1 -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 sac --env seals/Walker2d-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo sac --env seals/Walker2d-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env seals/Walker2d-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env seals/Walker2d-v1 -f logs/ -orga ernestum
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 100000),
('gamma', 0.99),
('learning_rate', 0.0005845844772048097),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'log_std_init': 0.1955317469998743,
'net_arch': [400, 300],
'use_sde': False}),
('tau', 0.02),
('train_freq', 1),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ceadar-ie/Llama2-13B-AIVision360 | 2023-09-28T22:44:05.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"summarization",
"classification",
"translation",
"NLP",
"media and journalism",
"domain specific llm",
"en",
"dataset:AIVision360",
"doi:10.57967/hf/1124",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | ceadar-ie | null | null | ceadar-ie/Llama2-13B-AIVision360 | 0 | 2 | transformers | 2023-09-18T10:28:11 | ---
language:
- en
datasets:
- AIVision360
tags:
- summarization
- classification
- translation
- NLP
- media and journalism
- domain specific llm
license: apache-2.0
pipeline_tag: text-generation
---
# Llama2-13B-AIVision360
NewsConnect 13B (Llama2-13B-AIVision360) is a state-of-the-art, open-source chat model that stands as a beacon for technology, media, and AI news discussions. Built on the robust Llama2-13B architecture, this model has been enhanced and refined utilizing the AIVision360-8k dataset, making it a pioneer in the domain of AI news generation and interpretation.
## Model Details
- Architecture: Llama2-13B
- Training Dataset: [AIVision360-8k](https://huggingface.co/datasets/ceadar-ie/AIVision360-8k)
## Dataset Utilized: AIVision360-8k
Drawing strength from the AIVision360-8k dataset, a curated collection hailing from "ainewshub.ie", this model is tailor-made for technology media and journalism. Offering structured interactions related to AI news, it captures the essence of the latest AI trends and evolutions. For a deeper dive into the dataset visit: [AIVision360-8k](https://huggingface.co/datasets/ceadar-ie/AIVision360-8k)
### Model Specification
- **Developed by:** CeADAR Connect Group
- **Model type:** Large Language Model
- **Language(s):** en
- **Finetuned from model:** Llama2-13B
## Key Features and Functionalities
### Domain Specialization
The Llama2-13B-AIVision360 model is specialized in AI news, serving as a resource for AI researchers, enthusiasts, and media experts.
### Model API Accessibility
Offers a straightforward Python integration for generating AI news insights.
### Performance Optimisation
Efficient performance across both CPU and GPU platforms.
### Data Representation
Utilises a comprehensive AI news dataset, enabling content generation akin to professional journalism standards.
## Model Usage
Experience the capabilities of the Llama2-13B-AIVision360 model through a well-structured Python interface. To kick-start your exploration, follow the steps and snippets given below:
## Prerequisites
### 1. Ensure required packages are available
```python
import torch
import transformers
from typing import Any, Dict
from transformers import PreTrainedTokenizerFast, AutoTokenizer,
AutoModelForCausalLM
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
)
import time
```
### 2. Initiate the model and tokenizer
```python
model_name = "ceadar-ie/Llama2-13B-AIVision360"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto",
torch_dtype=torch.float16, trust_remote_code=True)
```
### 3. Create a function for generating text
```python
def generate_text(input_text):
num_words_input = len(input_text.split())
start_time = time.time()
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=500,temperature=1, top_k=10, top_p=0.1, repetition_penalty = 1.15)
prompt = f" [INST] {input_text} [/INST]"
result = pipe(prompt)
generated_text = result[0]['generated_text'].replace(prompt, "").strip()
end_time = time.time()
time_taken = end_time - start_time
num_words_generated = len(generated_text.split())
print("Generated Output:")
print(generated_text)
print('=' * 60 )
print("\nNumber of Words in Input Text:", num_words_input)
print("Number of Words in Generated Output:", num_words_generated)
print("Time Taken to Generate Output: {:.2f} seconds".format(time_taken))
```
## Example Evaluation and Use
### Example Prompt 1:
List new regulations or guidelines that have been introduced globally concerning AI ethics and bias mitigation.
