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
|
|---|---|---|---|---|---|---|
AmirBialer/amirbialer-Classifier
|
[] | null |
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| 0 | null |
---
license: cc-by-nc-nd-4.0
---
This model is a multi-class classifier, model fine-tuned using the model 'bert-base-uncased'.
It is built around a large corpus of Twitter users' metadata.
It filters the data into 3 main categories - (1) Non-ExpertUser (2) ExpertUser (3) Other.
The aim of this project was to find out whether a tweet belongs to an individual or not. And if it is, whether the person is an expert in the field of Security and Privacy.
Originally, the Model had 4 classes - where the 'Other' field was classified into 'Non-Person' (denoting accounts such as organizations)and 'Unknown'.
Since the main aim was to find out about whether a user is a non-expert user or not, the classes were reduced to 3 classes in this version 2.
The validation scores for the module were as follows
Accuracy = 0.93
<table>
<tr>
<th>Class</th>
<th>Precision</th>
<th>Recall</th>
<th>F1-Score</th>
</tr>
<tr>
<td>ExpertUser (0)</td>
<td>0.88</td>
<td>0.90</td>
<td>0.89</td>
</tr>
<tr>
<td><b>Non-ExpertUser (1)</b></td>
<td><b>0.95</b></td>
<td><b>0.97</b></td>
<td><b>0.96</b></td>
</tr>
<tr>
<td>Other (2)</td>
<td>0.85</td>
<td>0.78</td>
<td>0.81</td>
</tr>
</table>
<b>Paper:</b> The paper detailing how it was designed can be found here <a href="https://www.sciencedirect.com/science/article/pii/S016740482200400X">Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter</a>
<b>Please cite the paper if you use this model </b>:
Nandita Pattnaik, Shujun Li, and Jason R.C. Nurse. 2023.<br> Perspectives of non-expert users on cyber security and
privacy: An analysis of online discussions on Twitter.<br> Computers & Security 125 (2023), 103008. https://doi.org/10.1016/j.cose.2022.103008
|
Andranik/TestQA2
|
[
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
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| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9361948955916474
- name: Recall
type: recall
value: 0.9506900033658701
- name: F1
type: f1
value: 0.9433867735470942
- name: Accuracy
type: accuracy
value: 0.9865632542532525
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0608
- Precision: 0.9362
- Recall: 0.9507
- F1: 0.9434
- Accuracy: 0.9866
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0869 | 1.0 | 1756 | 0.0684 | 0.9180 | 0.9369 | 0.9274 | 0.9823 |
| 0.033 | 2.0 | 3512 | 0.0681 | 0.9264 | 0.9487 | 0.9374 | 0.9854 |
| 0.0178 | 3.0 | 5268 | 0.0608 | 0.9362 | 0.9507 | 0.9434 | 0.9866 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Andranik/TestQaV1
|
[
"pytorch",
"rust",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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| 4 | 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: 249.00 +/- 126.94
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 Erwanlbv -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 Erwanlbv -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 Erwanlbv
```
## 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)])
```
|
AndrewNLP/redditDepressionPropensityClassifiers
|
[] | null |
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| 0 | null |
---
license: unknown
language:
- pt
tags:
- legal
---
**How to use:**
```python
>>> from transformers import AutoTokenizer, AutoModelForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("mynoguti/BERTimbau_Legal")
>>> model = AutoModelForMaskedLM.from_pretrained("mynoguti/BERTimbau_Legal")
```
|
Andrey1989/mbart-finetuned-en-to-kk
|
[] | null |
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| 0 | null |
---
license: openrail
---
# Model Card for Model ID
这个模型是用来跑图的
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
这是一个一个一个融合模型
- ** Developedby:** yushui
- **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]
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
放到webui里就能用
## 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 [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### 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]
|
Andrey1989/mbert-finetuned-ner
|
[
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
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| 12 | 2023-02-06T13:22:15Z |
---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
### Run Configuration
{
"arch": "crnn_vgg16_bn",
"train_path": "doctr-train-40k",
"val_path": null,
"train_samples": 1000,
"val_samples": 20,
"font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf",
"min_chars": 1,
"max_chars": 12,
"name": null,
"epochs": 10,
"batch_size": 32,
"device": 0,
"input_size": 32,
"lr": 0.001,
"weight_decay": 0,
"workers": 4,
"resume": null,
"vocab": "turkish",
"test_only": false,
"show_samples": false,
"wb": false,
"push_to_hub": true,
"pretrained": true,
"sched": "cosine",
"amp": false,
"find_lr": false
}
|
Andrija/SRoBERTa-base
|
[
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 80 | null |
---
language:
- en
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- figfig/restaurant_order_test
metrics:
- wer
model-index:
- name: restaurant_test_model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: test_data
type: figfig/restaurant_order_test
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 78.57142857142857
---
<!-- 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. -->
# restaurant_test_model
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the test_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5435
- Wer: 78.5714
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 10.0 | 10 | 2.2425 | 7.1429 |
| No log | 20.0 | 20 | 0.6651 | 0.0 |
| 2.4375 | 30.0 | 30 | 0.5776 | 35.7143 |
| 2.4375 | 40.0 | 40 | 0.5435 | 78.5714 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ann2020/rubert-base-cased-sentence-finetuned-ner_tags
|
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}
}
| 0 | null |
---
license: gpl-2.0
datasets:
- pavanBuduguppa/abcdv1.1_nsp
language:
- en
library_name: transformers
tags:
- contactCenter
- chat
- digital
---
# 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 [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### 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]
|
AnnettJaeger/AnneJae
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
- medical
model-index:
- name: stop_reasons_classificator_multilabel
results: []
datasets:
- opentargets/clinical_trial_reason_to_stop
language:
- en
metrics:
- accuracy
library_name: transformers
widget:
- text: "Study stopped due to problems to recruit patients"
example_title: "Enrollment issues"
- text: "Efficacy endpoint unmet"
example_title: "Negative reasons"
- text: "Study stopped due to unexpected adverse effects"
example_title: "Safety"
- text: "Study paused due to the pandemic"
example_title: "COVID-19"
---
<!-- 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. -->
# Clinical trial stop reasons
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the task of classification of why a clinical trial has stopped early.
The dataset containing 3,747 manually curated reasons used for fine-tuning is available in the [Hub](https://huggingface.co/datasets/opentargets/clinical_trial_reason_to_stop).
More details on the model training are available in the GitHub project ([link](https://github.com/opentargets/stopReasons)) and in the associated publication (TBC).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| No log | 1.0 | 106 | 0.1824 | 0.9475 |
| No log | 2.0 | 212 | 0.1339 | 0.9630 |
| No log | 3.0 | 318 | 0.1109 | 0.9689 |
| No log | 4.0 | 424 | 0.0988 | 0.9741 |
| 0.1439 | 5.0 | 530 | 0.0943 | 0.9743 |
| 0.1439 | 6.0 | 636 | 0.0891 | 0.9763 |
| 0.1439 | 7.0 | 742 | 0.0899 | 0.9760 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Anomic/DialoGPT-medium-loki
|
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| 0 | null |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: translated_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. -->
# translated_model
This model is a fine-tuned version of [paust/pko-t5-small](https://huggingface.co/paust/pko-t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AnonARR/qqp-bert
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 38 | null |
---
language:
- en
pipeline_tag: token-classification
tags:
- medical
---
Protected health information (PHI) anonymization tool. Fine-tuned on the [i2b2 2014 training dataset](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989908/) from the pretrained `roberta-base` model.
