modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Blaine-Mason/hackMIT-finetuned-sst2
|
[
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
] |
text-classification
|
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| 36
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: my_awesome_swag_model
results: []
duplicated_from: hypefi/my_awesome_swag_model
---
<!-- 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_swag_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0107
- Accuracy: 0.7899
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7454 | 1.0 | 4597 | 0.6122 | 0.7662 |
| 0.3786 | 2.0 | 9194 | 0.6400 | 0.7833 |
| 0.1338 | 3.0 | 13791 | 1.0107 | 0.7899 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BobBraico/distilbert-base-uncased-finetuned-imdb
|
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| 0
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: Larxel/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2
|
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}
| 0
| null |
---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- onevholy/autotrain-data-ner2
co2_eq_emissions:
emissions: 0.26356486260831363
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 50086120238
- CO2 Emissions (in grams): 0.2636
## Validation Metrics
- Loss: 0.427
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- F1: 1.000
## 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/onevholy/autotrain-ner2-50086120238
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("onevholy/autotrain-ner2-50086120238", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("onevholy/autotrain-ner2-50086120238", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
BotterHax/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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| 8
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-12
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. -->
# bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-12
This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7209
## 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: 4e-05
- 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: 8.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.529 | 1.0 | 1 | 6.3267 |
| 6.9588 | 2.0 | 2 | 3.4058 |
| 3.9832 | 3.0 | 3 | 2.4716 |
| 3.1592 | 4.0 | 4 | 3.9477 |
| 3.8702 | 5.0 | 5 | 2.0330 |
| 2.9637 | 6.0 | 6 | 0.0111 |
| 1.7751 | 7.0 | 7 | 2.0013 |
| 1.1956 | 8.0 | 8 | 1.2214 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BumBelDumBel/ZORK_AI_FANTASY
|
[] | null |
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| 0
| null |
To run this model, please clone: https://github.com/ggerganov/llama.cpp and install using Make (see instruction in Github)
Then, download the model and place it in the models folder and run:
Linux:
./main -m models/baize_lora_13b_q4.bin -n -1 -t 4 --ignore-eos --temp 0.4 --interactive-first --repeat_penalty 1.2 --instruct --color
Windows:
main.exe -m models/baize_lora_13b_q4.bin -n -1 -t 4 --ignore-eos --temp 0.4 --interactive-first --repeat_penalty 1.2 --instruct --color
It should look something like this:
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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"BertForTokenClassification"
],
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| 85
| 2023-04-17T05:09:28Z
|
---
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>
|
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
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"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
| 37
| 2023-04-17T05:24:46Z
|
---
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 |
| learning_rate | 0.0010000000474974513 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
|
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"has_space"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
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}
| 19,850
| null |
---
tags:
- generated_from_trainer
model-index:
- name: dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20_continue
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. -->
# dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20_continue
This model is a fine-tuned version of [rohitp1/dgx2_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_30](https://huggingface.co/rohitp1/dgx2_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_30) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 33.4149
- eval_wer: 0.9868
- eval_runtime: 768.2877
- eval_samples_per_second: 7.81
- eval_steps_per_second: 7.81
- epoch: 13.23
- step: 900
## 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: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-da
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
| 449
| 2023-04-17T05:34:40Z
|
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
- instruction fine-tuning
model-index:
- name: flan-t5-small-distil-v2
results: []
language:
- en
pipeline_tag: text2text-generation
widget:
- text: >-
how can I become more healthy?
example_title: example
---
<!-- 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. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-Flan-T5-783M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to response to human instructions written in natural language.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text2text-generation', model = checkpoint)
input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 783M.
### Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 45
| null |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
- instruction fine-tuning
model-index:
- name: flan-t5-small-distil-v2
results: []
language:
- en
pipeline_tag: text2text-generation
widget:
- text: >-
how can I become more healthy?
example_title: example
---
<!-- 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. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-T5-738M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to response to human instructions written in natural language.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text2text-generation', model = checkpoint)
input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [t5-large](https://huggingface.co/t5-large) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 738M.
### Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 63
| 2023-04-17T05:43:58Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flan-t5-large-extraction-all-dm_4000-ep10-nonstop
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. -->
# flan-t5-large-extraction-all-dm_4000-ep10-nonstop
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5811
- Hint Hit Num: 2.366
- Hint Precision: 0.4373
- Num: 5.3574
- Gen Len: 18.7764
## 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: 12
- eval_batch_size: 96
- seed: 1799
- 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 | Hint Hit Num | Hint Precision | Num | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------------:|:------:|:-------:|
| 2.2334 | 0.6 | 200 | 1.5968 | 2.7154 | 0.4867 | 5.6214 | 18.96 |
| 2.0104 | 1.2 | 400 | 1.5582 | 2.6402 | 0.4732 | 5.5972 | 18.9258 |
| 1.8919 | 1.8 | 600 | 1.5531 | 2.5878 | 0.4689 | 5.5198 | 18.8178 |
| 1.8495 | 2.4 | 800 | 1.5572 | 2.4834 | 0.4556 | 5.4372 | 18.8354 |
| 1.7984 | 2.99 | 1000 | 1.5569 | 2.426 | 0.4514 | 5.352 | 18.791 |
| 1.7446 | 3.59 | 1200 | 1.5443 | 2.4482 | 0.4502 | 5.4084 | 18.8278 |
| 1.7167 | 4.19 | 1400 | 1.5532 | 2.4502 | 0.4524 | 5.3864 | 18.8066 |
| 1.6838 | 4.79 | 1600 | 1.5603 | 2.3258 | 0.4387 | 5.2672 | 18.7164 |
| 1.6713 | 5.39 | 1800 | 1.5602 | 2.3874 | 0.4431 | 5.3434 | 18.7818 |
| 1.6403 | 5.99 | 2000 | 1.5636 | 2.313 | 0.4315 | 5.3092 | 18.7958 |
| 1.5963 | 6.59 | 2200 | 1.5745 | 2.353 | 0.4389 | 5.3184 | 18.7622 |
| 1.6203 | 7.19 | 2400 | 1.5757 | 2.3634 | 0.4348 | 5.3772 | 18.784 |
| 1.5808 | 7.78 | 2600 | 1.5822 | 2.33 | 0.4318 | 5.3452 | 18.8062 |
| 1.5436 | 8.38 | 2800 | 1.5791 | 2.3764 | 0.4394 | 5.3548 | 18.7792 |
| 1.5707 | 8.98 | 3000 | 1.5786 | 2.3592 | 0.4349 | 5.37 | 18.807 |
| 1.5512 | 9.58 | 3200 | 1.5820 | 2.362 | 0.4378 | 5.3418 | 18.773 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 31
| null |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="sumitk/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 62
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2_dp_2
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. -->
# gpt2_dp_2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1193
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.5051 | 0.56 | 500 | 5.4057 |
| 4.8491 | 1.11 | 1000 | 4.9029 |
| 4.5723 | 1.67 | 1500 | 4.6001 |
| 4.2162 | 2.22 | 2000 | 4.3995 |
| 4.0917 | 2.78 | 2500 | 4.2408 |
| 3.8062 | 3.33 | 3000 | 4.1527 |
| 3.7775 | 3.89 | 3500 | 4.1193 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1,862
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_test
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sumitk/taxi_test", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-half
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 16
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: mdapri/poca-SoccerTwos_v3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 52
| null |
---
license: apache-2.0
datasets:
- OpenAssistant/oasst1
language:
- ja
metrics:
- accuracy
- bertscore
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a 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 Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
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"conversational": {
"max_length": null
},
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},
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 21
| null |
---
language:
- en
tags:
- t5
- xxl
- rewriting
- paraphrase
- paraphrase-generation
- paraphrasing
- pytorch
- text-generation
- text2text-generation
license: "apache-2.0"
---
This is the HuggingFace model release of our paper "Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense".
## Paper and Github Repository
Paper: https://arxiv.org/abs/2303.13408
Code: https://github.com/martiansideofthemoon/ai-detection-paraphrases
Usage instructions: https://github.com/martiansideofthemoon/ai-detection-paraphrases#running-the-paraphraser-model-dipper
## What is DIPPER?
DIPPER ("**Di**scourse **P**ara**p**hras**er**") is a 11B parameter paraphrase generation model built by fine-tuning T5-XXL. DIPPER possesses two unique features that help its outputs evade AI-generated text detectors:
* *Paraphrasing long-form text in context*: Most modern paraphrasers are exclusively trained on sentence-level data, ignoring discourse-level information. However, many critical use cases of LLMs involve generating long-form text in responses to detailed userspecified prompts. Thus, we train DIPPER to paraphrase paragraph-length texts, re-order content, and optionally leverage context such as input prompts.
* *Controlling output diversity*: Another weakness of existing paraphrasers is that they lack an easy way to control output diversity. An attacker may want to apply just the minimum amount of lexical and syntactic modifications necessary to evade a detection algorithm. DIPPER provides users with two intuitive scalar control knobs at inference time that are trained end-to-end: one controls the lexical diversity of the paraphrase, and the other controls the amount of content re-ordering.
We leverage the PAR3 dataset publicly released by Thai et al. (2022) to train DIPPER. This dataset contains multiple translations of non-English novels into English aligned at a paragraph level (e.g., it contains both the Henry Morley and Robert Adams translations of Voltaire’s Candide), which we treat as paragraphlevel paraphrases and use to train our paraphraser.
## Using DIPPER
Full instructions: https://github.com/martiansideofthemoon/ai-detection-paraphrases#running-the-paraphraser-model-dipper
We suggest using the code below to use the model correctly:
```
class DipperParaphraser(object):
def __init__(self, model="kalpeshk2011/dipper-paraphraser-xxl", verbose=True):
time1 = time.time()
self.tokenizer = T5Tokenizer.from_pretrained('google/t5-v1_1-xxl')
self.model = T5ForConditionalGeneration.from_pretrained(model)
if verbose:
print(f"{model} model loaded in {time.time() - time1}")
self.model.cuda()
self.model.eval()
def paraphrase(self, input_text, lex_diversity, order_diversity, prefix="", sent_interval=3, **kwargs):
"""Paraphrase a text using the DIPPER model.
Args:
input_text (str): The text to paraphrase. Make sure to mark the sentence to be paraphrased between <sent> and </sent> blocks, keeping space on either side.
lex_diversity (int): The lexical diversity of the output, choose multiples of 20 from 0 to 100. 0 means no diversity, 100 means maximum diversity.
order_diversity (int): The order diversity of the output, choose multiples of 20 from 0 to 100. 0 means no diversity, 100 means maximum diversity.