### Generated Output:
Globally, there is a growing concern about the potential negative impacts of artificial intelligence (AI) on society, particularly with regards to ethics and bias. In response, regulators and policymakers have been introducing new guidelines and regulations to address these issues. Some examples of the new regulations and guidelines introduced globally concerning AI ethics and bias mitigation include:
- The European Union's General Data Protection Regulation (GDPR) and the EU AI Act, which aim to ensure transparency and accountability in the use of AI technology.
- The US Federal Trade Commission's (FTC) policy statement on commercial use of AI, which highlights the need for companies to avoid deceptive practices and discrimination when using AI.
- Australia's review into the use of facial recognition technology, which has led to a temporary ban on the use of this technology in the country.
- Britain's proposed AI Code, which will provide guidance on the use of trustworthy AI systems and promote principles such as safety, robustness, privacy, and fairness.
- India's consultation paper on ethics and governance of AI, which outlines principles such as transparency, accountability, democracy, and inclusion.
These new regulations and guidelines demonstrate a growing trend towards the regulation of AI and the importance of addressing ethical and bias issues in the development and use of AI technology.
### Example Prompt 2:
List What are the current market trends in the AI industry?
### Generated Output:
The text provides an overview of the current state of the AI industry, including the performance of various companies and technologies in the sector. The trends listed include the success of Nvidia's H100 GPU, the growing demand for AI chips, and the competition among major tech companies to dominate the AI market. These trends highlight the rapid growth and increasing importance of the AI industry, with a focus on the competitive landscape and the demand for high-performance computing resources. The current market trends in the AI industry include Nvidia's dominance of the H100 GPU, the growing demand for AI chips, and the competition among major tech companies such as Intel, Google, and Microsoft to dominate the AI market. 85% of H100 GPUs sold since January have been used for AI tasks, there is a shortage of AI chips, and the market for programmable AI chips is expected to reach $30 billion by 2025. Additionally, major tech companies are investing heavily in AI research and development, and the market for AI chips is becoming increasingly competitive.
### Example Prompt 3:
Concisely list in separate points, any recent controversies or debates in the AI community.
### Generated Output:
Recent controversies and debates in the AI community include:
- The release of a chatbot called Bard by Google, which promptly spread false information about NASA's moon mission
- OpenAI CEO Sam Altman tweeted that Bard had been tested for months, but later clarified that only select employees were involved in testing
- Microsoft has partnered with OpenAI to integrate GPT-4 into its products, including Bing and Edge browser, but there have been reports of unsolicited calls and payments mistakenly charged to customers using these products
- Meta Platforms' Oversight Board received more funding, but found out through social media like everyone else when CEO Mark Zuckerberg announced the change
- There have been calls for greater transparency from Meta after it failed to inform the board of the AI hiring freeze and layoffs affecting AI teams
### Example Prompt 4:
Concisely list in separate points, sectors or industries that are seeing the most rapid AI adoption right now. Do not repeat provided information.
### Generated Output:
Sectors and industries are rapidly incorporating AI into their processes to improve efficiency and decision-making, according to a report by Forrester Research. The report highlights the urgent need for organizations to adopt AI to remain competitive and agile in the face of changing market conditions and customer demands. Here are the details about the report and its findings:
- The report by Forrester Research highlights the growing importance of AI adoption in various sectors and industries.
- The report provides insights into the current state of AI adoption and the potential for future growth.
- The sectors and industries that are seen the most rapid AI adoption right now include finance, healthcare, retail, and manufacturing.
- Organizations that adopt AI are able to improve efficiency, make better decisions, and gain a competitive advantage.
- The report also highlights the challenges and barriers to AI adoption, such as data quality issues and lack of expertise.