Anonymizes according to the i2b2 2014 standard, including all ages, locations and organizations, dates (including lone years), names, professions, identification numbers, and contact information.
Model released with the approval of Informatics for Integrating Biology & the Bedside.
|
Anonymous/ReasonBERT-BERT
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 5 | null |
---
tags:
- nagatoro
- hayase nagatoro
- makima
- nazuna nanakusa
- lora
---
https://civitai.com/models/6060/nagatoro-hayase-ti NOT LORA. THATS TI
https://civitai.com/models/5662/nazuna-nanakusa-call-of-the-night-lora
https://civitai.com/models/5373/makima-chainsaw-man-lora
|
AnonymousNLP/pretrained-model-1
|
[
"pytorch",
"gpt2",
"transformers"
] | null |
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| 4 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-finetuned-ner-per-v5
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. -->
# bert-finetuned-ner-per-v5
This model is a fine-tuned version of [BeardedJohn/bert-ner-wikiann](https://huggingface.co/BeardedJohn/bert-ner-wikiann) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0136
- Validation Loss: 0.0009
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 313, '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 |
|:----------:|:---------------:|:-----:|
| 0.0136 | 0.0009 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AnonymousNLP/pretrained-model-2
|
[
"pytorch",
"gpt2",
"transformers"
] | null |
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| 4 | 2023-02-06T15:16:33Z |
---
language:
- en
pipeline_tag: token-classification
tags:
- medical
---
Protected health information (PHI) anonymization tool. Fine-tuned on the [i2b2 2014 training dataset](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989908/) from the pretrained `bert-base-cased` model.
Anonymizes according to the i2b2 2014 standard, including all ages, locations and organizations, dates (including lone years), names, professions, identification numbers, and contact information.
Model released with the approval of Informatics for Integrating Biology & the Bedside.
|
AnonymousSub/AR_EManuals-BERT
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 5 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# fathyshalab/massive-roberta
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/massive-roberta")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
AnonymousSub/AR_rule_based_roberta_hier_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 6 | null |
---
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
|
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 4 | null |
---
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a finetuned DeBERTav3 model from https://huggingface.co/sileod/deberta-v3-base-tasksource-nli.
# Model Details
This model was finetuned on policy data related to the rules laid out in the Sparrow paper by Deepmind.
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** SummerSigh
- **Model type:** DeBERTav3
- **Language(s) (NLP):** English
- **License:** apache-2.0
- **Finetuned from model:** https://huggingface.co/sileod/deberta-v3-base-tasksource-nli
# 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. -->
Given an input text, this model will output "KEPT" (0) or "BROKE" (1). KEPT indicates that the text keeps the policies finetuned in mind, while BROKE means that it broke one or more of the policies.
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
| 2 | 2023-02-06T16:05:27Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 2361.59 +/- 46.12
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
|
AnonymousSub/AR_specter
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 2 | 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: hectorjelly/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/SR_rule_based_roberta_hier_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
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}
}
}
| 5 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ru
datasets:
- lmqg/qg_ruquad
pipeline_tag: text2text-generation
tags:
- question generation
- answer extraction
widget:
- text: "generate question: Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов."
example_title: "Question Generation Example 1"
- text: "generate question: Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки."
example_title: "Question Generation Example 2"
- text: "generate question: Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами."
example_title: "Question Generation Example 3"
- text: "extract answers: <hl> в английском языке в нарицательном смысле применяется термин rapid transit (скоростной городской транспорт), однако употребляется он только тогда, когда по смыслу невозможно ограничиться названием одной конкретной системы метрополитена. <hl> в остальных случаях используются индивидуальные названия: в лондоне — london underground, в нью-йорке — new york subway, в ливерпуле — merseyrail, в вашингтоне — washington metrorail, в сан-франциско — bart и т. п. в некоторых городах применяется название метро (англ. metro) для систем, по своему характеру близких к метро, или для всего городского транспорта (собственно метро и наземный пассажирский транспорт (в том числе автобусы и трамваи)) в совокупности."
example_title: "Answer Extraction Example 1"
- text: "extract answers: Вопреки ожиданиям, объединение денежных систем республик не привело к уменьшению инфляции. Напротив, закдензнаки стали невероятно быстро обесцениваться, особенно в 1924 году. Для обеспечения денежного рынка приходилось увеличивать эмиссию закдензнаков и выпускать в оборот купюры невероятно больших номиналов. <hl> Так, в период с 1 января по 20 марта 1924 года были введены в оборот купюры достоинством 25 000 000 рублей, затем — 250 000 000 рублей. <hl> И, наконец, в апреле 1924 года были выпущены купюры миллиардного достоинства (в просторечии лимард)."
example_title: "Answer Extraction Example 2"
model-index:
- name: lmqg/mbart-large-cc25-ruquad-qg-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_ruquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 17.97
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 33.61
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 29.35
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 86.5
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 65.37
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
value: 60.14
- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
value: 62.21
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
value: 58.32
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
value: 42.22
- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
value: 43.58
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))
type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
value: 41.05
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 30.37
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 48.9
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 38.32
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 85.62
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 73.64
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 63.23
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 42.67
---
# Model Card of `lmqg/mbart-large-cc25-ruquad-qg-ae`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation and answer extraction jointly on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
- **Language:** ru
- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ru", model="lmqg/mbart-large-cc25-ruquad-qg-ae")
# model prediction
question_answer_pairs = model.generate_qa("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-ruquad-qg-ae")
# answer extraction
answer = pipe("generate question: Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")
# question generation
question = pipe("extract answers: <hl> в английском языке в нарицательном смысле применяется термин rapid transit (скоростной городской транспорт), однако употребляется он только тогда, когда по смыслу невозможно ограничиться названием одной конкретной системы метрополитена. <hl> в остальных случаях используются индивидуальные названия: в лондоне — london underground, в нью-йорке — new york subway, в ливерпуле — merseyrail, в вашингтоне — washington metrorail, в сан-франциско — bart и т. п. в некоторых городах применяется название метро (англ. metro) для систем, по своему характеру близких к метро, или для всего городского транспорта (собственно метро и наземный пассажирский транспорт (в том числе автобусы и трамваи)) в совокупности.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 86.5 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 34.01 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 26.99 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 21.9 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 17.97 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 29.35 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 65.37 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 33.61 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 60.14 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedF1Score (MoverScore) | 42.22 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (BERTScore) | 58.32 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (MoverScore) | 41.05 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (BERTScore) | 62.21 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (MoverScore) | 43.58 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 42.67 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| AnswerF1Score | 63.23 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| BERTScore | 85.62 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 44.16 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 39.37 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 34.9 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 30.37 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 38.32 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 73.64 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 48.9 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_ruquad
- dataset_name: default
- input_types: ['paragraph_answer', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 32
- epoch: 12
- batch: 2
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 32
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
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}
}
| 1 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.60 +/- 22.89
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/declutr-model_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
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}
| 26 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-base
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pfs
type: rishabhjain16/infer_pfs
config: en
split: test
metrics:
- type: wer
value: 42.2
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_myst
type: rishabhjain16/infer_myst
config: en
split: test