**kwargs: Additional keyword arguments like top_p, top_k, max_length.
"""
assert lex_diversity in [0, 20, 40, 60, 80, 100], "Lexical diversity must be one of 0, 20, 40, 60, 80, 100."
assert order_diversity in [0, 20, 40, 60, 80, 100], "Order diversity must be one of 0, 20, 40, 60, 80, 100."
lex_code = int(100 - lex_diversity)
order_code = int(100 - order_diversity)
input_text = " ".join(input_text.split())
sentences = sent_tokenize(input_text)
prefix = " ".join(prefix.replace("\n", " ").split())
output_text = ""
for sent_idx in range(0, len(sentences), sent_interval):
curr_sent_window = " ".join(sentences[sent_idx:sent_idx + sent_interval])
final_input_text = f"lexical = {lex_code}, order = {order_code}"
if prefix:
final_input_text += f" {prefix}"
final_input_text += f" <sent> {curr_sent_window} </sent>"
final_input = self.tokenizer([final_input_text], return_tensors="pt")
final_input = {k: v.cuda() for k, v in final_input.items()}
with torch.inference_mode():
outputs = self.model.generate(**final_input, **kwargs)
outputs = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
prefix += " " + outputs[0]
output_text += " " + outputs[0]
return output_text
if __name__ == "__main__":
dp = DipperParaphraser()
prompt = "In a shocking finding, scientist discovered a herd of unicorns living in a remote valley."
input_text = "They have never been known to mingle with humans. Today, it is believed these unicorns live in an unspoilt environment which is surrounded by mountains. Its edge is protected by a thick wattle of wattle trees, giving it a majestic appearance. Along with their so-called miracle of multicolored coat, their golden coloured feather makes them look like mirages. Some of them are rumored to be capable of speaking a large amount of different languages. They feed on elk and goats as they were selected from those animals that possess a fierceness to them, and can \"eat\" them with their long horns."
print(f"Input = {prompt} <sent> {input_text} </sent>\n")
output_l60_sample = dp.paraphrase(input_text, lex_diversity=60, order_diversity=0, prefix=prompt, do_sample=True, top_p=0.75, top_k=None, max_length=512)
print(f"Output (Lexical diversity = 60, Sample p = 0.75) = {output_l60_sample}\n")
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 133
| null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 1059.00 +/- 439.89
name: mean_reward
verified: false
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga Mihail-P -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga Mihail-P -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Mihail-P
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 150000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('normalize', False)])
```
|
CAUKiel/JavaBERT-uncased
|
[
"pytorch",
"safetensors",
"bert",
"fill-mask",
"java",
"code",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7
| null |
---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
widget:
- text: "Battery temperature is below 15 degrees Celsius. Warm up the battery temperature to above 25 degree Celsius to ensure a safe flight."
example_title: "Negative Sentiment"
- text: "Aircraft is returning to the Home Point. Minimum RTH Altitude is 30m. You can reset the RTH Altitude in Remote Controller Settings after cancelling RTH if necessary."
example_title: "Positive Sentiment"
---
|
CAUKiel/JavaBERT
|
[
"pytorch",
"safetensors",
"bert",
"fill-mask",
"code",
"arxiv:2110.10404",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 388
| null |
---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- onevholy/autotrain-data-bert-ner-cased
co2_eq_emissions:
emissions: 0.27179932029905673
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 50114120258
- CO2 Emissions (in grams): 0.2718
## Validation Metrics
- Loss: 0.011
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- F1: 1.000
## 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/onevholy/autotrain-bert-ner-cased-50114120258
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("onevholy/autotrain-bert-ner-cased-50114120258", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("onevholy/autotrain-bert-ner-cased-50114120258", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
CLTL/gm-ner-xlmrbase
|
[
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"nl",
"transformers",
"dighum",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2
| null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: UchihaMadara/thesis-pretrained-5
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. -->
# UchihaMadara/thesis-pretrained-5
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 7.3418
- Validation Loss: 7.3457
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 653, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 |
|:----------:|:---------------:|:-----:|
| 7.3369 | 7.3491 | 0 |
| 7.3418 | 7.3457 | 1 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
CLTL/icf-levels-adm
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
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"RobertaForSequenceClassification"
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}
}
| 33
| null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: pabloyesteb/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CLTL/icf-levels-att
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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},
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},
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},
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}
}
}
| 32
| null |
---
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: 273.54 +/- 20.04
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
...
```
|
CLTL/icf-levels-enr
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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},
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},
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},
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},
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}
}
| 30
| null |
---
license: mit
language:
- nl
- en
pipeline_tag: translation
---
# Opus-MT-NL-EN-Healthcare
This model can be used to translate medical text from Dutch to English, as a baseline for Clinical NLP systems in Dutch.
## Model Details
### Model Description
This model based on MarianMT was trained using OpusNLP data as well as specialized medical data (see paper for more details).
- **Developed by:** François Remy
- **Model type:** Translation
- **Languages:** From Dutch to English
- **License:** MIT
- **Finetuned from model:** Helsinki-NLP/opus-mt-nl-en
### Model Sources
- **Repository:** [Snomed Translation Dictionaries](https://github.com/FremyCompany/snomed-translate-dictionaries)
- **Paper:** [Taming large lexicons: translating clinical text using medical ontologies and sentence templates](https://biblio.ugent.be/publication/01GMN7J3X37CKWCKQV6DWGM4ZE/file/01GMN7NT30DKF37RXN31DSJZ2D.pdf)
## Citation
If you use this model as part of your work, please cite the following paper:
**BibTeX:**
```
@inproceedings{remy-et-al-2022-translating,
author = {{Remy, François and De Jaeger, P. and Demuynck, Kris}},
title = {{Taming large lexicons: translating clinical text using medical ontologies and sentence templates}},
booktitle = {{EmP 2022}}
location = {{Roeselare, Belgium}},
year = {{2022}},
language = {{en}},
pages = {{5}},
}
```
**APA:**
```
Remy, F., De Jaeger, P., & Demuynck, K. (2022). Taming large lexicons: translating clinical text using medical ontologies and sentence templates. Presented at the EmP 2022, The “Engineers meet Practicians” conference, Roeselare, Belgium.
```
|
CLTL/icf-levels-etn
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 31
| null |
---
datasets:
- arxiv_dataset
language:
- en
metrics:
- precision
library_name: spacy
pipeline_tag: text-classification
tags:
- journal
- sections
---
|
CM-CA/Cartman
|
[] | null |
{
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},
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},
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},
"translation_en_to_fr": {
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}
| 0
| null |
---
license: apache-2.0
---
Introduction
This repo contains pre-trained models, checkpoints, training logs and decoding results of the following pull-request:
https://github.com/k2-fsa/icefall/pull/997
|
CSResearcher/TestModel
|
[
"license:mit"
] | null |
{
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},
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},
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},
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}
}
}
| 0
| null |
---
license: wtfpl
---
X X X warehouse X X X
|
CZWin32768/xlm-align
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2106.06381",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 6
| null |
Access to model hobizhope/my_summary_model is restricted and you are not in the authorized list. Visit https://huggingface.co/hobizhope/my_summary_model to ask for access.
|
Calamarii/calamari
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
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"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0
| 2023-04-17T07:11:06Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- cartesinus/iva_mt_wslot
metrics:
- bleu
model-index:
- name: iva_mt_wslot-m2m100_418M-en-es
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: iva_mt_wslot
type: iva_mt_wslot
config: en-es
split: validation
args: en-es
metrics:
- name: Bleu
type: bleu
value: 69.2836
language:
- en
- es
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. -->
# iva_mt_wslot-m2m100_418M-en-es
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the iva_mt_wslot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0115
- Bleu: 69.2836
- Gen Len: 20.2064
## Model description
More information needed
## How to use
First please make sure to install `pip install transformers`. First download model:
```python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
import torch
def translate(input_text, lang):
input_ids = tokenizer(input_text, return_tensors="pt")
generated_tokens = model.generate(**input_ids, forced_bos_token_id=tokenizer.get_lang_id(lang))
return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
model_name = "cartesinus/iva_mt_wslot-m2m100_418M-0.1.0-en-es"
tokenizer = M2M100Tokenizer.from_pretrained(model_name, src_lang="en", tgt_lang="es")
model = M2M100ForConditionalGeneration.from_pretrained(model_name)
```
Then you can translate either plain text like this:
```python
print(translate("set the temperature on my thermostat", "es"))
```
or you can translate with slot annotations that will be restored in tgt language:
```python
print(translate("wake me up at <a>nine am<a> on <b>friday<b>", "es"))
```
Limitations of translation with slot transfer:
1) Annotated words must be placed between semi-xml tags like this "this is \<a\>example\<a\>"
2) There is no closing tag for example "\<\a\>" in the above example - this is done on purpose to omit problems with backslash escape
3) If the sentence consists of more than one slot then simply use the next alphabet letter. For example "this is \<a\>example\<a\> with more than \<b\>one\<b\> slot"
4) Please do not add space before the first or last annotated word because this particular model was trained this way and it most probably will lower its results
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.0135 | 1.0 | 2104 | 0.0122 | 66.8284 | 20.2851 |
| 0.009 | 2.0 | 4208 | 0.0112 | 68.1164 | 20.1501 |
| 0.0067 | 3.0 | 6312 | 0.0110 | 68.256 | 20.0603 |
| 0.0051 | 4.0 | 8416 | 0.0110 | 68.7002 | 20.1219 |
| 0.0037 | 5.0 | 10520 | 0.0112 | 68.699 | 20.2733 |
| 0.0027 | 6.0 | 12624 | 0.0113 | 68.9916 | 20.209 |
| 0.0023 | 7.0 | 14728 | 0.0115 | 69.2836 | 20.2064 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
CalvinHuang/mt5-small-finetuned-amazon-en-es
|
[
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
summarization
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 16
| null |
---
license: mit
---
bert-base-uncased, trained on fake news dataset.
Input title, text, split with ////////////
Output: 1 - fake, 0 - real.
|
Cameron/BERT-Jigsaw
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 35
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- cartesinus/iva_mt_wslot
metrics:
- bleu
model-index:
- name: iva_mt_wslot-m2m100_418M-en-de
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: iva_mt_wslot
type: iva_mt_wslot
config: en-de
split: validation
args: en-de
metrics:
- name: Bleu
type: bleu
value: 66.5548
language:
- en
- de
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. -->
# iva_mt_wslot-m2m100_418M-en-de
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the iva_mt_wslot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0126
- Bleu: 66.5548
- Gen Len: 20.6835
## Model description
More information needed
## How to use
First please make sure to install `pip install transformers`. First download model:
```python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
import torch
def translate(input_text, lang):
input_ids = tokenizer(input_text, return_tensors="pt")
generated_tokens = model.generate(**input_ids, forced_bos_token_id=tokenizer.get_lang_id(lang))
return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
model_name = "cartesinus/iva_mt_wslot-m2m100_418M-0.1.0-en-de"
tokenizer = M2M100Tokenizer.from_pretrained(model_name, src_lang="en", tgt_lang="de")
model = M2M100ForConditionalGeneration.from_pretrained(model_name)
```
Then you can translate either plain text like this:
```python
print(translate("set the temperature on my thermostat", "de"))
```
or you can translate with slot annotations that will be restored in tgt language:
```python
print(translate("wake me up at <a>nine am<a> on <b>friday<b>", "de"))
```
Limitations of translation with slot transfer:
1) Annotated words must be placed between semi-xml tags like this "this is \<a\>example\<a\>"
2) There is no closing tag for example "\<\a\>" in the above example - this is done on purpose to omit problems with backslash escape
3) If the sentence consists of more than one slot then simply use the next alphabet letter. For example "this is \<a\>example\<a\> with more than \<b\>one\<b\> slot"
4) Please do not add space before the first or last annotated word because this particular model was trained this way and it most probably will lower its results
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.0183 | 1.0 | 1884 | 0.0144 | 63.5045 | 20.2994 |
| 0.0119 | 2.0 | 3768 | 0.0127 | 66.0473 | 20.5423 |
| 0.0083 | 3.0 | 5652 | 0.0123 | 65.5139 | 20.5409 |
| 0.0065 | 4.0 | 7536 | 0.0124 | 66.0731 | 20.6114 |
| 0.0048 | 5.0 | 9420 | 0.0122 | 66.2454 | 20.5906 |
| 0.0038 | 6.0 | 11304 | 0.0124 | 66.7412 | 20.6263 |
| 0.0031 | 7.0 | 13188 | 0.0126 | 66.5548 | 20.6835 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Cameron/BERT-eec-emotion
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 36
| null |
---
tags:
- conversational
---
Mostly just for testing; On a less trained model.