Here are the sectors or industries that are seeing the most rapid AI adoption right now, based on the report by Forrester Research:
1. Finance
2. Healthcare
3. Retail
4. Manufacturing
## Training Details
### Training Hyperparameters
- per_device_train_batch_size = 6
- gradient_accumulation_steps = 4
- optim = "paged_adamw_32bit"
- warmup_steps = 100
- learning_rate = 2e-4
- max_grad_norm = 0.3
- warmup_ratio = 0.03
## Model Limitations
Potential Biases: With its fine-tuning centered on AI news sources, inherent biases from these sources may reflect in the model's outputs.
## Licensing
The Llama2-13B-AIVision360 model, developed in collaboration with CeADAR Connect Group, combines the licensing frameworks of both Llama2 and AIVision360. Under Meta's terms, users are granted a non-exclusive, worldwide, non-transferable, royalty-free limited license for the use and modification of Llama Materials, inclusive of the Llama2 model and its associated documentation. When redistributing, the provided Agreement and a specific attribution notice must be included. In alignment with the AIVision360 dataset's licensing, the model is also distributed under the Apache 2.0 open-source license, promoting its use and modification within the AI community, while ensuring content reliability sourced from established AI news publishers.
## Out-of-Scope Use
Llama2-13B-AIVision360 is specifically tailored for AI news discussions. It is not optimized for:
- General, non-AI-related conversations.
- Domain-specific tasks outside AI news.
- Direct interfacing with physical devices or applications.
## Bias, Risks, and Limitations
- Dataset Biases: The AIVision360-8k dataset may contain inherent biases that influence the model's outputs.
- Over-reliance: The model is an aid, not a replacement for human expertise. Decisions should be made with careful consideration.
- Content Understanding: The model lacks human-like understanding and cannot judge the veracity of news.
- Language Limitations: The model's primary language is English. Performance may decrease with other languages.
- Knowledge Cut-off: The model may not be aware of events or trends post its last training update.
## Citation:
```
@misc {ceadar_2023,
author = { {CeADAR} },
title = { Llama2-13B-AIVision360 (Revision caa5124) },
year = 2023,
url = { https://huggingface.co/ceadar-ie/Llama2-13B-AIVision360 },
doi = { 10.57967/hf/1124 },
publisher = { Hugging Face }
}
```
## Contact:
For any further inquiries or feedback concerning Llama2-13B-AIVision360, please forward your communications to ahtsham.zafar@ucd.ie | 11,276 | [
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Sunny98/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-18T11:10:49.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Sunny98 | null | null | Sunny98/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-18T11:10:08 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 709.00 +/- 320.43
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga Sunny98 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga Sunny98 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Sunny98
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier | 2023-09-18T13:35:21.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | image-classification | ayoubkirouane | null | null | ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier | 0 | 2 | transformers | 2023-09-18T11:56:08 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# Fine-Tuned ViT for Beans Leaf Disease Classification
## Model Information
* **Model Name**: VIT_Beans_Leaf_Disease_Classifier
* **Base Model**: Google/ViT-base-patch16-224-in21k
* **Task**: Image Classification (Beans Leaf Disease Classification)
* **Dataset**: Beans leaf dataset with images of diseased and healthy leaves.