metrics:
- type: wer
value: 16.17
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/libritts_dev_clean
type: rishabhjain16/libritts_dev_clean
config: en
split: test
metrics:
- type: wer
value: 10.65
name: WER
---
<!-- 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. -->
# openai/whisper-base
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6082
- Wer: 16.5259
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2939 | 4.02 | 1000 | 0.3712 | 14.9737 |
| 0.1381 | 8.04 | 2000 | 0.4280 | 16.5207 |
| 0.0248 | 13.01 | 3000 | 0.5326 | 16.9985 |
| 0.0063 | 17.02 | 4000 | 0.5855 | 16.4293 |
| 0.0048 | 21.04 | 5000 | 0.6082 | 16.5259 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
AnonymousSub/declutr-techqa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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}
}
}
| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-base.en
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pfs
type: rishabhjain16/infer_pfs
config: en
split: test
metrics:
- type: wer
value: 33.53
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_myst
type: rishabhjain16/infer_myst
config: en
split: test
metrics:
- type: wer
value: 15.17
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_cmu
type: rishabhjain16/infer_cmu
config: en
split: test
metrics:
- type: wer
value: 13.32
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/libritts_dev_clean
type: rishabhjain16/libritts_dev_clean
config: en
split: test
metrics:
- type: wer
value: 7.43
name: WER
---
<!-- 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. -->
# openai/whisper-base.en
This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6446
- Wer: 16.4580
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3205 | 4.02 | 1000 | 0.4080 | 14.5116 |
| 0.1568 | 8.04 | 2000 | 0.4672 | 15.3758 |
| 0.035 | 13.01 | 3000 | 0.5696 | 15.9737 |
| 0.0087 | 17.02 | 4000 | 0.6242 | 15.7283 |
| 0.0065 | 21.04 | 5000 | 0.6446 | 16.4580 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
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}
| 8 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: Write your model_id: SharpNLight/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
| 8 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`income.bpr.gpl.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
| 8 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`income.bpr.gpl.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 8 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`income.bpr.gpl.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
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}
}
}
| 4 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`income.bpr.gpl.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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| 31 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`income.bpr.gpl.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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| 6 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`income.bpr.gpl.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 8 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`income.bpr.gpl.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"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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"prefix": null
}
}
}
| 6 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: poetry-gpt2-large-no-hoel_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. -->
# poetry-gpt2-large-no-hoel_2
This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7067
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.6683 | 1.0 | 19927 | 3.7260 |
| 3.3474 | 2.0 | 39854 | 3.7067 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 4 | null |
---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
- visual-question-answering
pipeline_tag: image-to-text
inference: false
---
# BLIP-2, Flan T5-xl, pre-trained only
BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2).
Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model.
The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen
while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings,
which bridge the gap between the embedding space of the image encoder and the large language model.
The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
alt="drawing" width="600"/>
This allows the model to be used for tasks like:
- image captioning
- visual question answering (VQA)
- chat-like conversations by feeding the image and the previous conversation as prompt to the model
## Direct Use and Downstream Use
You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
fine-tuned versions on a task that interests you.
## Bias, Risks, Limitations, and Ethical Considerations
BLIP2-FlanT5 uses off-the-shelf Flan-T5 as the language model. It inherits the same risks and limitations from [Flan-T5](https://arxiv.org/pdf/2210.11416.pdf):
> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).
#### Running the model on CPU
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import BlipProcessor, Blip2ForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
#### Running the model on GPU
##### In full precision
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl", device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In half precision (`float16`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In 8-bit precision (`int8`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate bitsandbytes
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl", load_in_8bit=True, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 1 | 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="michalcisek5/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"])
```
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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}
| 5 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: khachouni/my_awesome_qa_model
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. -->
# khachouni/my_awesome_qa_model
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: 5.9591
- Validation Loss: 5.7678
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4, '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.9591 | 5.7678 | 0 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.10.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
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"task_specific_params": {
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}
| 23 | 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: Unterwexi/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
| 6 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: duskgem
---
[](https://huggingface.co/spaces/MultiversexPeeps/JemandtheHolograms)
### Jem and the Holograms Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
If you want to donate towards costs and don't want to subscribe:
https://ko-fi.com/DUSKFALLcrew
If you want to monthly support the EARTH & DUSK media projects and not just AI:
https://www.patreon.com/earthndusk
duskgem (use that on your prompt)
|
ArBert/bert-base-uncased-finetuned-ner-gmm
|
[] | null |
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}
| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: Write your model_id: Gaivoronsky/Dogge
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ArBert/roberta-base-finetuned-ner
|
[
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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},
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"early_stopping": 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: 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="rlucasz93/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"])
```
|
Aran/DialoGPT-medium-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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}
}
}
| 8 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 62.20 +/- 39.28
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Aran/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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}
}
| 8 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('alex-uv2/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
ArashEsk95/bert-base-uncased-finetuned-sst2
|
[] | null |
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| 0 | null |
Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
Aravinth/test
|
[] | null |
{
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}
| 0 | null |
Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
ArcQ/gpt-experiments
|
[] | null |
{
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: duskspider
---
[](https://huggingface.co/spaces/MultiversexPeeps/the_pink_spider)
### The Pink Spider Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
If you want to donate towards costs and don't want to subscribe:
https://ko-fi.com/DUSKFALLcrew
If you want to monthly support the EARTH & DUSK media projects and not just AI:
https://www.patreon.com/earthndusk
duskspider (use that on your prompt)
|
AriakimTaiyo/DialoGPT-small-Kumiko
|
[
"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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},
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},
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}
}
| 11 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter_v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 40.00 +/- 25.33
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AriakimTaiyo/DialoGPT-small-Rikka
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
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,
"prefix": null
},
"text-generation": {
"do_sample": null,
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| 8 | null |
---
license: creativeml-openrail-m
language:
- en
tags:
- art
---
# In short,
- FaceBomb : Covers from anime to 2.5D style. Suited for general use.
- recipe : 0.5((0.5(AbyssOrangeMix2_hard) + 0.5(pastelmix-better-vae-fp32)) + 0.5(CounterfeitV25_25)) + 0.5(dalcefoV3Painting_dalcefoV3Painting)
- ColorBomb : FaceBomb + vivid color and lighting. A bit picky about prompts.
- recipe : dalcefoV3Painting_dalcefoV3Painting + 0.5(ultracolorV4_ultracolorV4 - CounterfeitV25_25)
- HyperBomb : Strong anime style w/ highly saturated color.
- recipe : 0.5((0.5(AbyssOrangeMix2_hard) + 0.5(pastelmix-better-vae-fp32)) + 0.5(CounterfeitV25_25)) + 0.5(dalcefoV3Painting_dalcefoV3Painting) + 0.3(0.8(pastelMixStylizedAnime_pastelMixPrunedFP16) + 0.2(CounterfeitV25_25) - f222)
# Recommended Setting
## VAE
- If the color appears dull or washed out, try applying VAE. I used `kl-f8-anime2`
- https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt
## Sampling method and Hires.fix
1. `DPM++ SDE Karras: 24~32 steps` / `R-ESRGAN 4x+ Anime6B: 2x, 14 steps` / `Denoising strength:0.45 ~ 0.55`
2. `DDIM: 24~32 steps` / `Latent: 2x 14 steps` / `Denoising Strength:0.45 ~ 0.7`
- First option yields better result in general. Recommended.