#House MD DialoGPT Model
|
Cameron/BERT-mdgender-convai-ternary
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 38
| null |
---
license: other
---
# 聲明 Disclaimer
本資料夾中的模型不是我所製作，版權歸原作者所有（各模型版權詳見 http://www.civitai.com 所示）。我上傳至本資料夾僅爲方便在綫抽取資源，并非盈利。
The models in this folder are not made by me, and the copyright belongs to the original author (see http://www.civitai.com for details on the copyright of each model). I uploaded to this folder only for the convenience of extracting resources online, not for profit.
# 模型列表 List of Models
本資料夾中所有模型詳見下表。
All the models in this folder are detailed in the table below.
| 模型名稱 Model Name | Civitai 頁面鏈接 Civitai Page Link | Civitai 下載鏈接 Civitai Download Link |
|----------------------|--------------------|--------------------|
|BeautyProMix_v1.safetensors |https://civitai.com/models/16599/beautypromix |https://civitai.com/api/download/models/19597 |
<img src="https://img1.wsimg.com/isteam/ip/062334e1-a8fb-4784-b30a-5b8d15b1aaeb/00098-2694622730.png" width="768" height="">
<img src="https://img1.wsimg.com/isteam/ip/062334e1-a8fb-4784-b30a-5b8d15b1aaeb/00331-1764330505.png" width="768" height="">
|
Camzure/MaamiBot
|
[] | null |
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| 0
| null |
Access to model AI-Officially/AI_Req is restricted and you are not in the authorized list. Visit https://huggingface.co/AI-Officially/AI_Req to ask for access.
|
Canyonevo/DialoGPT-medium-KingHenry
|
[] | null |
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| 0
| 2023-04-17T07:23:36Z
|
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CapitainData/wav2vec2-large-xlsr-turkish-demo-colab
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: distillgpt2_dp
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. -->
# distillgpt2_dp
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1676
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.5302 | 0.56 | 500 | 5.4222 |
| 4.8669 | 1.11 | 1000 | 4.9173 |
| 4.599 | 1.67 | 1500 | 4.6151 |
| 4.2545 | 2.22 | 2000 | 4.4298 |
| 4.1416 | 2.78 | 2500 | 4.2782 |
| 3.8827 | 3.33 | 3000 | 4.1968 |
| 3.8577 | 3.89 | 3500 | 4.1676 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Capreolus/birch-bert-large-car_mb
|
[
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null |
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| 4
| null |
---
datasets:
- attack
library_name: diffusers
pipeline_tag: text-to-image
---
|
Capreolus/birch-bert-large-msmarco_mb
|
[
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null |
{
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"BertForNextSentencePrediction"
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| 1
| null |
To install the requirements for this demo, do pip install "optimum-intel[openvino, diffusers]".
```python
from optimum.intel.openvino import OVStableDiffusionPipeline
pipe = OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-pokemons-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt = "plant pokemon in jungle"
output = pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
```
|
Captain-1337/CrudeBERT
|
[
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
] |
text-classification
|
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}
| 28
| null |
---
language:
- ja
tags:
- 安倍晋三
- AbeShinzo
- FormerJapanesePrimeMinister
- ESPnet
- VITS
---
|
Captain272/lstm
|
[] | null |
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| 0
| null |
---
language:
- en
tags:
- FantasyGPT
widget:
- "Legolas and Gimli advanced on the orcs, raising their weapons with a harrowing war cry."
- "Hermione smiled at Harry."
- "Ghost bared his teeth."
- "Geralt drew his sword"
- "Galadriel drew her sword"
- "Daenerys kissed Gandalf, as the witcher hacked off Lord Voldemort's head with a brutal swing of Longclaw."
- "Harry leapt forward, dodging Fingolfin's wildfire and reaching for the ring."
---
GPT-2 (including tokenizer) trained **from scratch** on some of my favorite books (about 31M words in total).
It's only trained on an RTX 3090 for three hours, so don't take it seriously, just have fun!
- peak lr: 4e-4
- global batch size: 32
- weight decay: 0.01
- training steps: 25k
- warmup steps: 1k
- lr decay: cosine
Example usage:
```python
from transformers import AutoTokenizer, GPT2LMHeadModel
tokenizer = AutoTokenizer.from_pretrained('Geralt-Targaryen/FantasyGPT')
model = GPT2LMHeadModel.from_pretrained('Geralt-Targaryen/FantasyGPT')
input_text = ["Daenerys kissed Gandalf, as the witcher hacked off Lord Voldemort's head with a brutal swing of Longclaw."]
input_tokenized = tokenizer(input_text, return_tensors='pt')
output = model.generate(inputs=input_tokenized.input_ids, max_new_tokens=256, do_sample=True, top_p=0.9, temperature=1)
print(tokenizer.decode(output[0]))
```
Sample output:
> Daenerys kissed Gandalf, as the witcher hacked off Lord Voldemort's head with a brutal swing of Longclaw. “Do you know what a warrior like that will do?”
>
> “I am a knight of seven,” Geralt said. “And how is this knight?”
>
> “Prince, it is known,” replied the witcher, “I am a knight of Solamnia, not a Knight. A knight of Solamnia in the name of Reorx, with elven armies at the head of his knights and knights. You do not even remember my name, elf. It was a good call.”
>
> “That name,” said the witcher, “what does it mean, elf?”
>
> “Some story,” said the old knight. “A good story from the Cataclysm.”
>
> The witcher snorted and looked at the witcher. “That is how you feel, elf. You don't understand why the knights won't be executed for them.”
>
> “That's because the knight is a king,” Chireadan said finally, “the king's elder son. Because he is, the knights would also be honorable with the title of knighthood. But a warrior would be better suited to have the honor to win.”
>
> “But…”
>
> “It's true,” said the old knight, “that, isn't entirely what you want. The lance of your sword is one of the best
|
CarlosTron/Yo
|
[] | null |
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| 0
| null |
---
license: other
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: opt125_dp
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. -->
# opt125_dp
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2128
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.6353 | 0.56 | 500 | 5.5525 |
| 4.9297 | 1.11 | 1000 | 5.0101 |
| 4.6464 | 1.67 | 1500 | 4.6887 |
| 4.2876 | 2.22 | 2000 | 4.4929 |
| 4.1641 | 2.78 | 2500 | 4.3336 |
| 3.879 | 3.33 | 3000 | 4.2447 |
| 3.8521 | 3.89 | 3500 | 4.2128 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Carolhuehuehuehue/Sla
|
[] | null |
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| 0
| null |
---
duplicated_from: HuggingFaceM4/tiny-random-LlamaForCausalLM
---
|
dccuchile/albert-base-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 34
| null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 13.24 +/- 3.59
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MrDivakaruni/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
dccuchile/albert-base-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
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}
}
| 14
| null |
---
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: 248.68 +/- 18.21
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
...
```
|
dccuchile/albert-large-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
| 25
| null |
---
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: 844.00 +/- 291.13
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 hawkeoni -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 hawkeoni -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 hawkeoni
```
## 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)])
```
|
dccuchile/albert-tiny-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 5
| null |
---
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: 247.74 +/- 32.01
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
...
```
|
dccuchile/albert-tiny-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 7
| null |
---
title:
- YSDA_ML_PROJECT_FEDOR_GERASIMOV
emoji:
- 🌍
colorFrom:
- green
colorTo:
- blue
sdk:
- streamlit
app_file:
- app.py
pinned:
- false
license:
- openrail
---
|
dccuchile/albert-xlarge-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
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"early_stopping": null,
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}
}
}
| 5
| null |
Access to model shawt100/shawt100 is restricted and you are not in the authorized list. Visit https://huggingface.co/shawt100/shawt100 to ask for access.