## Problem Statement
The goal of this model is to classify leaf images into three categories:
```
{
"angular_leaf_spot": 0,
"bean_rust": 1,
"healthy": 2,
}
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1495 | 1.54 | 100 | 0.0910 | 0.9774 |
| 0.0121 | 3.08 | 200 | 0.0155 | 1.0 |
## Framework versions
+ Transformers 4.33.2
+ Pytorch 2.0.1+cu118
+ Datasets 2.14.5
+ Tokenizers 0.13.3
## Get Started With The Model:
```
! pip -q install datasets transformers[torch]
```
```python
from transformers import pipeline
from PIL import Image
# Use a pipeline as a high-level helper
pipe = pipeline("image-classification", model="ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier")
# Load the image
image_path = "Your image_path "
image = Image.open(image_path)
# Run inference using the pipeline
result = pipe(image)
# The result contains the predicted label and the corresponding score
predicted_label = result[0]['label']
confidence_score = result[0]['score']
print(f"Predicted Label: {predicted_label}")
print(f"Confidence Score: {confidence_score}")
```
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johannes-garstenauer/distilbert_class_heaps | 2023-10-30T13:27:21.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"digital forensics",
"dataset:johannes-garstenauer/structs_token_size_4_reduced_labelled_eval",
"dataset:johannes-garstenauer/structs_token_size_4_reduced_labelled_train",
"endpoints_compatible",
"region:us"
] | text-classification | johannes-garstenauer | null | null | johannes-garstenauer/distilbert_class_heaps | 1 | 2 | transformers | 2023-09-18T13:59:04 | ---
datasets:
- johannes-garstenauer/structs_token_size_4_reduced_labelled_eval
- johannes-garstenauer/structs_token_size_4_reduced_labelled_train
tags:
- digital forensics
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
DistilBERT for sequence classification trained on OpenSSH heap data structures dataset for the purpose of generating representations.
This model was created for the thesis "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer.
It is finetuned from "johannes-garstenauer/distilbert_masking_heaps".
### Model Description
- **Developed by:** Johannes Garstenauer
- **Funded by [optional]:** Universität Passau
- **Finetuned from model [optional]:** johannes-garstenauer/distilbert_masking_heaps
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://zenodo.org/records/10053730
### 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. -->
Training data: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_train
Validation data: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_eval
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kolkata97/autotrain-pe-llm-0 | 2023-09-20T11:57:37.000Z | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain",
"it",
"dataset:kolkata97/autotrain-data-pe-llm-0.6",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | text-classification | kolkata97 | null | null | kolkata97/autotrain-pe-llm-0 | 0 | 2 | transformers | 2023-09-18T13:59:49 | ---
tags:
- autotrain
- text-classification
language:
- it
widget:
- text: "I love AutoTrain"
datasets:
- kolkata97/autotrain-data-pe-llm-0.6
co2_eq_emissions:
emissions: 0.022138627441573373
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 89942144050
- CO2 Emissions (in grams): 0.0221
## Validation Metrics
- Loss: 0.841
- Accuracy: 0.761
- Macro F1: 0.644
- Micro F1: 0.761
- Weighted F1: 0.750
- Macro Precision: 0.679
- Micro Precision: 0.761
- Weighted Precision: 0.748
- Macro Recall: 0.635
- Micro Recall: 0.761
- Weighted Recall: 0.761
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/kolkata97/autotrain-pe-llm-0.6-89942144050
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("kolkata97/autotrain-pe-llm-0.6-89942144050", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("kolkata97/autotrain-pe-llm-0.6-89942144050", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,292 | [
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kamara3k/dqn-SpaceInvadersNoFrameskip-v4 | 2023-09-18T15:16:12.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | kamara3k | null | null | kamara3k/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-09-18T15:15:33 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 688.50 +/- 181.62
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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
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 dqn --env SpaceInvadersNoFrameskip-v4 -orga kamara3k -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga kamara3k -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga kamara3k
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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amaliaam/image_classification | 2023-09-18T16:58:49.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | amaliaam | null | null | amaliaam/image_classification | 0 | 2 | transformers | 2023-09-18T16:06:43 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: image_classification
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. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.0915