- Second option was 1.5 ~ 2 times faster on my system but the output was questionable. Especially for ColorBomb.
## FaceBomb
- Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), masterpiece*portrait, realistic, 3d face, lustrous skin, `
- Negative : `(worst quality, low quality:1.4), watermark, logo,`
## ColorBomb
- Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), (ultra-detailed, high-resolution: 1.2), beautiful girl, { color } { color } theme, `
- e.g. black gold theme
- Negative : `(worst quality, low quality:1.4), watermark, logo,`
## HyperBomb
- Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2),`
- Negative : `(worst quality, low quality:1.4), watermark, logo,`
# Example
- More pictures in folder.
- Below are the ideal/intended outputs.
### FaceBomb
(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), masterpiece*portrait, realistic, 3d face, glowing eyes, shiny hair, lustrous skin, solo, embarassed
Negative prompt: (worst quality, low quality:1.4), watermark, logo,
Steps: 32, Sampler: DPM++ SDE Karras, CFG scale: 9, Seed: 3624413002, Size: 512x768, Model hash: aad629159b, Model: __Custom_FaceBombMix-fp16-no-ema, Denoising strength: 0.5, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: R-ESRGAN 4x+ Anime6B
---
### ColorBomb
((masterpiece, best quality, ultra-detailed, high-resolution)), solo, beautiful girl, gleaming eye, perfect eye, age 15, black white gold theme,
Negative prompt: (worst quality, low quality:1.4), (depth of field, blurry:1.2), (greyscale, monochrome:1.1), 3D face, cropped, lowres, text, jpeg artifacts, signature, watermark, username, blurry, artist name, trademark, watermark, title, (tan, muscular, sd character:1.1), multiple view, Reference sheet, non-linear background, blurred background, bad anatomy, cropped hands, extra digit, fewer digit,
Steps: 24, Sampler: DDIM, CFG scale: 7, Seed: 3050494714, Size: 512x768, Model hash: 627f50eea8, Model: __Custom_ColorBomb-fp16-no-ema, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: Latent
---
### HyperBomb
(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2),
Negative prompt: (worst quality, low quality:1.4), watermark, logo,
Steps: 32, Sampler: DDIM, CFG scale: 9, Seed: 2411870881, Size: 768x512, Model hash: 16c6ca45b1, Model: __Custom_HyperBombMix-fp16-no-ema, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: Latent
|
Arina/Erine
|
[] | null |
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}
}
| 0 | null |
---
library_name: rl-algo-impls
tags:
- Acrobot-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: -70.5 +/- 9.68
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Acrobot-v1
type: Acrobot-v1
---
# **PPO** Agent playing **Acrobot-v1**
This is a trained model of a **PPO** agent playing **Acrobot-v1** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo.
All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/ysd5gj7p.
## Training Results
This model was trained from 3 trainings of **PPO** agents using different initial seeds. These agents were trained by checking out [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).
| algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url |
|:-------|:-----------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
| ppo | Acrobot-v1 | 1 | -75.8125 | 14.2882 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/i3jpey37) |
| ppo | Acrobot-v1 | 2 | -88.5 | 46.9281 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/s4yeahcr) |
| ppo | Acrobot-v1 | 3 | -70.5 | 9.68246 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/jm8ykhu9) |
### Prerequisites: Weights & Biases (WandB)
Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.
Before doing anything below, you'll need to create a wandb account and run `wandb
login`.
## Usage
/sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls
Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
[983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b).
```
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/jm8ykhu9
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
notebook.
## Training
If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). While
training is deterministic, different hardware will give different results.
```
python train.py --algo ppo --env Acrobot-v1 --seed 3
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
notebook.
## Benchmarking (with Lambda Labs instance)
This and other models from https://api.wandb.ai/links/sgoodfriend/ysd5gj7p were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:
```
git clone [email protected]:sgoodfriend/rl-algo-impls.git
cd rl-algo-impls
bash ./lambda_labs/setup.sh
wandb login
bash ./lambda_labs/benchmark.sh [-a {"ppo a2c dqn vpg"}] [-e ENVS] [-j {6}] [-p {rl-algo-impls-benchmarks}] [-s {"1 2 3"}]
```
### Alternative: Google Colab Pro+
As an alternative,
[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
## Hyperparameters
This isn't exactly the format of hyperparams in hyperparams/ppo.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
```
additional_keys_to_log: []
algo: ppo
algo_hyperparams:
ent_coef: 0
gae_lambda: 0.94
gamma: 0.99
n_epochs: 4
n_steps: 256
device: auto
env: Acrobot-v1
env_hyperparams:
n_envs: 16
normalize: true
env_id: null
eval_hyperparams: {}
microrts_reward_decay_callback: false
n_timesteps: 1000000
policy_hyperparams: {}
seed: 3
use_deterministic_algorithms: true
wandb_entity: null
wandb_group: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_983cb75
- host_129-159-43-75
- branch_main
- v0.0.9
```
|
ArvinZhuang/BiTAG-t5-large
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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"prefix": "summarize: "
},
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 4 | 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="ryanaspen/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"])
```
|
Ashagi/Ashvx
|
[] | null |
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}
}
}
| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **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: Write your model_id: zlicastro/zl-ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AshiNLP/Bert_model
|
[] | null |
{
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}
}
| 0 | 2023-02-07T01:25:17Z |
---
library_name: rl-algo-impls
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: 500.0 +/- 0.0
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo.
All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/ysd5gj7p.
## Training Results
This model was trained from 3 trainings of **PPO** agents using different initial seeds. These agents were trained by checking out [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).
| algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url |
|:-------|:------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
| ppo | CartPole-v1 | 1 | 500 | 0 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/h6q6ybro) |
| ppo | CartPole-v1 | 2 | 500 | 0 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/t43i90su) |
| ppo | CartPole-v1 | 3 | 500 | 0 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/bf8ho2cx) |
### Prerequisites: Weights & Biases (WandB)
Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.
Before doing anything below, you'll need to create a wandb account and run `wandb
login`.
## Usage
/sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls
Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
[983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b).
```
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/t43i90su
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
notebook.
## Training
If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). While
training is deterministic, different hardware will give different results.
```
python train.py --algo ppo --env CartPole-v1 --seed 2
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
notebook.