|
dccuchile/albert-xlarge-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
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},
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"max_length": null
},
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"prefix": null
},
"translation_en_to_fr": {
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},
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"prefix": null
}
}
}
| 3
| null |
---
tags:
- generated_from_keras_callback
model-index:
- name: layout_lm_fine_tune_funsd_dataset
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. -->
# layout_lm_fine_tune_funsd_dataset
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2499
- Validation Loss: 0.6927
- Train Overall Precision: 0.7401
- Train Overall Recall: 0.8159
- Train Overall F1: 0.7761
- Train Overall Accuracy: 0.8046
- Epoch: 7
## 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': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.7326 | 1.4249 | 0.2261 | 0.2052 | 0.2151 | 0.5250 | 0 |
| 1.1901 | 0.9108 | 0.5753 | 0.6207 | 0.5972 | 0.7156 | 1 |
| 0.7777 | 0.7170 | 0.6511 | 0.7396 | 0.6925 | 0.7679 | 2 |
| 0.5681 | 0.6626 | 0.6988 | 0.7777 | 0.7362 | 0.7920 | 3 |
| 0.4449 | 0.6512 | 0.7236 | 0.7762 | 0.7490 | 0.8013 | 4 |
| 0.3576 | 0.6547 | 0.7251 | 0.7888 | 0.7556 | 0.8073 | 5 |
| 0.2910 | 0.6700 | 0.7380 | 0.7958 | 0.7658 | 0.8106 | 6 |
| 0.2499 | 0.6927 | 0.7401 | 0.8159 | 0.7761 | 0.8046 | 7 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 7
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: aadhaar_front
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. -->
# aadhaar_front
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
dccuchile/albert-base-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
{
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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},
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 586
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.55 +/- 0.86
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
dccuchile/albert-tiny-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
{
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 393
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: generic_ner_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. -->
# generic_ner_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0992
- Precision: 0.8749
- Recall: 0.8980
- F1: 0.8863
- Accuracy: 0.9746
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1084 | 1.0 | 1958 | 0.1001 | 0.8639 | 0.8822 | 0.8730 | 0.9722 |
| 0.0681 | 2.0 | 3916 | 0.0971 | 0.8699 | 0.8955 | 0.8825 | 0.9740 |
| 0.0471 | 3.0 | 5874 | 0.0992 | 0.8749 | 0.8980 | 0.8863 | 0.9746 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
dccuchile/albert-xxlarge-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
{
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 42
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flan-t5-large-extraction-all-dm_8000-ep10-nonstop
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. -->
# flan-t5-large-extraction-all-dm_8000-ep10-nonstop
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5334
- Hint Hit Num: 2.2682
- Hint Precision: 0.4261
- Num: 5.2682
- Gen Len: 18.776
## 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: 12
- eval_batch_size: 96
- seed: 1799
- 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 | Hint Hit Num | Hint Precision | Num | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------------:|:------:|:-------:|
| 2.1988 | 0.3 | 200 | 1.5870 | 2.6768 | 0.4798 | 5.6224 | 18.9782 |
| 1.976 | 0.6 | 400 | 1.5613 | 2.5624 | 0.4663 | 5.495 | 18.9114 |
| 1.9367 | 0.9 | 600 | 1.5303 | 2.4822 | 0.4551 | 5.4574 | 18.9418 |
| 1.8626 | 1.2 | 800 | 1.5336 | 2.3896 | 0.4403 | 5.3966 | 18.9096 |
| 1.8278 | 1.5 | 1000 | 1.5110 | 2.5016 | 0.4514 | 5.5236 | 18.9486 |
| 1.8115 | 1.8 | 1200 | 1.5116 | 2.2886 | 0.4269 | 5.3196 | 18.9194 |
| 1.776 | 2.1 | 1400 | 1.5212 | 2.3278 | 0.4326 | 5.3394 | 18.8936 |
| 1.7332 | 2.4 | 1600 | 1.5172 | 2.2982 | 0.4323 | 5.2878 | 18.828 |
| 1.7543 | 2.7 | 1800 | 1.5003 | 2.473 | 0.4522 | 5.4414 | 18.9048 |
| 1.7212 | 3.0 | 2000 | 1.5051 | 2.3878 | 0.4389 | 5.4032 | 18.854 |
| 1.6915 | 3.3 | 2200 | 1.5083 | 2.3352 | 0.4347 | 5.3186 | 18.836 |
| 1.6808 | 3.6 | 2400 | 1.5065 | 2.3414 | 0.4367 | 5.321 | 18.8136 |
| 1.6812 | 3.9 | 2600 | 1.5047 | 2.3422 | 0.4376 | 5.3144 | 18.812 |
| 1.6408 | 4.2 | 2800 | 1.5158 | 2.3108 | 0.4297 | 5.33 | 18.8116 |
| 1.6266 | 4.5 | 3000 | 1.5086 | 2.2752 | 0.4227 | 5.329 | 18.8472 |
| 1.6144 | 4.8 | 3200 | 1.5120 | 2.2434 | 0.4192 | 5.283 | 18.8684 |
| 1.6164 | 5.1 | 3400 | 1.5135 | 2.3636 | 0.4356 | 5.3754 | 18.8526 |
| 1.5981 | 5.4 | 3600 | 1.5202 | 2.245 | 0.4201 | 5.2762 | 18.8574 |
| 1.5923 | 5.7 | 3800 | 1.5190 | 2.2462 | 0.4208 | 5.28 | 18.8358 |
| 1.5835 | 6.0 | 4000 | 1.5182 | 2.2812 | 0.4249 | 5.3042 | 18.8182 |
| 1.577 | 6.3 | 4200 | 1.5268 | 2.2928 | 0.4254 | 5.335 | 18.8268 |
| 1.5572 | 6.6 | 4400 | 1.5229 | 2.261 | 0.4237 | 5.276 | 18.7788 |
| 1.5522 | 6.9 | 4600 | 1.5153 | 2.3372 | 0.4323 | 5.3516 | 18.8326 |
| 1.5095 | 7.2 | 4800 | 1.5334 | 2.2108 | 0.4195 | 5.2086 | 18.7338 |
| 1.5568 | 7.5 | 5000 | 1.5243 | 2.302 | 0.4305 | 5.2964 | 18.7742 |
| 1.5373 | 7.8 | 5200 | 1.5277 | 2.2502 | 0.4204 | 5.2868 | 18.8176 |
| 1.5191 | 8.1 | 5400 | 1.5321 | 2.2716 | 0.4247 | 5.2856 | 18.7934 |
| 1.5261 | 8.4 | 5600 | 1.5300 | 2.2938 | 0.4273 | 5.3064 | 18.7828 |
| 1.5202 | 8.7 | 5800 | 1.5337 | 2.2744 | 0.4236 | 5.3086 | 18.8092 |
| 1.4942 | 9.0 | 6000 | 1.5351 | 2.2522 | 0.4239 | 5.257 | 18.7704 |
| 1.4816 | 9.3 | 6200 | 1.5349 | 2.2528 | 0.4247 | 5.2518 | 18.7682 |
| 1.5169 | 9.6 | 6400 | 1.5339 | 2.2698 | 0.4265 | 5.2646 | 18.7736 |
| 1.5007 | 9.9 | 6600 | 1.5334 | 2.269 | 0.4263 | 5.2664 | 18.776 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-ner
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 81
| null |
---
language: en
license: apache-2.0
library_name: pytorch
tags:
- deep-reinforcement-learning
- reinforcement-learning
- DI-engine
- LunarLander-v2
benchmark_name: OpenAI/Gym/Box2d
task_name: LunarLander-v2
pipeline_tag: reinforcement-learning
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: OpenAI/Gym/Box2d-LunarLander-v2
type: OpenAI/Gym/Box2d-LunarLander-v2
metrics:
- type: mean_reward
value: 244.37 +/- 3.77
name: mean_reward
---
# Play **LunarLander-v2** with **TD3** Policy
## Model Description
<!-- Provide a longer summary of what this model is. -->
This is a simple **TD3** implementation to OpenAI/Gym/Box2d **LunarLander-v2** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo).
**DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework.
## Model Usage
### Install the Dependencies
<details close>
<summary>(Click for Details)</summary>
```shell
# install huggingface_ding
git clone https://github.com/opendilab/huggingface_ding.git
pip3 install -e ./huggingface_ding/
# install environment dependencies if needed
pip3 install DI-engine[common_env]
```
</details>
### Git Clone from Huggingface and Run the Model
<details close>
<summary>(Click for Details)</summary>
```shell
# running with trained model
python3 -u run.py
```
**run.py**
```python
from ding.bonus import TD3Agent
from ding.config import Config
from easydict import EasyDict
import torch
# Pull model from files which are git cloned from huggingface
policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu"))
cfg = EasyDict(Config.file_to_dict("policy_config.py"))
# Instantiate the agent
agent = TD3Agent(
env="lunarlander_continuous",
exp_name="LunarLander-v2-TD3",
cfg=cfg.exp_config,
policy_state_dict=policy_state_dict
)
# Continue training
agent.train(step=5000)
# Render the new agent performance
agent.deploy(enable_save_replay=True)
```
</details>
### Run Model by Using Huggingface_ding
<details close>
<summary>(Click for Details)</summary>
```shell
# running with trained model
python3 -u run.py
```
**run.py**
```python
from ding.bonus import TD3Agent
from huggingface_ding import pull_model_from_hub
# Pull model from Hugggingface hub
policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/LunarLander-v2-TD3")
# Instantiate the agent
agent = TD3Agent(
env="lunarlander_continuous",
exp_name="LunarLander-v2-TD3",
cfg=cfg.exp_config,
policy_state_dict=policy_state_dict
)
# Continue training
agent.train(step=5000)
# Render the new agent performance
agent.deploy(enable_save_replay=True)
```
</details>
## Model Training
### Train the Model and Push to Huggingface_hub
<details close>
<summary>(Click for Details)</summary>
```shell
#Training Your Own Agent
python3 -u train.py
```
**train.py**
```python
from ding.bonus import TD3Agent
from huggingface_ding import push_model_to_hub
# Instantiate the agent
agent = TD3Agent("lunarlander_continuous", exp_name="LunarLander-v2-TD3")
# Train the agent
return_ = agent.train(step=int(4000000), collector_env_num=4, evaluator_env_num=4)
# Push model to huggingface hub
push_model_to_hub(
agent=agent.best,
env_name="OpenAI/Gym/Box2d",
task_name="LunarLander-v2",
algo_name="TD3",
wandb_url=return_.wandb_url,
github_repo_url="https://github.com/opendilab/DI-engine",
github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/td3.html",
github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html",
installation_guide="pip3 install DI-engine[common_env]",
usage_file_by_git_clone="./td3/lunarlander_td3_deploy.py",
usage_file_by_huggingface_ding="./td3/lunarlander_td3_download.py",
train_file="./td3/lunarlander_td3.py",
repo_id="OpenDILabCommunity/LunarLander-v2-TD3"
)
```
</details>
**Configuration**
<details close>
<summary>(Click for Details)</summary>
```python
exp_config = {
'env': {
'manager': {
'episode_num': float("inf"),
'max_retry': 1,
'retry_type': 'reset',
'auto_reset': True,
'step_timeout': None,
'reset_timeout': None,
'retry_waiting_time': 0.1,
'cfg_type': 'BaseEnvManagerDict'
},
'stop_value': 240,
'env_id': 'LunarLanderContinuous-v2',
'collector_env_num': 4,
'evaluator_env_num': 8,
'n_evaluator_episode': 8,
'act_scale': True
},
'policy': {
'model': {
'twin_critic': True,
'obs_shape': 8,
'action_shape': 2,
'action_space': 'regression'
},
'learn': {
'learner': {
'train_iterations': 1000000000,
'dataloader': {
'num_workers': 0
},
'log_policy': True,
'hook': {
'load_ckpt_before_run': '',
'log_show_after_iter': 100,
'save_ckpt_after_iter': 10000,
'save_ckpt_after_run': True
},
'cfg_type': 'BaseLearnerDict'
},
'update_per_collect': 256,
'batch_size': 256,
'learning_rate_actor': 0.0003,
'learning_rate_critic': 0.001,
'ignore_done': False,
'target_theta': 0.005,
'discount_factor': 0.99,
'actor_update_freq': 2,
'noise': True,
'noise_sigma': 0.1,
'noise_range': {
'min': -0.5,
'max': 0.5
}
},
'collect': {
'collector': {},
'unroll_len': 1,
'noise_sigma': 0.1,
'n_sample': 256
},
'eval': {
'evaluator': {
'eval_freq': 1000,