- eval_accuracy: 0.0938
- eval_runtime: 10.0977
- eval_samples_per_second: 15.845
- eval_steps_per_second: 0.99
- step: 0
## 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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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MattStammers/qrdqn-QbertNoFrameskip-v4-final | 2023-09-22T15:30:37.000Z | [
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | MattStammers | null | null | MattStammers/qrdqn-QbertNoFrameskip-v4-final | 0 | 2 | stable-baselines3 | 2023-09-18T20:21:26 | ---
library_name: stable-baselines3
tags:
- QbertNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: QbertNoFrameskip-v4
type: QbertNoFrameskip-v4
metrics:
- type: mean_reward
value: 23787.50 +/- 2397.09
name: mean_reward
verified: false
---
# **QRDQN** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **QbertNoFrameskip-v4**
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
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 qrdqn --env QbertNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env QbertNoFrameskip-v4 -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 qrdqn --env QbertNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env QbertNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo qrdqn --env QbertNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env QbertNoFrameskip-v4 -f logs/ -orga MattStammers
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 50000000.0),
('normalize', False),
('optimize_memory_usage', False),
('policy', 'CnnPolicy')])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
## Additional Comments
Training for this seems to peak at about 50 million timesteps
Interestingly this guy doesn't even seem to care about using the spinners. I guess he gets so good at dodging the snake that he considers them valueless. | 2,667 | [
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sattensil/parts_matching | 2023-09-18T20:27:20.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | sattensil | null | null | sattensil/parts_matching | 0 | 2 | transformers | 2023-09-18T20:22:32 | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: parts_matching
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. -->
# parts_matching
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.4187
## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.4169 | 1.0 | 635 | 8.4187 |
| 8.4774 | 2.0 | 1270 | 8.6748 |
| 8.4123 | 3.0 | 1905 | 9.0346 |
| 8.4861 | 4.0 | 2540 | 9.7969 |
| 8.3973 | 5.0 | 3175 | 10.5403 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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jordandavis/ppo-LunarLander-v2 | 2023-09-19T15:48:16.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jordandavis | null | null | jordandavis/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-09-18T21:28:32 | ---
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: 280.30 +/- 26.11
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
...
```
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umd-zhou-lab/claude2-alpaca-7B | 2023-10-22T16:08:23.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:umd-zhou-lab/claude2_alpaca",
"license:llama2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | umd-zhou-lab | null | null | umd-zhou-lab/claude2-alpaca-7B | 0 | 2 | transformers | 2023-09-18T22:03:17 | ---
license: llama2
datasets:
- umd-zhou-lab/claude2_alpaca
language:
- en
---
# Model Card for umd-zhou-lab/claude2-alpaca-7B
<!-- Provide a quick summary of what the model is/does. -->
This model is trained by fine-tuning llama-2 with claude2 alpaca data.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** UMD Tianyi Zhou Lab
- **Model type:** An auto-regressive language model based on the transformer architecture
- **License:** Llama 2 Community License Agreement
- **Finetuned from model:** [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b)
### Model Sources
<!-- Provide the basic links for the model. -->
- **GitHub:** [Claude2-Alpaca](https://github.com/Lichang-Chen/claude2-alpaca)
- **Data:** [claude2_alpaca](https://huggingface.co/datasets/umd-zhou-lab/claude2_alpaca)
## Uses
The primary use of this model is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## Training
We use the prompt from [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
| --- | ---: | ---: | ---: | ---: | ---: |
| Model (7B) | 128 | 2e-5 | 3 | 4096 | 0 |
## Performance
Compared to the llama2-chat, our models can have better average performance.<br>
| | Average | ARC | HellaSwag | MMLU | TruthfulQA | Alpaca_Eval | Avg Length |
|---|---|---|---|---|---|---|---|
| Llama-2-7b-chat | 56.335 | 52.9 | 78.55 | 48.32 | 45.57 | 71.37 | 1479 |
| Llama-2-13b-chat | 59.935 | 59.04| 81.94 | 54.64 | 44.12 | 81.09 | 1513 |
|||||||||
| claude_alpaca-7b | 57.78 | 56.66 | 81.17 | 46.58 | 46.71 | 71.23 | 1066 |
| claude_alpaca-13b | 61.29 | 61.18 | 84.08 | 55.74 | 44.18 | 78.93 | 1127 |
## Citation
Please consider citing our paper if you think our codes, data, or models are useful. Thank you!