## Benchmarking (with Lambda Labs instance)
This and other models from https://api.wandb.ai/links/sgoodfriend/ysd5gj7p were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:
```
git clone [email protected]:sgoodfriend/rl-algo-impls.git
cd rl-algo-impls
bash ./lambda_labs/setup.sh
wandb login
bash ./lambda_labs/benchmark.sh [-a {"ppo a2c dqn vpg"}] [-e ENVS] [-j {6}] [-p {rl-algo-impls-benchmarks}] [-s {"1 2 3"}]
```
### Alternative: Google Colab Pro+
As an alternative,
[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
## Hyperparameters
This isn't exactly the format of hyperparams in hyperparams/ppo.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
```
additional_keys_to_log: []
algo: ppo
algo_hyperparams:
batch_size: 256
clip_range: 0.2
clip_range_decay: linear
ent_coef: 0
gae_lambda: 0.8
gamma: 0.98
learning_rate: 0.001
learning_rate_decay: linear
n_epochs: 20
n_steps: 32
device: auto
env: CartPole-v1
env_hyperparams:
n_envs: 8
env_id: null
eval_hyperparams:
step_freq: 25000
microrts_reward_decay_callback: false
n_timesteps: 100000
policy_hyperparams: {}
seed: 2
use_deterministic_algorithms: true
wandb_entity: null
wandb_group: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_983cb75
- host_129-159-43-75
- branch_main
- v0.0.9
```
|
Ashkanmh/bert-base-parsbert-uncased-finetuned
|
[
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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"max_length": null,
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}
}
}
| 3 | 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: hectorjelly/Mespil_Rangers
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ashl3y/model_name
|
[] | null |
{
"architectures": null,
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| 0 | null |
---
library_name: rl-algo-impls
tags:
- QbertNoFrameskip-v4
- ppo
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: 13079.69 +/- 3555.52
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: QbertNoFrameskip-v4
type: QbertNoFrameskip-v4
---
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-v4** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo.
All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/ysd5gj7p.
## Training Results
This model was trained from 3 trainings of **PPO** agents using different initial seeds. These agents were trained by checking out [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).
| algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url |
|:-------|:--------------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
| ppo | QbertNoFrameskip-v4 | 1 | 13079.7 | 3555.52 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/ldhc0835) |
| ppo | QbertNoFrameskip-v4 | 2 | 12012.5 | 3064.83 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/177aynzr) |
| ppo | QbertNoFrameskip-v4 | 3 | 11512.5 | 3495.89 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/7tbenuzg) |
### Prerequisites: Weights & Biases (WandB)
Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.
Before doing anything below, you'll need to create a wandb account and run `wandb
login`.
## Usage
/sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls
Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
[983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b).
```
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/ldhc0835
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
notebook.
## Training
If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). While
training is deterministic, different hardware will give different results.
```
python train.py --algo ppo --env QbertNoFrameskip-v4 --seed 1
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
notebook.
## Benchmarking (with Lambda Labs instance)
This and other models from https://api.wandb.ai/links/sgoodfriend/ysd5gj7p were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:
```
git clone [email protected]:sgoodfriend/rl-algo-impls.git
cd rl-algo-impls
bash ./lambda_labs/setup.sh
wandb login
bash ./lambda_labs/benchmark.sh [-a {"ppo a2c dqn vpg"}] [-e ENVS] [-j {6}] [-p {rl-algo-impls-benchmarks}] [-s {"1 2 3"}]
```
### Alternative: Google Colab Pro+
As an alternative,
[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
## Hyperparameters
This isn't exactly the format of hyperparams in hyperparams/ppo.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
```
additional_keys_to_log: []
algo: ppo
algo_hyperparams:
batch_size: 256
clip_range: 0.1
clip_range_decay: linear
ent_coef: 0.01
learning_rate: 0.00025
learning_rate_decay: linear
n_epochs: 4
n_steps: 128
vf_coef: 0.5
device: auto
env: QbertNoFrameskip-v4
env_hyperparams:
frame_stack: 4
n_envs: 8
no_reward_fire_steps: 500
no_reward_timeout_steps: 1000
vec_env_class: async
env_id: null
eval_hyperparams:
deterministic: false
microrts_reward_decay_callback: false
n_timesteps: 10000000
policy_hyperparams:
activation_fn: relu
seed: 1
use_deterministic_algorithms: true
wandb_entity: null
wandb_group: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_983cb75
- host_129-159-43-75
- branch_main
- v0.0.9
```
|
Ashok/my-new-tokenizer
|
[] | null |
{
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}
| 0 | null |
---
library_name: rl-algo-impls
tags:
- SpaceInvadersNoFrameskip-v4
- ppo
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: 1023.12 +/- 348.15
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo.
All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/ysd5gj7p.
## Training Results
This model was trained from 3 trainings of **PPO** agents using different initial seeds. These agents were trained by checking out [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).
| algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url |
|:-------|:----------------------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
| ppo | SpaceInvadersNoFrameskip-v4 | 1 | 1023.12 | 348.151 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/l4ghiz7l) |
| ppo | SpaceInvadersNoFrameskip-v4 | 2 | 1085.31 | 511.617 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/sizwucqe) |
| ppo | SpaceInvadersNoFrameskip-v4 | 3 | 801.875 | 290.349 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/lrfkg544) |
### Prerequisites: Weights & Biases (WandB)
Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.
Before doing anything below, you'll need to create a wandb account and run `wandb
login`.
## Usage
/sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls
Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
[983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b).
```
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/l4ghiz7l
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
notebook.
## Training
If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). While
training is deterministic, different hardware will give different results.
```
python train.py --algo ppo --env SpaceInvadersNoFrameskip-v4 --seed 1
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
notebook.
## Benchmarking (with Lambda Labs instance)
This and other models from https://api.wandb.ai/links/sgoodfriend/ysd5gj7p were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:
```
git clone [email protected]:sgoodfriend/rl-algo-impls.git
cd rl-algo-impls
bash ./lambda_labs/setup.sh
wandb login
bash ./lambda_labs/benchmark.sh [-a {"ppo a2c dqn vpg"}] [-e ENVS] [-j {6}] [-p {rl-algo-impls-benchmarks}] [-s {"1 2 3"}]
```
### Alternative: Google Colab Pro+
As an alternative,
[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
## Hyperparameters
This isn't exactly the format of hyperparams in hyperparams/ppo.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
```
additional_keys_to_log: []
algo: ppo
algo_hyperparams:
batch_size: 256
clip_range: 0.1
clip_range_decay: linear
ent_coef: 0.01
learning_rate: 0.00025
learning_rate_decay: linear
n_epochs: 4
n_steps: 128
vf_coef: 0.5
device: auto
env: SpaceInvadersNoFrameskip-v4
env_hyperparams:
frame_stack: 4
n_envs: 8
no_reward_fire_steps: 500
no_reward_timeout_steps: 1000
vec_env_class: async
env_id: null
eval_hyperparams:
deterministic: false
microrts_reward_decay_callback: false
n_timesteps: 10000000
policy_hyperparams:
activation_fn: relu
seed: 1
use_deterministic_algorithms: true
wandb_entity: null
wandb_group: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_983cb75
- host_129-159-43-75
- branch_main
- v0.0.9
```
|
AshtonBenson/DialoGPT-small-quentin-coldwater
|
[] | null |
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| 0 | null |
---
library_name: rl-algo-impls
tags:
- BreakoutNoFrameskip-v4
- ppo
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: 383.31 +/- 42.47
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BreakoutNoFrameskip-v4
type: BreakoutNoFrameskip-v4
---
# **PPO** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **BreakoutNoFrameskip-v4** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo.
All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/ysd5gj7p.
## Training Results
This model was trained from 3 trainings of **PPO** agents using different initial seeds. These agents were trained by checking out [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).
| algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url |
|:-------|:-----------------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
| ppo | BreakoutNoFrameskip-v4 | 1 | 383.312 | 42.4672 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/tnnhrvm0) |
| ppo | BreakoutNoFrameskip-v4 | 2 | 361.375 | 83.3823 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/m4xiygou) |
| ppo | BreakoutNoFrameskip-v4 | 3 | 361.125 | 27.1521 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/yowmnksz) |
### Prerequisites: Weights & Biases (WandB)
Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.