'render': {
'render_freq': -1,
'mode': 'train_iter'
},
'cfg_type': 'InteractionSerialEvaluatorDict',
'n_episode': 8,
'stop_value': 240
}
},
'other': {
'replay_buffer': {
'replay_buffer_size': 100000
}
},
'on_policy': False,
'cuda': True,
'multi_gpu': False,
'bp_update_sync': True,
'traj_len_inf': False,
'type': 'td3',
'priority': False,
'priority_IS_weight': False,
'random_collect_size': 10000,
'transition_with_policy_data': False,
'action_space': 'continuous',
'reward_batch_norm': False,
'multi_agent': False,
'cfg_type': 'TD3PolicyDict'
},
'exp_name': 'LunarLander-v2-TD3',
'seed': 0,
'wandb_logger': {
'gradient_logger': True,
'video_logger': True,
'plot_logger': True,
'action_logger': True,
'return_logger': False
}
}
```
</details>
**Training Procedure**
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/LunarLander-v2-TD3)
## Model Information
<!-- Provide the basic links for the model. -->
- **Github Repository:** [repo link](https://github.com/opendilab/DI-engine)
- **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/td3.html)
- **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-TD3/blob/main/policy_config.py)
- **Demo:** [video](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-TD3/blob/main/replay.mp4)
<!-- Provide the size information for the model. -->
- **Parameters total size:** 57.52 KB
- **Last Update Date:** 2023-04-17
## Environments
<!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. -->
- **Benchmark:** OpenAI/Gym/Box2d
- **Task:** LunarLander-v2
- **Gym version:** 0.25.1
- **DI-engine version:** v0.4.7
- **PyTorch version:** 1.7.1
- **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html)
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 25
| null |
---
license: mit
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: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8619104375957773
---
<!-- 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: 0.1370
- F1: 0.8619
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2582 | 1.0 | 525 | 0.1547 | 0.8225 |
| 0.126 | 2.0 | 1050 | 0.1362 | 0.8450 |
| 0.0793 | 3.0 | 1575 | 0.1370 | 0.8619 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.11.0
|
Chan/distilroberta-base-finetuned-wikitext2
|
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| 0
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-kl_1_07-hs_cn
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. -->
# gpt2-kl_1_07-hs_cn
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5400
## 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: 4
- seed: 21
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 74.3984 | 0.02 | 10 | 69.5824 |
| 46.8803 | 0.04 | 20 | 32.9798 |
| 14.3833 | 0.06 | 30 | 10.6415 |
| 7.575 | 0.08 | 40 | 4.6376 |
| 3.743 | 0.1 | 50 | 2.0118 |
| 2.2075 | 0.12 | 60 | 1.0526 |
| 1.7704 | 0.14 | 70 | 0.9221 |
| 1.6742 | 0.16 | 80 | 0.7326 |
| 1.6277 | 0.18 | 90 | 0.6323 |
| 1.5239 | 0.2 | 100 | 0.6305 |
| 1.4315 | 0.22 | 110 | 0.6303 |
| 1.4175 | 0.24 | 120 | 0.6087 |
| 1.4311 | 0.26 | 130 | 0.6332 |
| 1.551 | 0.28 | 140 | 0.5729 |
| 1.594 | 0.3 | 150 | 0.5792 |
| 1.4637 | 0.32 | 160 | 0.5718 |
| 1.3139 | 0.34 | 170 | 0.5715 |
| 1.5152 | 0.36 | 180 | 0.5667 |
| 1.5807 | 0.38 | 190 | 0.5628 |
| 1.3656 | 0.4 | 200 | 0.5642 |
| 1.622 | 0.42 | 210 | 0.5629 |
| 1.5337 | 0.44 | 220 | 0.5561 |
| 1.3122 | 0.46 | 230 | 0.5596 |
| 1.4214 | 0.48 | 240 | 0.5549 |
| 1.3091 | 0.5 | 250 | 0.5594 |
| 1.408 | 0.52 | 260 | 0.5501 |
| 1.3621 | 0.54 | 270 | 0.5445 |
| 1.472 | 0.56 | 280 | 0.5457 |
| 1.3257 | 0.58 | 290 | 0.5445 |
| 1.2563 | 0.6 | 300 | 0.5443 |
| 1.451 | 0.62 | 310 | 0.5410 |
| 1.5655 | 0.64 | 320 | 0.5384 |
| 1.3168 | 0.66 | 330 | 0.5416 |
| 1.3216 | 0.68 | 340 | 0.5391 |
| 1.3135 | 0.7 | 350 | 0.5400 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.11.0+cu113
- Datasets 2.11.0
- Tokenizers 0.12.1
|
Chun/w-en2zh-mtm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 7
| null |
---
tags:
- generated_from_keras_callback
model-index:
- name: layoutlm-funsd-sequence-tf
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. -->
# layoutlm-funsd-sequence-tf
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2348
- Validation Loss: 0.6737
- Train Overall Precision: 0.7356
- Train Overall Recall: 0.7998
- Train Overall F1: 0.7663
- Train Overall Accuracy: 0.8220
- Epoch: 7
## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.7150 | 1.4139 | 0.2373 | 0.2860 | 0.2594 | 0.4954 | 0 |
| 1.1803 | 0.9205 | 0.5676 | 0.6322 | 0.5981 | 0.7008 | 1 |
| 0.7884 | 0.7100 | 0.6202 | 0.7250 | 0.6685 | 0.7735 | 2 |
| 0.5877 | 0.6476 | 0.6689 | 0.7662 | 0.7142 | 0.7942 | 3 |
| 0.4490 | 0.6179 | 0.7133 | 0.8078 | 0.7576 | 0.8066 | 4 |
| 0.3746 | 0.6305 | 0.7176 | 0.7878 | 0.7510 | 0.8129 | 5 |
| 0.3082 | 0.6924 | 0.7163 | 0.8018 | 0.7566 | 0.7937 | 6 |
| 0.2348 | 0.6737 | 0.7356 | 0.7998 | 0.7663 | 0.8220 | 7 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Chun/w-en2zh-otm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MBartForConditionalGeneration"
],
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},
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}
| 7
| null |
---
license: gpl-3.0
tags:
- text2text-generation
pipeline_tag: text2text-generation
language:
- zh
- en
---
Considering LLaMA's license constraints, the model is for research and learning only.
Please strictly respect LLaMA's usage policy. We are not allowed to publish weights for LLaMA, of course, even finetuned, but there is no problem publishing the difference, a patch that we suggest to apply to the files.
The encryption is a simple XOR between files, ensuring that only the people that have access to the original weights (from completely legal sources, of course) can transform them into finetuned weights.
You can find the decrypt code on https://github.com/LianjiaTech/BELLE/tree/main/models .
# Model Card for Model ID
## Welcome
If you find this model helpful, please *like* this model and star us on https://github.com/LianjiaTech/BELLE !
## Model description
We release our model described in the paper
[Towards Better Instruction Following Language Models for Chinese](https://github.com/LianjiaTech/BELLE/blob/main/docs/Towards%20Better%20Instruction%20Following%20Language%20Models%20for%20Chinese.pdf)
This model achieves the best performance comparing other instruction-following models with a score of 0.762 on our evaluation set.
## Download, Convert & Check
1. After you git clone this model
```
md5sum ./*
29db882bdab3131ef05943ee8ba82e2c ./config.json.6375ff434583e14cfc1fd45f9f599ddb9c689cb9b8c542d427dc6d5dc1059037.enc
f9b33d359f17a437f6c24b4de6f2272e ./generation_config.json.fd7ff399e5568cc21a0a8414f43df88ef7c424995b9b97a90563165d2cf79efd.enc
794e28fff16ef8c3fe9e48e3aa6ccf3a ./pytorch_model-00001-of-00002.bin.b552ebc4dd499812cfe1e45ffcaad0ee93851ef83df95eb4f824be53b25e5531.enc
1ab136a4489016c3004e3f04c438f268 ./pytorch_model-00002-of-00002.bin.45adb5c7b91f81b2c03c913f2e52487a0e22663e088063b699c6a903101b7968.enc
0d6db7f247a51589f3dd6d08dbfe64ce ./pytorch_model.bin.index.json.4f08b269e18619675bc3fd62f6efb3a8d59f9d54fa50f5625d0bba7adabaf90e.enc
34696bfce7b27548cfc2410e2b55762e ./special_tokens_map.json.96bdbb8504d9967606e5f661ccc7cbbac44a3661af863a7a58614670a0ccab33.enc
6014cf2235521f974c8d9fb69b6cf07e ./tokenizer_config.json.7078cc180b3d35e7ccd06b49ede4a7fef85f2572bda40c1fe2fc8f9ab25418d3.enc
56724a79091f3d1877cca65c6412d646 ./tokenizer.model.0b716a618c9e7c45648f91d997431eba3b0ff111b17ce7b777280ed771a49f95.enc
```
2. Decrypt the files using the scripts in https://github.com/LianjiaTech/BELLE/tree/main/models
You can use the following command in Bash.
Please replace "/path/to_encrypted" with the path where you stored your encrypted file,
replace "/path/to_original_llama_7B" with the path where you stored your original llama7B file,
and replace "/path/to_finetuned_model" with the path where you want to save your final trained model.
```bash
mkdir /path/to_finetuned_model
for f in "/path/to_encrypted"/*; \
do if [ -f "$f" ]; then \
python3 decrypt.py "$f" "/path/to_original_llama_7B/consolidated.00.pth" "/path/to_finetuned_model/"; \
fi; \
done
```
After executing the aforementioned command, you will obtain the following files.
```
./config.json
./generation_config.json
./pytorch_model-00001-of-00002.bin
./pytorch_model-00002-of-00002.bin
./pytorch_model.bin.index.json
./special_tokens_map.json
./tokenizer_config.json
./tokenizer.model
```
3. Check md5sum
You can verify the integrity of these files by performing an MD5 checksum to ensure their complete recovery.
Here are the MD5 checksums for the relevant files:
```
md5sum ./*
139cb9dc0065bd878b277860c70add74 ./config.json
2917a1cafb895cf57e746cfd7696bfe5 ./generation_config.json
2f6cce3296b6bfeb8beb1629bf07dfe9 ./pytorch_model-00001-of-00002.bin
8fe5b4ad70788b3a6086ef28709a8730 ./pytorch_model-00002-of-00002.bin
e5385004e4876ea6b93d6126e845a82f ./pytorch_model.bin.index.json
15f7a943faa91a794f38dd81a212cb01 ./special_tokens_map.json
08f6f621dba90b2a23c6f9f7af974621 ./tokenizer_config.json
6ffe559392973a92ea28032add2a8494 ./tokenizer.model
```
## Use model
Please note that the input should be formatted as follows in both **training** and **inference**.
``` python
Human: {input} \n\nAssistant:
```
In order to load BELLE-LLAMA-7B-2M-enc with huggingface transformers, please install the main version, as the latest stable version doesn't support LLAMA (as of March 26, 2023).
``` python
pip install git+https://github.com/huggingface/transformers
```
After you decrypt the files, BELLE-LLAMA-7B-2M can be easily loaded with LlamaForCausalLM.
``` python
from transformers import LlamaForCausalLM, AutoTokenizer
import torch
ckpt = '/path/to_finetuned_model/'
device = torch.device('cuda')
model = LlamaForCausalLM.from_pretrained(ckpt, device_map='auto', low_cpu_mem_usage=True)
tokenizer = AutoTokenizer.from_pretrained(ckpt)
prompt = "Human: 写一首中文歌曲，赞美大自然 \n\nAssistant: "
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
generate_ids = model.generate(input_ids, max_new_tokens=300, do_sample = True, top_k = 30, top_p = 0.85, temperature = 0.5，repetition_penalty=1.2, eos_token_id=2, bos_token_id=1, pad_token_id=0)
output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
response = output[len(prompt):]
print(response)
```
## Limitations
There still exists a few issues in the model trained on current base model and data:
1. The model might generate factual errors when asked to follow instructions related to facts.
2. Occasionally generates harmful responses since the model still struggles to identify potential harmful instructions.
3. Needs improvements on reasoning and coding.
Since the model still has its limitations, we require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
## Citation
Please cite our paper and github when using our code, data or model.