```
@misc{claude2-alpaca,
author = {Lichang Chen and Khalid Saifullah and Ming Li and Tianyi Zhou and Heng Huang},
title = {Claude2-Alpaca: Instruction tuning datasets distilled from claude},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Lichang-Chen/claude2-alpaca}},
}
``` | 2,498 | [
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cpuli/my_awesome_eli5_mlm_model | 2023-09-19T01:01:07.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | cpuli | null | null | cpuli/my_awesome_eli5_mlm_model | 0 | 2 | transformers | 2023-09-18T23:52:27 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model
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. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5502
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 26 | 2.4994 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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owngpt/finetuned-phobert-base-v2 | 2023-11-02T02:30:16.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | owngpt | null | null | owngpt/finetuned-phobert-base-v2 | 0 | 2 | sentence-transformers | 2023-09-19T04:18:04 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 13 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 258, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,791 | [
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] |
TheBloke/13B-Legerdemain-L2-AWQ | 2023-09-27T12:50:26.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/13B-Legerdemain-L2-AWQ | 0 | 2 | transformers | 2023-09-19T04:59:18 | ---
license: llama2
model_name: 13B Legerdemain L2
base_model: CalderaAI/13B-Legerdemain-L2
inference: false
model_creator: CalderaAI
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# 13B Legerdemain L2 - AWQ
- Model creator: [CalderaAI](https://huggingface.co/CalderaAI)
- Original model: [13B Legerdemain L2](https://huggingface.co/CalderaAI/13B-Legerdemain-L2)
<!-- description start -->
## Description
This repo contains AWQ model files for [CalderaAI's 13B Legerdemain L2](https://huggingface.co/CalderaAI/13B-Legerdemain-L2).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.
It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/13B-Legerdemain-L2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/13B-Legerdemain-L2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/13B-Legerdemain-L2-GGUF)
* [CalderaAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/CalderaAI/13B-Legerdemain-L2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files and AWQ parameters
For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/13B-Legerdemain-L2-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Serving this model from vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/13B-Legerdemain-L2-AWQ --quantization awq
```
When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/13B-Legerdemain-L2-AWQ", quantization="awq")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-python start -->
## How to use this AWQ model from Python code
### Install the necessary packages
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later
```shell
pip3 install autoawq
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### You can then try the following example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/13B-Legerdemain-L2-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
print("Output: ", tokenizer.decode(generation_output[0]))
# Inference can also be done using transformers' pipeline
from transformers import pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm).
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781).
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: CalderaAI's 13B Legerdemain L2
## 13B-Legerdemain-L2
13B-Legerdemain-L2 is the first model merge of its kind in a series of LLaMaV2 models mixed using a custom script built in-house by CalderaAI called Model-REVOLVER.
M-REVOLVER is also the first in a series of custom scripts based on the concept of mixtuning - not only does the end user have contol over which models are mixed
and their percentages on a per-layer basis, we tackle the problem of overcomplexity that arises from such a level of control; this model is the first of its series.
## The Model-REVOLVER Process Designed by CalderaAI
M-REVOLVER (Rapid Evolution Via Optimized-List Viewer Evaluated Response)
Per-layer merging between parent models is a nebulous inexact science, and therefore impractical to most users despite the raw power it offers. We propose an
entirely new approach that gives the user a clear looking glass into the impact vastly different layer merge configurations between selected parent models of
their choice will have on the potential offspring model - especially its inherited behaviors. We've developed solution MK.1 - A cyclic random pattern search
in place that determines all layer merge ratios, combines test models, infers prompt completions, and deletes a prototype after data collection is saved.