Before doing anything below, you'll need to create a wandb account and run `wandb
login`.
## Usage
/sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls
Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
[983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b).
```
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/tnnhrvm0
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
notebook.
## Training
If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). While
training is deterministic, different hardware will give different results.
```
python train.py --algo ppo --env BreakoutNoFrameskip-v4 --seed 1
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
notebook.
## Benchmarking (with Lambda Labs instance)
This and other models from https://api.wandb.ai/links/sgoodfriend/ysd5gj7p were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:
```
git clone [email protected]:sgoodfriend/rl-algo-impls.git
cd rl-algo-impls
bash ./lambda_labs/setup.sh
wandb login
bash ./lambda_labs/benchmark.sh [-a {"ppo a2c dqn vpg"}] [-e ENVS] [-j {6}] [-p {rl-algo-impls-benchmarks}] [-s {"1 2 3"}]
```
### Alternative: Google Colab Pro+
As an alternative,
[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
## Hyperparameters
This isn't exactly the format of hyperparams in hyperparams/ppo.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
```
additional_keys_to_log: []
algo: ppo
algo_hyperparams:
batch_size: 256
clip_range: 0.1
clip_range_decay: linear
ent_coef: 0.01
learning_rate: 0.00025
learning_rate_decay: linear
n_epochs: 4
n_steps: 128
vf_coef: 0.5
device: auto
env: BreakoutNoFrameskip-v4
env_hyperparams:
frame_stack: 4
n_envs: 8
no_reward_fire_steps: 500
no_reward_timeout_steps: 1000
vec_env_class: async
env_id: null
eval_hyperparams:
deterministic: false
microrts_reward_decay_callback: false
n_timesteps: 10000000
policy_hyperparams:
activation_fn: relu
seed: 1
use_deterministic_algorithms: true
wandb_entity: null
wandb_group: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_983cb75
- host_129-159-43-75
- branch_main
- v0.0.9
```
|
Ateeb/FullEmotionDetector
|
[
"pytorch",
"funnel",
"text-classification",
"transformers"
] |
text-classification
|
{
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"FunnelForSequenceClassification"
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}
}
}
| 31 | null |
---
library_name: rl-algo-impls
tags:
- MountainCar-v0
- ppo
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: -110.88 +/- 7.11
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo.
All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/ysd5gj7p.
## Training Results
This model was trained from 3 trainings of **PPO** agents using different initial seeds. These agents were trained by checking out [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).
| algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url |
|:-------|:---------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
| ppo | MountainCar-v0 | 1 | -115.625 | 16.1434 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/thx0omdg) |
| ppo | MountainCar-v0 | 2 | -110.875 | 7.10524 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/24s7j9ik) |
| ppo | MountainCar-v0 | 3 | -115.562 | 13.2569 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/2h5q3ghp) |
### Prerequisites: Weights & Biases (WandB)
Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.
Before doing anything below, you'll need to create a wandb account and run `wandb
login`.
## Usage
/sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls
Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
[983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b).
```
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/24s7j9ik
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
notebook.
## Training
If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: [983cb75](https://github.com/sgoodfriend/rl-algo-impls/tree/983cb75e43e51cf4ef57f177194ab9a4a1a8808b). While
training is deterministic, different hardware will give different results.
```
python train.py --algo ppo --env MountainCar-v0 --seed 2
```
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
notebook.
## Benchmarking (with Lambda Labs instance)
This and other models from https://api.wandb.ai/links/sgoodfriend/ysd5gj7p were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:
```
git clone [email protected]:sgoodfriend/rl-algo-impls.git
cd rl-algo-impls
bash ./lambda_labs/setup.sh
wandb login
bash ./lambda_labs/benchmark.sh [-a {"ppo a2c dqn vpg"}] [-e ENVS] [-j {6}] [-p {rl-algo-impls-benchmarks}] [-s {"1 2 3"}]
```
### Alternative: Google Colab Pro+
As an alternative,
[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
## Hyperparameters
This isn't exactly the format of hyperparams in hyperparams/ppo.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
```
additional_keys_to_log: []
algo: ppo
algo_hyperparams:
ent_coef: 0
gae_lambda: 0.98
gamma: 0.99
n_epochs: 4
n_steps: 16
device: auto
env: MountainCar-v0
env_hyperparams:
n_envs: 16
normalize: true
env_id: null
eval_hyperparams: {}
microrts_reward_decay_callback: false
n_timesteps: 1000000
policy_hyperparams: {}
seed: 2
use_deterministic_algorithms: true
wandb_entity: null
wandb_group: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_983cb75
- host_129-159-43-75
- branch_main
- v0.0.9
```
|
Axon/resnet50-v1
|
[
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
] | null |
{
"architectures": null,
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"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_ro": {
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}
}
}
| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: Write your model_id: JessicaHsu/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ayham/albert_roberta_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 6 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: thanat/bert-finetuned-ner
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. -->
# thanat/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the [CoNLL-2003](https://huggingface.co/datasets/conll2003) dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0280
- Validation Loss: 0.0513
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, '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 |
|:----------:|:---------------:|:-----:|
| 0.1691 | 0.0630 | 0 |
| 0.0484 | 0.0529 | 1 |
| 0.0280 | 0.0513 | 2 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ayham/distilbert_bert_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
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},
"text-generation": {
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 11 | 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="AdonaiHS/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"])
```
|
Ayham/distilbert_gpt2_summarization_cnndm
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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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"max_length": null
},
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},
"translation_en_to_fr": {
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},
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"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 6 | 2023-02-07T03:20:43Z |
---
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: 618.50 +/- 249.78
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga njrosati -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 njrosati -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 njrosati
```
## 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)])
```
|
Ayham/distilbert_gpt2_summarization_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-Unit2-part2
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="AdonaiHS/Taxi-v3-Unit2-part2", 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"])
```
|
Ayham/distilbert_roberta_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
}
}
}
| 14 | 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: 619.50 +/- 87.25
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 Pearson -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 Pearson -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 Pearson
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Ayham/roberta_gpt2_new_max64_summarization_cnndm
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
}
}
}
| 4 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: 2-Taxi-v3-Unit2-part2
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="AdonaiHS/2-Taxi-v3-Unit2-part2", 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"])
```
|
Ayham/roberta_gpt2_summarization_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
}
}
}
| 6 | 2023-02-07T03:37:37Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 55.90 +/- 40.18
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Ayran/DialoGPT-small-gandalf
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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}
| 11 | null |
---
tags:
- TensorRT
- Text2Image
- Stable Diffusion
- Image2Image
- SDA
---
# burnerbaby/sds converted into TensorRT
<img src="https://i.imgur.com/fQS926g.png"></a>
Model converted from diffusers into TensorRT for accelerated inference up to 4x faster.