```
@misc{ji2023better,
title={Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation},
author={Yunjie Ji and Yan Gong and Yong Deng and Yiping Peng and Qiang Niu and Baochang Ma and Xiangang Li},
year={2023},
eprint={2304.07854},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{BELLE,
author = {Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Baochang Ma, Xiangang Li},
title = {BELLE: Be Everyone's Large Language model Engine},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
}
```
|
Chun/w-zh2en-mto
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 7
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: try-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
- name: F1
type: f1
value: 0.880794701986755
---
<!-- 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. -->
# try-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2903
- Accuracy: 0.88
- F1: 0.8808
## 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
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Cinnamon/electra-small-japanese-discriminator
|
[
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null |
{
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"ElectraForPreTraining"
],
"model_type": "electra",
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}
| 419
| null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: irgallard/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Cinnamon/electra-small-japanese-generator
|
[
"pytorch",
"electra",
"fill-mask",
"ja",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"ElectraForMaskedLM"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
| 19
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### doggoart4 Dreambooth model trained by brunneis 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:
|
Ciruzzo/DialoGPT-medium-harrypotter
|
[] | null |
{
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}
}
| 0
| null |
Access to model CatVat/DARTT is restricted and you are not in the authorized list. Visit https://huggingface.co/CatVat/DARTT to ask for access.
|
Ciruzzo/DialoGPT-small-hattypotter
|
[] | null |
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}
| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: anercorpDataset_v2.0
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. -->
# anercorpDataset_v2.0
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3549
- Precision: 0.6878
- Recall: 0.6011
- F1: 0.6415
- Accuracy: 0.9317
## 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
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2867 | 1.0 | 7057 | 0.4187 | 0.5231 | 0.4992 | 0.5109 | 0.9111 |
| 0.2945 | 2.0 | 14114 | 0.3420 | 0.6300 | 0.5616 | 0.5938 | 0.9246 |
| 0.2098 | 3.0 | 21171 | 0.3549 | 0.6878 | 0.6011 | 0.6415 | 0.9317 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
CleveGreen/FieldClassifier
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
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}
| 34
| null |
---
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: 257.82 +/- 32.40
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
...
```
|
CleveGreen/FieldClassifier_v2
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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}
}
}
| 46
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### mann-e_4-2-base Dreambooth model trained by Muhammadreza 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:
|
CleveGreen/FieldClassifier_v2_gpt
|
[
"pytorch",
"gpt2",
"text-classification",
"transformers"
] |
text-classification
|
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| 26
| 2023-04-17T10:52:59Z
|
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-kl_1_03-hs_cn
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. -->
# gpt2-kl_1_03-hs_cn
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5831
## 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: 4
- seed: 21
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 73.6964 | 0.02 | 10 | 69.5849 |
| 46.2529 | 0.04 | 20 | 32.9743 |
| 13.6872 | 0.06 | 30 | 10.6434 |
| 6.9606 | 0.08 | 40 | 4.2609 |
| 3.0304 | 0.1 | 50 | 2.0214 |
| 1.5531 | 0.12 | 60 | 1.0653 |
| 1.2219 | 0.14 | 70 | 0.9094 |
| 1.123 | 0.16 | 80 | 0.8057 |
| 0.9086 | 0.18 | 90 | 0.6375 |
| 0.8539 | 0.2 | 100 | 0.6410 |
| 0.7491 | 0.22 | 110 | 0.5954 |
| 0.7632 | 0.24 | 120 | 0.5803 |
| 0.7792 | 0.26 | 130 | 0.5807 |
| 0.774 | 0.28 | 140 | 0.5821 |
| 0.7971 | 0.3 | 150 | 0.5831 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.11.0+cu113
- Datasets 2.11.0
- Tokenizers 0.12.1
|
CleveGreen/JobClassifier_v2
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 37
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-bottom_cleaned_data
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: 0.9726247987117552
---
<!-- 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-bottom_cleaned_data
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: 0.0839
- Accuracy: 0.9726
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4444 | 1.0 | 174 | 0.2271 | 0.9163 |
| 0.3518 | 2.0 | 349 | 0.2449 | 0.9034 |
| 0.225 | 3.0 | 523 | 0.1325 | 0.9501 |
| 0.2195 | 4.0 | 698 | 0.1024 | 0.9549 |
| 0.2627 | 5.0 | 872 | 0.1046 | 0.9630 |
| 0.142 | 6.0 | 1047 | 0.0839 | 0.9726 |
| 0.1516 | 7.0 | 1221 | 0.0918 | 0.9630 |
| 0.1498 | 8.0 | 1396 | 0.0780 | 0.9726 |
| 0.1189 | 9.0 | 1570 | 0.0721 | 0.9662 |
| 0.1594 | 9.97 | 1740 | 0.0668 | 0.9726 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Cloudy/DialoGPT-CJ-large
|
[
"pytorch",
"conversational"
] |
conversational
|
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| 1
| 2023-04-17T10:56:28Z
|
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - sayakpaul/dreambooth
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
|
ClydeWasTaken/DialoGPT-small-joshua
|
[
"conversational"
] |
conversational
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Regression_bert_1500
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. -->
# Regression_bert_1500
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: 0.2224
- Train Mae: 0.4296
- Train Mse: 0.2717
- Train R2-score: 0.8508
- Validation Loss: 0.1846
- Validation Mae: 0.4542
- Validation Mse: 0.2649
- Validation R2-score: 0.7458
- Epoch: 39
## 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': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.2245 | 0.4557 | 0.2970 | 0.6980 | 0.1019 | 0.3869 | 0.1805 | 0.6789 | 0 |
| 0.1102 | 0.3741 | 0.1848 | 0.8480 | 0.0839 | 0.2912 | 0.1209 | 0.8311 | 1 |
| 0.0845 | 0.3575 | 0.1689 | 0.2888 | 0.0620 | 0.3294 | 0.1463 | 0.8421 | 2 |
| 0.0554 | 0.3231 | 0.1366 | 0.7118 | 0.0613 | 0.2950 | 0.1305 | 0.7960 | 3 |
| 0.0481 | 0.3071 | 0.1276 | 0.8265 | 0.0516 | 0.3112 | 0.1357 | 0.8145 | 4 |
| 0.0461 | 0.2876 | 0.1168 | 0.8131 | 0.0524 | 0.2770 | 0.1097 | 0.8434 | 5 |
| 0.0454 | 0.2809 | 0.1140 | 0.7029 | 0.0502 | 0.3041 | 0.1340 | 0.8063 | 6 |
| 0.0448 | 0.2930 | 0.1212 | 0.6336 | 0.0514 | 0.2761 | 0.1143 | 0.8154 | 7 |
| 0.0446 | 0.2728 | 0.1079 | 0.8086 | 0.0532 | 0.2696 | 0.1145 | 0.8033 | 8 |
| 0.0450 | 0.2733 | 0.1086 | 0.6564 | 0.0504 | 0.2590 | 0.1033 | 0.8335 | 9 |
| 0.0792 | 0.3267 | 0.1471 | 0.5533 | 0.0848 | 0.3585 | 0.1709 | 0.7364 | 10 |
| 0.0673 | 0.3235 | 0.1407 | 0.6299 | 0.0621 | 0.3478 | 0.1549 | 0.7915 | 11 |
| 0.0571 | 0.3151 | 0.1347 | 0.6762 | 0.0729 | 0.2784 | 0.1203 | 0.8483 | 12 |
| 0.0528 | 0.2797 | 0.1104 | 0.7393 | 0.0642 | 0.2901 | 0.1291 | 0.7939 | 13 |
| 0.0490 | 0.2930 | 0.1187 | 0.6853 | 0.0821 | 0.2995 | 0.1446 | 0.7879 | 14 |
| 0.0566 | 0.2789 | 0.1120 | 0.7938 | 0.0878 | 0.3242 | 0.1589 | 0.8498 | 15 |
| 0.1926 | 0.4228 | 0.2638 | 0.6635 | 0.2201 | 0.4528 | 0.2705 | 0.6836 | 16 |
| 0.1921 | 0.4231 | 0.2458 | 0.7452 | 0.1018 | 0.3940 | 0.1900 | 0.7454 | 17 |
| 0.1288 | 0.4126 | 0.2154 | 0.6279 | 0.1035 | 0.3943 | 0.1991 | 0.7491 | 18 |
| 0.1897 | 0.4375 | 0.2617 | 0.8354 | 0.2664 | 0.4703 | 0.3447 | 0.7632 | 19 |
| 0.2095 | 0.4337 | 0.2676 | 0.8187 | 0.2730 | 0.4652 | 0.3425 | 0.8093 | 20 |
| 0.2682 | 0.4555 | 0.3358 | 0.7789 | 0.2851 | 0.4590 | 0.3518 | 0.8013 | 21 |
| 0.2416 | 0.4616 | 0.3213 | 0.7117 | 0.2466 | 0.4999 | 0.3667 | 0.6966 | 22 |
| 0.1798 | 0.4569 | 0.2737 | 0.5086 | 0.0945 | 0.4097 | 0.2023 | 0.7475 | 23 |
| 0.1361 | 0.4186 | 0.2140 | 0.5820 | 0.1181 | 0.4135 | 0.2075 | 0.7951 | 24 |
| 0.1660 | 0.4334 | 0.2322 | 0.4041 | 0.2522 | 0.4329 | 0.3019 | 0.7785 | 25 |
| 0.3046 | 0.4603 | 0.3555 | 0.7796 | 0.2886 | 0.4894 | 0.3587 | 0.7229 | 26 |
| 0.2843 | 0.4723 | 0.3491 | 0.7267 | 0.2937 | 0.4712 | 0.3616 | 0.7308 | 27 |
| 0.2793 | 0.4531 | 0.3379 | 0.7893 | 0.2952 | 0.4762 | 0.3738 | 0.7540 | 28 |
| 0.2410 | 0.4286 | 0.2899 | 0.8189 | 0.2358 | 0.4379 | 0.2932 | 0.7727 | 29 |
| 0.2270 | 0.4203 | 0.2691 | 0.8224 | 0.2361 | 0.4466 | 0.2992 | 0.7742 | 30 |
| 0.2260 | 0.4341 | 0.2776 | 0.8716 | 0.2290 | 0.4345 | 0.2885 | 0.7809 | 31 |
| 0.2198 | 0.4225 | 0.2629 | 0.7666 | 0.2303 | 0.4392 | 0.2895 | 0.7775 | 32 |
| 0.2247 | 0.4245 | 0.2709 | 0.8193 | 0.2291 | 0.4424 | 0.2921 | 0.7813 | 33 |
| 0.2241 | 0.4276 | 0.2704 | 0.6859 | 0.2280 | 0.4430 | 0.2960 | 0.7879 | 34 |
| 0.2239 | 0.4292 | 0.2709 | 0.8308 | 0.2314 | 0.4445 | 0.2893 | 0.7641 | 35 |
| 0.2174 | 0.4251 | 0.2637 | 0.7179 | 0.2354 | 0.4565 | 0.3017 | 0.7566 | 36 |
| 0.2211 | 0.4257 | 0.2686 | 0.7896 | 0.2409 | 0.4467 | 0.3014 | 0.7683 | 37 |
| 0.2321 | 0.4218 | 0.2745 | 0.8021 | 0.2413 | 0.4525 | 0.3062 | 0.7663 | 38 |
| 0.2224 | 0.4296 | 0.2717 | 0.8508 | 0.1846 | 0.4542 | 0.2649 | 0.7458 | 39 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
CoShin/XLM-roberta-large_ko_en_nil_sts
|
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| 0
| null |
---
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: 497.50 +/- 204.55
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 anilkumar2444 -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 anilkumar2444 -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 anilkumar2444
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 50000),
('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)])
```
|
CoachCarter/distilbert-base-uncased-finetuned-squad
|
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| 0
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.75 +/- 0.39
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
CoachCarter/distilbert-base-uncased
|
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| 0
| null |
---
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: 273.59 +/- 18.54
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
...