When the cyclic system has completed its entire run, nothing is left but the telemetry collected along with the cycle and layer merge ratios from every
single prototype merge. This data is then used to empower the user to choose which offspring is most fit to their desired outcome. This final step is
only initiated when all necessary data has been aggregated from all assembled-tested-erased prototypes sampled in the search space.
From here, the user is provided five 300 token prompt completions from each and every offspring contender that was created and tested during the cyclic process.
The user simply browses each prototype's series of responses and selects their desired outcome model by entering the cycle number associated with the prompt
completions they feel best suits their vision. That model is then instantly repatriated into the official offspring of its parent models and tokenizer files
found to be most relevant are instantly auto-copied from the parent model dir to the offspring.
That's it - the user instantly has a complete model based on the behavior they decided on, suggested from one of many potentials; all with their own unique
trait inheritence thanks to layer merge auto randomization inside an ordered system. One more thing - the user not only selects how many cycles to run,
the user can edit prompts.txt which the system reads as a single prompt - this means if the user desires to use any multiline instruct format to observe
all potential model outcomes from instruct, or desires simply their own prompt, it's up to them.. simply works.
Link to GitHub for M-REVOLVER are at the end of the model card. More advanced MergeTech toolsets and merge techniques are currently under internal testing
and development by Caldera.
## 13B-Legerdemain-L2 Use
13B-Legerdemain-L2 is capable of following Alpaca instructions however it seems far more receptive to the by-the-book method as seen here:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
{New Line}
```
The primary model of choice for this model was a story-only model called Holodeck by KoboldAI. Traits preserved seem to be detailed descriptiveness, verbosity,
and characters with personality. The two other models selected were 13B-Nous-Hermes by NousResearch and 13B-orca-8k-3319 by OpenAssistant. I began the process by
providing an incredibly obscene prompt and simply ignored each and every guardrail or censorship laden prompt completion and accepted the offensive ones in turn -
intent wasn't to be crass but trigger censorship parts of the network to test if it's possible to completely undermine them. Second pass with offspring model and
Orca was a simple milquetoast prompt to gauge vocabulary, word flow, and intelligence as I selected the most fit in that category. Result model seems a bit of a
curiosity - different samplers and even a different UI (as I went from TGUI to KoboldAI) seem to uncover different facets of behavior. Godlike preset with Alpaca
Instruct in TGUI worked fine. In KoboldAI some tweaking was necessary to get the same experience. If you choose to test this model, have fun - it's got a mind of
its own.
Model-REVOLVER Git:
https://github.com/Digitous/ModelREVOLVER
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] |
TheBloke/llama-2-13B-chat-limarp-v2-merged-AWQ | 2023-09-27T12:50:34.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"en",
"license:agpl-3.0",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/llama-2-13B-chat-limarp-v2-merged-AWQ | 0 | 2 | transformers | 2023-09-19T05:38:10 | ---
language:
- en
license: agpl-3.0
library_name: transformers
tags:
- llama
- llama-2
model_name: Llama 2 13B Chat - LimaRP v2 Merged
base_model: Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged
inference: false
model_creator: Doctor-Shotgun
model_type: llama
pipeline_tag: text-generation
prompt_template: "### Instruction:\nCharacter's Persona: bot character description\n\
\nUser's persona: user character description\n \nScenario: what happens in the\
\ story\n\nPlay the role of Character. You must engage in a roleplaying chat with\
\ User below this line. Do not write dialogues and narration for User. Character\
\ should respond with messages of medium length.\n\n### Input:\nUser: {prompt}\n\
\n### Response:\nCharacter: \n"
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama 2 13B Chat - LimaRP v2 Merged - AWQ
- Model creator: [Doctor-Shotgun](https://huggingface.co/Doctor-Shotgun)
- Original model: [Llama 2 13B Chat - LimaRP v2 Merged](https://huggingface.co/Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged)
<!-- description start -->
## Description
This repo contains AWQ model files for [Doctor-Shotgun's Llama 2 13B Chat - LimaRP v2 Merged](https://huggingface.co/Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.