originally from: https://github.com/chavinlo/sda-node
This model was automatically converted by SDA-node
Compilation configuration:
```json
{
"_class_name": "StableDiffusionAccelerated_Base",
"_sda_version": "0.1.2",
"_trt_version": "8.5.3",
"_cuda_version": "none",
"_cudnn_version": "none",
"_onnx2trt_version": "8.5.3",
"unet": {
"precision": "fp16",
"path": "engine/unet.plan"
},
"clip": {
"path": "engine/clip.plan"
},
"de_vae": {
"path": "engine/de_vae.plan"
}
}
```
|
Ayta/Haha
|
[] | null |
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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: 598.00 +/- 261.07
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 amoselberg -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 amoselberg -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 amoselberg
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('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)])
```
|
AyushPJ/ai-club-inductions-21-nlp-distilBERT
|
[
"pytorch",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
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"DistilBertForQuestionAnswering"
],
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}
| 8 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-plotqa-trained
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. -->
# donut-plotqa-trained
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
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.5.2
- Tokenizers 0.12.1
|
AyushPJ/test-squad-trained-finetuned-squad
|
[
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"max_length": null
},
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},
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},
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}
}
}
| 8 | 2023-02-07T05:46:07Z |
---
tags:
- espnet
- audio
- text-to-speech
language: jp
license: cc-by-4.0
---
## ESPnet2 TTS model
### `mio/tokiwa_midori`
This model was trained by mio using amadeus recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 0232f540a98ece921477b961db8ae019211da9af
pip install -e .
cd egs2/amadeus/tts1
./run.sh --skip_data_prep false --skip_train true --download_model mio/tokiwa_midori
```
## TTS config
<details><summary>expand</summary>
```
config: conf/tuning/finetune_vits.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_midori_vits_finetune_from_jsut_32_sentence
ngpu: 1
seed: 777
num_workers: 4
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- total_count
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: false
create_graph_in_tensorboard: false
use_wandb: true
wandb_project: midori
wandb_id: null
wandb_entity: null
wandb_name: vits_finetune_midori_from_jsut
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- downloads/f3698edf589206588f58f5ec837fa516/exp/tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause/train.total_count.ave_10best.pth:tts:tts
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: 1000
batch_size: 20
valid_batch_size: null
batch_bins: 5000000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/text_shape.phn
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/text_shape.phn
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/22k/raw/train/text
- text
- text
- - dump/22k/raw/train/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/22k/raw/dev/text
- text
- text
- - dump/22k/raw/dev/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adamw
optim_conf:
lr: 0.0001
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler: exponentiallr
scheduler_conf:
gamma: 0.999875
optim2: adamw
optim2_conf:
lr: 0.0001
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler2: exponentiallr
scheduler2_conf:
gamma: 0.999875
generator_first: false
token_list:
- <blank>
- <unk>
- '1'
- '2'
- '0'
- '3'
- '4'
- '-1'
- '5'
- a
- o
- '-2'
- i
- '-3'
- u
- e
- k
- n
- t
- '6'
- r
- '-4'
- s
- N
- m
- pau
- '7'
- sh
- d
- g
- w
- '8'
- U
- '-5'
- I
- cl
- h
- y
- b
- '9'
- j
- ts
- ch
- '-6'
- z
- p
- '-7'
- f
- ky
- ry
- '-8'
- gy
- '-9'
- hy
- ny
- '-10'
- by
- my
- '-11'
- '-12'
- '-13'
- py
- '-14'
- '-15'
- v
- '10'
- '-16'
- '-17'
- '11'
- '-21'
- '-20'
- '12'
- '-19'
- '13'
- '-18'
- '14'
- dy
- '15'
- ty
- '-22'
- '16'
- '18'
- '19'
- '17'
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: jaconv
g2p: pyopenjtalk_accent_with_pause
feats_extract: linear_spectrogram
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
normalize: null
normalize_conf: {}
tts: vits
tts_conf:
generator_type: vits_generator
generator_params:
hidden_channels: 192
spks: -1
global_channels: -1
segment_size: 32
text_encoder_attention_heads: 2
text_encoder_ffn_expand: 4
text_encoder_blocks: 6
text_encoder_positionwise_layer_type: conv1d
text_encoder_positionwise_conv_kernel_size: 3
text_encoder_positional_encoding_layer_type: rel_pos
text_encoder_self_attention_layer_type: rel_selfattn
text_encoder_activation_type: swish
text_encoder_normalize_before: true
text_encoder_dropout_rate: 0.1
text_encoder_positional_dropout_rate: 0.0
text_encoder_attention_dropout_rate: 0.1
use_macaron_style_in_text_encoder: true
use_conformer_conv_in_text_encoder: false
text_encoder_conformer_kernel_size: -1
decoder_kernel_size: 7
decoder_channels: 512
decoder_upsample_scales:
- 8
- 8
- 2
- 2
decoder_upsample_kernel_sizes:
- 16
- 16
- 4
- 4
decoder_resblock_kernel_sizes:
- 3
- 7
- 11
decoder_resblock_dilations:
- - 1
- 3
- 5
- - 1
- 3
- 5
- - 1
- 3
- 5
use_weight_norm_in_decoder: true
posterior_encoder_kernel_size: 5
posterior_encoder_layers: 16
posterior_encoder_stacks: 1
posterior_encoder_base_dilation: 1
posterior_encoder_dropout_rate: 0.0
use_weight_norm_in_posterior_encoder: true
flow_flows: 4
flow_kernel_size: 5
flow_base_dilation: 1
flow_layers: 4
flow_dropout_rate: 0.0
use_weight_norm_in_flow: true
use_only_mean_in_flow: true
stochastic_duration_predictor_kernel_size: 3
stochastic_duration_predictor_dropout_rate: 0.5
stochastic_duration_predictor_flows: 4
stochastic_duration_predictor_dds_conv_layers: 3
vocabs: 85
aux_channels: 513
discriminator_type: hifigan_multi_scale_multi_period_discriminator
discriminator_params:
scales: 1
scale_downsample_pooling: AvgPool1d
scale_downsample_pooling_params:
kernel_size: 4
stride: 2
padding: 2
scale_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 15
- 41
- 5
- 3
channels: 128
max_downsample_channels: 1024
max_groups: 16
bias: true
downsample_scales:
- 2
- 2
- 4
- 4
- 1
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
follow_official_norm: false
periods:
- 2
- 3
- 5
- 7
- 11
period_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 5
- 3
channels: 32
downsample_scales:
- 3
- 3
- 3
- 3
- 1
max_downsample_channels: 1024
bias: true
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
generator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
discriminator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
feat_match_loss_params:
average_by_discriminators: false
average_by_layers: false
include_final_outputs: true
mel_loss_params:
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
window: hann
n_mels: 80
fmin: 0
fmax: null
log_base: null
lambda_adv: 1.0
lambda_mel: 45.0
lambda_feat_match: 2.0
lambda_dur: 1.0
lambda_kl: 1.0
sampling_rate: 22050
cache_generator_outputs: true
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: '202207'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BME-TMIT/foszt2oszt
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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
},
"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
}
}
}
| 15 | null |
---
library_name: diffusers
pipeline_tag: text-to-image
tags:
- pepe
---