```
|
CodeDanCode/CartmenBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
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}
| 14
| null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="RagnaChris/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CodeNinja1126/bert-p-encoder
|
[
"pytorch"
] | null |
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}
}
| 3
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-V3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: -92.27 +/- 26.64
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="RagnaChris/Taxi-V3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CodeNinja1126/koelectra-model
|
[] | null |
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}
| 0
| null |
---
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- anime
- diffusers
license: creativeml-openrail-m
duplicated_from: haor/Evt_V2
---
# Evt_V2
Based on animefull-latest, fine-tuned using a training set of 15000 images (7700 flipped). Most of the training set uses [pixiv_AI_crawler](https://github.com/7eu7d7/pixiv_AI_crawler) to filter the pixiv daily ranking, and then mixes some nsfw animation images.
### Examples
```
best quality, illustration,highly detailed,1girl,upper body,beautiful detailed eyes, medium_breasts, long hair,grey hair, grey eyes, curly hair, bangs,empty eyes,expressionless, ((masterpiece)),twintails,beautiful detailed sky, beautiful detailed water, cinematic lighting, dramatic angle,((back to the viewer)),(an extremely delicate and beautiful),school uniform,black ribbon,light smile,
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,artist name,bad feet
Steps: 40, Sampler: Euler a, CFG scale: 7, Clip skip: 2
*evt_bs6_ema is the first version of evt
```
```
{Masterpiece, Kaname_Madoka, tall and long double tails, well rooted hair, (pink hair), pink eyes, crossed bangs, ojousama, jk, thigh bandages, wrist cuffs, (pink bow: 1.2)}, plain color, sketch, masterpiece, high detail, masterpiece portrait, best quality, ray tracing, {:<, look at the edge}
Negative prompt: ((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)),extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((bad proportions))), ((extra limbs)), (((deformed))), (((disfigured))), cloned face, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), too many fingers, (((long neck))), (((low quality))), normal quality, blurry, bad feet, text font ui, ((((worst quality)))), anatomical nonsense, (((bad shadow))), unnatural body, liquid body, 3D, 3D game, 3D game scene, 3D character, bad hairs, poorly drawn hairs, fused hairs, big muscles, bad face, extra eyes, furry, pony, mosaic, disappearing calf, disappearing legs, extra digit, fewer digit, fused digit, missing digit, fused feet, poorly drawn eyes, big face, long face, bad eyes, thick lips, obesity, strong girl, beard，Excess legs
Steps: 40, Sampler: Euler a, CFG scale: 6,Clip skip: 2
```
|
CodeNinja1126/test-model
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
| 24
| null |
---
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- anime
- diffusers
license: creativeml-openrail-m
duplicated_from: haor/Evt_V3
---
# Evt_V3
Based on Evt_V2 with 20 epochs fine-tuning using 35467 images
### Examples
```
best quality, illustration,highly detailed,1girl,upper body,beautiful detailed eyes, medium_breasts, long hair,grey hair, grey eyes, curly hair, bangs,empty eyes,expressionless, ((masterpiece)),twintails,beautiful detailed sky, beautiful detailed water, cinematic lighting, dramatic angle,((back to the viewer)),(an extremely delicate and beautiful),school uniform,black ribbon,light smile
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,artist name,bad feet
Steps: 40, Sampler: Euler a, CFG scale: 6, Clip skip: 2
```
```
masterpiece, best quality, {best quality}, {{masterpiece}}, {highres}, original, extremely detailed wallpaper, 1girl,{an extremely delicate and beautiful}, {{angle}} , hair flower, illustration, school uniform, sunlight, detailed eyes, lily, white wings, ((halo)), silver hair,
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name,bad feet
Steps: 40, Sampler: Euler a, CFG scale: 6,Clip skip: 2
```
```
{{best quality}}, {{masterpiece}}, {{ultra-detailed}}, {illustration}, {detailed light}, {an extremely delicate and beautiful}, a girl, {beautiful detailed eyes}, stars in the eyes, messy floating hair, colored inner hair, Starry sky adorns hair, depth of field
Negative prompt: lowres, bad anatomy, text, error, extra digit, fewer digits, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, {blurry:1.1}, missing arms
Steps: 40, Sampler: Euler a, CFG scale: 7, Clip skip: 2
```
|
CodeNinja1126/xlm-roberta-large-kor-mrc
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
}
| 8
| null |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
duplicated_from: haor/Evt_V4-preview
---
# Evt_V4-preview
EVT series is an experimental project for finetune with large datasets on animation style model.
Evt_V4 uses a larger dataset than before, and its cosine similarity with ACertainty reaches 85%.
It may behave differently from other models, hope you enjoy it.
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or FLAX/JAX.
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "haor/Evt_V4-preview"
branch_name= "main"
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision=branch_name, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "1girl"
image = pipe(prompt).images[0]
image.save("./1girl.png")
```
## Examples
**Prompt1:**
```
1girl in black serafuku standing in a field solo, food, fruit, lemon, bubble, planet, moon, orange \(fruit\), lemon slice, leaf, fish, orange slice, by (tabi:1.25), spot color, looking at viewer, closeup cowboy shot
Negative prompt: (bad:0.81), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:0.81), (speech:0.81), (worst:0.81), (blush:0.9), 2koma, 3koma, 4koma, collage, lipstick
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2285895007, Size: 512x1152, Denoising strength: 0.7, Clip skip: 2
```
**Prompt2:**
```
{Masterpiece, Kaname_Madoka, tall and long double tails, well rooted hair, (pink hair), pink eyes, crossed bangs, ojousama, jk, thigh bandages, wrist cuffs, (pink bow: 1.2)}, plain color, sketch, masterpiece, high detail, masterpiece portrait, best quality, ray tracing, {:<, look at the edge}
Negative prompt: ((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)),extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((bad proportions))), ((extra limbs)), (((deformed))), (((disfigured))), cloned face, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), too many fingers, (((long neck))), (((low quality))), normal quality, blurry, bad feet, text font ui, ((((worst quality)))), anatomical nonsense, (((bad shadow))), unnatural body, liquid body, 3D, 3D game, 3D game scene, 3D character, bad hairs, poorly drawn hairs, fused hairs, big muscles, bad face, extra eyes, furry, pony, mosaic, disappearing calf, disappearing legs, extra digit, fewer digit, fused digit, missing digit, fused feet, poorly drawn eyes, big face, long face, bad eyes, thick lips, obesity, strong girl, beard，Excess legs
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2468255263, Size: 512x1152, Denoising strength: 0.7, Clip skip: 2
```
## Training
base model:[ACertainty](https://huggingface.co/JosephusCheung/ACertainty)
Trained for 10 epochs using around 550k anime-style images(pixiv and yandere).
Resolution: 512
UCG:0.1
Use arb:True
Trainer:[Mikubill/naifu-diffusion](https://github.com/Mikubill/naifu-diffusion)
```
arb:
enabled: true
debug: false
base_res: [512, 512]
max_size: [768, 512]
divisible: 64
max_ar_error: 4
min_dim: 256
dim_limit: 1024
```
```
scheduler:
name: diffusers.DDIMScheduler
params:
beta_end: 0.012
beta_schedule: "scaled_linear"
beta_start: 0.00085
clip_sample: false
num_train_timesteps: 1000
set_alpha_to_one: false
steps_offset: 1
trained_betas: null
optimizer:
name: bitsandbytes.optim.AdamW8bit
params:
lr: 2e-6
weight_decay: 5e-2
eps: 1e-7
lr_scheduler:
name: torch.optim.lr_scheduler.CosineAnnealingWarmRestarts
warmup:
enabled: true
init_lr: 2e-8
num_warmup: 50
strategy: "cos"
params:
T_0: 5
T_mult: 1
eta_min: 6e-7
last_epoch: -1
```
Spent about 300 V100 GPU hours.
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
CoderBoy432/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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}
| 11
| null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: andyleow/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CoderEFE/DialoGPT-marxbot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"has_space"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
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},
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}
}
| 11
| null |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
duplicated_from: haor/Evt_M
---
# Evt_M
Evt_M is a model derived from Evt_V4 EP06.
It retains the characteristics of Evt_V4, and the batch generation of images with the same set of parameters is no longer rigid and monotonous, and has more possibilities.
## Examples
**Prompt1:**
```
{Masterpiece, Kaname_Madoka, tall and long double tails, well rooted hair, (pink hair), pink eyes, crossed bangs, ojousama, jk, thigh bandages, wrist cuffs, (pink bow: 1.2)}, plain color, sketch, masterpiece, high detail, masterpiece portrait, best quality, ray tracing, {:<, look at the edge}
Negative prompt: ((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)),extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((bad proportions))), ((extra limbs)), (((deformed))), (((disfigured))), cloned face, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), too many fingers, (((long neck))), (((low quality))), normal quality, blurry, bad feet, text font ui, ((((worst quality)))), anatomical nonsense, (((bad shadow))), unnatural body, liquid body, 3D, 3D game, 3D game scene, 3D character, bad hairs, poorly drawn hairs, fused hairs, big muscles, bad face, extra eyes, furry, pony, mosaic, disappearing calf, disappearing legs, extra digit, fewer digit, fused digit, missing digit, fused feet, poorly drawn eyes, big face, long face, bad eyes, thick lips, obesity, strong girl, beard，Excess legs
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Clip skip: 2
```
**Prompt2:**
```
best quality, illustration,highly detailed,1girl,upper body,beautiful detailed eyes, medium_breasts, long hair,grey hair, grey eyes, curly hair, bangs,empty eyes,expressionless, ((masterpiece)),twintails,beautiful detailed sky, beautiful detailed water, cinematic lighting, dramatic angle,((back to the viewer)),(an extremely delicate and beautiful),school uniform,black ribbon,light smile,
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,artist name,bad feet
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Clip skip: 2
```
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
CoffeeAddict93/gpt2-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
}
}
| 6
| null |
---
license: apache-2.0
---
# Lighting ASR中文语音识别模型
## 环境依赖
```
- Python3.7+
- PyTorch 1.8.1+
- torchaudio
- soundfile
- librosa
```
## 安装[lighting-asr](https://github.com/gaochangfeng/lighting-asr.git)
### 下载源代码并添加到python路径
```
export PYTHONPATH=/path/to/lasrfolder/:$PYTHONPATH
```
### 源文件安装
```
git clone https://github.com/gaochangfeng/lighting-asr.git
cd lighting-asr
python setup.py install
```
### PIP 安装
```
pip install git+https://github.com/gaochangfeng/lighting-asr.git
```
## 使用
```
from lasr.process.asrprocess import ASRProcess
train_config="hparams.yaml"
decode_config="decode.yaml"
model_path="model.ckpt"
asrpipeline = ASRProcess(
train_config=train_config,
decode_config=decode_config,
model_path=model_path
)
token, text = asrpipeline("test.wav")
print(token)
print(text)
```
## 模型训练
参考https:https://github.com/gaochangfeng/lighting-asr.git
|
CoffeeAddict93/gpt2-medium-modest-proposal
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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}
| 7
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Coldestadam/Breakout_Mentors_SpongeBob_Model
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null,
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
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"early_stopping": null,
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}
}
}
| 10
| null |
---
license: mit
---
These are the models, configs, and logs for the migration of MBRL-lib from gym to Gymnasium.