It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/llama-2-13B-chat-limarp-v2-merged-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama-2-13B-chat-limarp-v2-merged-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/llama-2-13B-chat-limarp-v2-merged-GGUF)
* [Doctor-Shotgun's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: LimaRP-Alpaca
```
### Instruction:
Character's Persona: bot character description
User's persona: user character description
Scenario: what happens in the story
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length.
### Input:
User: {prompt}
### Response:
Character:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `agpl-3.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Doctor-Shotgun's Llama 2 13B Chat - LimaRP v2 Merged](https://huggingface.co/Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged).
<!-- licensing end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files and AWQ parameters
For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/llama-2-13B-chat-limarp-v2-merged-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Serving this model from vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/llama-2-13B-chat-limarp-v2-merged-AWQ --quantization awq
```
When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/llama-2-13B-chat-limarp-v2-merged-AWQ", quantization="awq")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-python start -->
## How to use this AWQ model from Python code
### Install the necessary packages
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later
```shell
pip3 install autoawq
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### You can then try the following example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/llama-2-13B-chat-limarp-v2-merged-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
prompt = "Tell me about AI"
prompt_template=f'''### Instruction:
Character's Persona: bot character description
User's persona: user character description
Scenario: what happens in the story
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length.
### Input:
User: {prompt}
### Response:
Character:
'''
print("\n\n*** Generate:")
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
print("Output: ", tokenizer.decode(generation_output[0]))
# Inference can also be done using transformers' pipeline
from transformers import pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm).
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781).
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Doctor-Shotgun's Llama 2 13B Chat - LimaRP v2 Merged
# Model Card: llama-2-13b-chat-limarp-v2-merged
This is a Llama 2-based model consisting of Llama 2 13b chat (https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) merged with LIMARP Lora v2 (https://huggingface.co/lemonilia/limarp-llama2-v2).
Requested by @dampf
## Usage:
Intended to be prompted with the Alpaca instruction format of the LIMARP v2:
```
### Instruction:
Character's Persona: {bot character description}
User's Persona: {user character description}
Scenario: {what happens in the story}
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length.
### Input:
Character: {utterance}
### Response:
User: {utterance}
```
## Bias, Risks, and Limitations
The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. It is not intended for supplying factual information or advice in any form.
## Training Details
This model is a merge. Please refer to the link repositories of the base model and lora for details.
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TamerAbdelaziz/distilbert-base-uncased-finetuned-imdb_CSV | 2023-09-19T07:31:24.000Z | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | TamerAbdelaziz | null | null | TamerAbdelaziz/distilbert-base-uncased-finetuned-imdb_CSV | 0 | 2 | transformers | 2023-09-19T06:26:36 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: TamerAbdelaziz/distilbert-base-uncased-finetuned-imdb_CSV
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TamerAbdelaziz/distilbert-base-uncased-finetuned-imdb_CSV
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1498
- Validation Loss: 0.2223
- Train Accuracy: 0.9164
- Epoch: 1
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2808 | 0.2603 | 0.8922 | 0 |
| 0.1498 | 0.2223 | 0.9164 | 1 |
### Framework versions
- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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nlp-chula/augment-aspect-finnlp-th | 2023-09-26T03:20:12.000Z | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | nlp-chula | null | null | nlp-chula/augment-aspect-finnlp-th | 0 | 2 | transformers | 2023-09-19T07:24:33 | ---
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: augment-aspect-finnlp-th
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. -->
# augment-aspect-finnlp-th
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8283
- Accuracy: 0.7819
## 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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1709 | 1.0 | 1024 | 0.9057 | 0.7215 |
| 0.762 | 2.0 | 2048 | 0.7890 | 0.7580 |
| 0.5093 | 3.0 | 3072 | 0.8182 | 0.7637 |
| 0.3634 | 4.0 | 4096 | 0.8283 | 0.7819 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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