The files in the `diff` folder correspond to env models trained after the migration.
|
ComCom/gpt2-large
|
[
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 1
| null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- jax-diffusers-event
inference: true
---
# controlnet- ioclab/control_v1u_sd15_illumination
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images in the following.
prompt: a woman sitting at a piano in a dark room with a window behind her and a window behind her, Atey Ghailan, anime art, a painting, neo-romanticism
prompt: a woman with horns and a demon face on her head, with her hands on her face, in front of a demon like background, Ayami Kojima, anime art, a manga drawing, space art
prompt: A girl with her head down, Sailor Moon
prompt: (masterpiece, best quality: 1.4), 1girl,detailed background, white crystal, crysal cluster,long hair,jewelry, earrings, necklace, crown, bride, white hair, halo,
prompt: (masterpiece, best quality: 1.4), 1girl,detailed background, white crystal, crysal cluster,long hair,jewelry, earrings, necklace, crown, bride, white hair, halo,
prompt: (masterpiece, best quality: 1.4), 1girl,detailed background, white crystal, crysal cluster,long hair,jewelry, earrings, necklace, crown, bride, white hair, halo,
prompt: (masterpiece, best quality: 1.4), 1girl,detailed background, white crystal, crysal cluster,long hair,jewelry, earrings, necklace, crown, bride, white hair, halo,
prompt: (masterpiece, best quality: 1.4), 1girl,detailed background, white crystal, crysal cluster,long hair,jewelry, earrings, necklace, crown, bride, white hair, halo,
|
ComCom-Dev/gpt2-bible-test
|
[] | null |
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| 0
| null |
---
license: other
---
初めてアップロードするので、間違ってるかもしれません
civitaiをのぞいてみたら、消えていたのでバックアップです。
Saimin Seishidou(催眠性指導) Style
I created Style Lora with the painting style of Saimin Seishidou, an animation created by the adult animation production company, ばにぃうぉ～か～.
The Anything 4.5 model was used for learning.
The recommended weight is 0.8~1 and simple eyes tag makes the eyes better drawn.
Since a lot of faces are included in the learning data, sometimes a head that does not fit the human body proportion may be drawn.
|
Connor-tech/bert_cn_finetuning
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
| 27
| null |
---
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: 274.50 +/- 31.50
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 RagnaChris -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 RagnaChris -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 RagnaChris
```
## 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', 50000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Contrastive-Tension/BERT-Base-CT-STSb
|
[
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 5
| null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -192.09 +/- 89.19
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'dvesely/ppo2-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Contrastive-Tension/BERT-Base-CT
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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},
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}
}
}
| 16
| null |
---
license: mit
widget:
- text: Tadbirkorlik – foyda olish <mask> faoliyat.
example_title: Example 1
---
|
Contrastive-Tension/RoBerta-Large-CT-STSb
|
[
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
| 5
| null |
---
license: apache-2.0
language:
- en
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Safvan CK
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a 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 Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a 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]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Cool/Demo
|
[] | null |
{
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}
| 0
| null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: andyleow/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CopymySkill/DialoGPT-medium-atakan
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 7
| 2023-04-17T12:04:36Z
|
---
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 |
| learning_rate | 0.0010000000474974513 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
|
Corvus/DialoGPT-medium-CaptainPrice-Extended
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
],
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}
| 7
| null |
---
tags:
- spacy
- text-classification
language:
- da
model-index:
- name: da_spacy_sentiment
results: []
---
| Feature | Description |
| --- | --- |
| **Name** | `da_spacy_sentiment` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.1,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `textcat` |
| **Components** | `tok2vec`, `textcat` |
| **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (3 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`textcat`** | `neutral`, `negative`, `positive` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `CATS_SCORE` | 82.58 |
| `CATS_MICRO_P` | 82.40 |
| `CATS_MICRO_R` | 82.40 |
| `CATS_MICRO_F` | 82.40 |
| `CATS_MACRO_P` | 81.24 |
| `CATS_MACRO_R` | 84.43 |
| `CATS_MACRO_F` | 82.58 |
| `CATS_MACRO_AUC` | 92.45 |
| `TOK2VEC_LOSS` | 39608.07 |
| `TEXTCAT_LOSS` | 913.24 |
|
CouchCat/ma_mlc_v7_distil
|
[
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"multi-label",
"license:mit"
] |
text-classification
|
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"DistilBertForSequenceClassification"
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}
| 29
| 2023-04-17T12:07:10Z
|
---
license: mit
tags:
- anime
- audio
- tts
- voice conversion
---
Origin (Generated From TTS):
<audio controls src="https://s3.amazonaws.com/moonup/production/uploads/62d3a59dc72c791b23918293/neVwV9PEc0gGylrEup2Kn.wav"></audio>
Converted (Using SoVits Chtholly-VC)
<audio controls src="https://s3.amazonaws.com/moonup/production/uploads/62d3a59dc72c791b23918293/oKNg3kVgAb7utyGCZa8f9.wav"></audio>
|
CouchCat/ma_ner_v6_distil
|
[
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] |
token-classification
|
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"DistilBertForTokenClassification"
],
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}
| 6
| 2023-04-17T12:09:20Z
|
---
tags:
- text-classification
language:
- it
widget:
- text: Mi chiamo Taras, sono felice di conoscerti!
datasets:
- Taraassss/autotrain-data-taras_es_sentiment_analysis
co2_eq_emissions:
emissions: 0.24907555356641484
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 50174120292
- CO2 Emissions (in grams): 0.2491
## Validation Metrics
- Loss: 0.816
- Accuracy: 0.647
- Macro F1: 0.637
- Micro F1: 0.647
- Weighted F1: 0.644
- Macro Precision: 0.643
- Micro Precision: 0.647
- Weighted Precision: 0.645
- Macro Recall: 0.635
- Micro Recall: 0.647
- Weighted Recall: 0.647
|
CouchCat/ma_sa_v7_distil
|
[
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
] |
text-classification
|
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"DistilBertForSequenceClassification"
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}
| 38
| null |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1576451816895975424/7pcCGPws_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Wayne</div>
<div style="text-align: center; font-size: 14px;">@lumber127</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Wayne.
| Data | Wayne |
| --- | --- |
| Tweets downloaded | 1155 |
| Retweets | 4 |
| Short tweets | 87 |
| Tweets kept | 1064 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/hgi1t5tf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lumber127's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jigqgk0o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jigqgk0o/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/lumber127')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Coyotl/DialoGPT-test3-arthurmorgan
|
[
"conversational"
] |
conversational
|
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| 0
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gbert-large-finetuned-cust
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. -->
# gbert-large-finetuned-cust
This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1846
## 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: 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8251 | 1.0 | 157 | 0.5204 |
| 0.508 | 2.0 | 314 | 0.3953 |
| 0.4009 | 3.0 | 471 | 0.3242 |
| 0.3587 | 4.0 | 628 | 0.3300 |
| 0.3276 | 5.0 | 785 | 0.3137 |
| 0.302 | 6.0 | 942 | 0.2826 |
| 0.2777 | 7.0 | 1099 | 0.2768 |
| 0.2609 | 8.0 | 1256 | 0.2726 |
| 0.244 | 9.0 | 1413 | 0.2660 |
| 0.2274 | 10.0 | 1570 | 0.2391 |
| 0.2132 | 11.0 | 1727 | 0.2353 |
| 0.2014 | 12.0 | 1884 | 0.2134 |
| 0.1835 | 13.0 | 2041 | 0.2278 |
| 0.1896 | 14.0 | 2198 | 0.2110 |
| 0.1974 | 15.0 | 2355 | 0.2132 |
| 0.1775 | 16.0 | 2512 | 0.1973 |
| 0.1715 | 17.0 | 2669 | 0.1941 |
| 0.1777 | 18.0 | 2826 | 0.2105 |
| 0.1741 | 19.0 | 2983 | 0.2127 |
| 0.1607 | 20.0 | 3140 | 0.1762 |
| 0.1562 | 21.0 | 3297 | 0.2095 |
| 0.1548 | 22.0 | 3454 | 0.1805 |
| 0.1534 | 23.0 | 3611 | 0.1852 |
| 0.1484 | 24.0 | 3768 | 0.1773 |
| 0.1473 | 25.0 | 3925 | 0.1759 |
| 0.1354 | 26.0 | 4082 | 0.1734 |
| 0.136 | 27.0 | 4239 | 0.1902 |
| 0.1306 | 28.0 | 4396 | 0.1769 |
| 0.1353 | 29.0 | 4553 | 0.1705 |
| 0.1368 | 30.0 | 4710 | 0.1846 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
CracklesCreeper/Piglin-Talks-Harry-Potter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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| 10
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: passport_demo
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. -->
# passport_demo
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Craig/mGqFiPhu
|
[
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
feature-extraction
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: my_awesome_mind_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_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6593
- Accuracy: 0.0619
## 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
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6472 | 0.0354 |
| No log | 1.87 | 7 | 2.6452 | 0.0708 |
| 2.6388 | 2.93 | 11 | 2.6492 | 0.0531 |
| 2.6388 | 4.0 | 15 | 2.6545 | 0.0796 |
| 2.6388 | 4.8 | 18 | 2.6562 | 0.0619 |
| 2.6292 | 5.87 | 22 | 2.6578 | 0.0708 |
| 2.6292 | 6.93 | 26 | 2.6594 | 0.0619 |
| 2.6231 | 8.0 | 30 | 2.6593 | 0.0619 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
CrayonShinchan/fine_tune_try_1
|
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| 0
| null |
---
license: mit
library_name: diffusers
---
# Mann-E 4.2 Merged
## Technical Information about the model
* Base Model : [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
* Merge : [mann-e/mann-e_4_rev-1-3](https://huggingface.co/mann-e/mann-e_4_rev-1-3)
* Merge amount : %70 fine-tuned SD 1.5 (or _Mann-E version 4.2 base_) and %30 of Mann-E 4.1.3 in order to get the old styles such as _Model Shoot_, _Elden Ring_, _Arcane_, _Analog Style_ and _GTA V Style_. Also this merge can be helpful for _Midjourney version 4_ style artwork as well.
### Training process
The code for pre-processing data and fine-tuning the model is available in [this repository](https://github.com/prp-e/mann-e_training) and you can run it on your own as well.
* Text encoder iterations : 1440 (number of pics times two in order to understand `mstyle` which can give the user a _Midjourney version 5_ vibe).
* Stable Diffusion iterations : 16000 iterations for one epoch
* Time: around 4 hours on a single T4 GPU.
|
Crisblair/Wkwk
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Humberto/MedicalArticlesClassificationModelMultiLabel
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. -->
# Humberto/MedicalArticlesClassificationModelMultiLabel
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: 1.4568
- Validation Loss: 1.4830
- Train Accuracy: 0.5
- Epoch: 2
## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 90, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.7905 | 1.7693 | 0.275 | 0 |
| 1.6592 | 1.5774 | 0.4667 | 1 |
| 1.4568 | 1.4830 | 0.5 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Crispy/dialopt-small-kratos
|
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| 0
| null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -158.14 +/- 71.18
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 500000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 2048
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'israel-avihail/Bereshit-PPO-LunarLander-v2'
'batch_size': 8192
'minibatch_size': 2048}
```
|
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