# How to use
***To prompt you can use the following code***
```python
from diffusers import StableDiffusionPipeline
model_path = "Dipl0/pepe-diffuser"
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
pipe.to("cuda")
prompt = "pepe surfing on the moon"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save(prompt.replace(" ","_") + ".png")
```
|
BSC-LT/roberta-large-bne-capitel-ner
|
[
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
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| 5 | null |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_wnli_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.14084507042253522
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_wnli_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5913
- Accuracy: 0.1408
## 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: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3404 | 1.0 | 435 | 0.5913 | 0.1408 |
| 0.3027 | 2.0 | 870 | 0.5985 | 0.1127 |
| 0.2935 | 3.0 | 1305 | 0.6351 | 0.1127 |
| 0.2884 | 4.0 | 1740 | 0.6013 | 0.0986 |
| 0.2838 | 5.0 | 2175 | 0.6154 | 0.0986 |
| 0.2788 | 6.0 | 2610 | 0.6608 | 0.0845 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BalajiSathesh/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 8 | 2023-02-07T07:43:46Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3076 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 150, 'do_lower_case': False}) with Transformer model: BloomModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Barbarameerr/Barbara
|
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| 0 | 2023-02-07T07:58:29Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1902.19 +/- 153.27
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
|
Barkavi/totto-t5-base-bert-score-121K
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"prefix": "translate English to German: "
},
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"prefix": "translate English to French: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
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}
| 51 | 2023-02-07T08:03:12Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.6561958941380164
- name: F1
type: f1
value: 0.1361093847110006
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6508
- Accuracy: 0.6562
- F1: 0.1361
- Combined Score: 0.3962
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
| 0.7744 | 1.0 | 29671 | 0.7100 | 0.6422 | 0.0598 | 0.3510 |
| 0.6803 | 2.0 | 59342 | 0.6914 | 0.6548 | 0.1291 | 0.3919 |
| 0.6617 | 3.0 | 89013 | 0.6944 | 0.6588 | 0.1498 | 0.4043 |
| 0.6531 | 4.0 | 118684 | 0.6699 | 0.6632 | 0.1741 | 0.4187 |
| 0.6482 | 5.0 | 148355 | 0.6633 | 0.6622 | 0.1666 | 0.4144 |
| 0.6451 | 6.0 | 178026 | 0.6508 | 0.6562 | 0.1361 | 0.3962 |
| 0.6431 | 7.0 | 207697 | 0.6632 | 0.6595 | 0.1526 | 0.4060 |
| 0.6416 | 8.0 | 237368 | 0.6621 | 0.6634 | 0.1720 | 0.4177 |
| 0.6404 | 9.0 | 267039 | 0.6579 | 0.6691 | 0.2001 | 0.4346 |
| 0.6395 | 10.0 | 296710 | 0.6554 | 0.6690 | 0.2029 | 0.4359 |
| 0.6387 | 11.0 | 326381 | 0.6558 | 0.6637 | 0.1755 | 0.4196 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Barleysack/klue-roberta-LSTM
|
[
"pytorch",
"roberta",
"transformers"
] | null |
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"QAWithLSTMModel"
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| 6 | 2023-02-07T08:11:24Z |
---
tags:
- generated_from_trainer
datasets:
- custom_squad_v2
model-index:
- name: kobigbird-pure45-94614916
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. -->
# kobigbird-pure45-94614916
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the custom_squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 45
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 42 | 1.2472 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28
|
[
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] |
question-answering
|
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}
| 18 | 2023-02-07T08:34:07Z |
---
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: -2.67 +/- 0.70
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
...
```
|
BatuhanYilmaz/mlm-finetuned-imdb
|
[] | null |
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}
| 0 | 2023-02-07T08:42:08Z |
---
license: creativeml-openrail-m
language:
- en
---
|
BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es
|
[] | null |
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| 0 | 2023-02-07T08:42:22Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **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: Write your model_id: mojoee/ppo-PyramidTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Baybars/debateGPT
|
[] | null |
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| 0 | 2023-02-07T08:43:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_data_aug_rte_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.4981949458483754
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_rte_256
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5461
- Accuracy: 0.4982
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3321 | 1.0 | 568 | 0.5461 | 0.4982 |
| 0.288 | 2.0 | 1136 | 0.5692 | 0.4910 |
| 0.2847 | 3.0 | 1704 | 0.5578 | 0.4982 |
| 0.283 | 4.0 | 2272 | 0.5487 | 0.4946 |
| 0.2822 | 5.0 | 2840 | 0.5564 | 0.4982 |
| 0.2813 | 6.0 | 3408 | 0.5508 | 0.5235 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Baybars/wav2vec2-xls-r-300m-cv8-turkish
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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}
| 5 | 2023-02-07T08:57:46Z |
---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- generated_from_trainer
datasets:
- squad
- newsqa
- LLukas22/cqadupstack
- LLukas22/fiqa
- LLukas22/scidocs
- deepset/germanquad
- LLukas22/nq
language:
- en
- de
---
# all-MiniLM-L12-v2-embedding-all
This model is a fine-tuned version of [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) on the following datasets: [squad](https://huggingface.co/datasets/squad), [newsqa](https://huggingface.co/datasets/newsqa), [LLukas22/cqadupstack](https://huggingface.co/datasets/LLukas22/cqadupstack), [LLukas22/fiqa](https://huggingface.co/datasets/LLukas22/fiqa), [LLukas22/scidocs](https://huggingface.co/datasets/LLukas22/scidocs), [deepset/germanquad](https://huggingface.co/datasets/deepset/germanquad), [LLukas22/nq](https://huggingface.co/datasets/LLukas22/nq).
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('LLukas22/all-MiniLM-L12-v2-embedding-all')
embeddings = model.encode(sentences)
print(embeddings)
```
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1E+00
- per device batch size: 60
- effective batch size: 180
- seed: 42
- optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
- weight decay: 2E-02
- D-Adaptation: True
- Warmup: True
- number of epochs: 20
- mixed_precision_training: bf16
## Training results
| Epoch | Train Loss | Validation Loss |
| ----- | ---------- | --------------- |
| 0 | 0.0708 | 0.0619 |
| 1 | 0.0609 | 0.0567 |
| 2 | 0.0531 | 0.0542 |
| 3 | 0.0475 | 0.0528 |
| 4 | 0.0428 | 0.0521 |
| 5 | 0.0389 | 0.0513 |
| 6 | 0.0352 | 0.0508 |
| 7 | 0.0322 | 0.0494 |
| 8 | 0.0289 | 0.0485 |
| 9 | 0.0264 | 0.0483 |
| 10 | 0.0242 | 0.0466 |
| 11 | 0.0221 | 0.0459 |
| 12 | 0.0204 | 0.0469 |
| 13 | 0.0189 | 0.0459 |
## Evaluation results
| Epoch | top_1 | top_3 | top_5 | top_10 | top_25 |
| ----- | ----- | ----- | ----- | ----- | ----- |
| 0 | 0.507 | 0.665 | 0.721 | 0.784 | 0.847 |
| 1 | 0.501 | 0.661 | 0.719 | 0.783 | 0.846 |
| 2 | 0.508 | 0.669 | 0.726 | 0.789 | 0.851 |
| 3 | 0.507 | 0.665 | 0.722 | 0.785 | 0.85 |
| 4 | 0.506 | 0.667 | 0.724 | 0.788 | 0.851 |
| 5 | 0.511 | 0.673 | 0.731 | 0.795 | 0.857 |
| 6 | 0.51 | 0.674 | 0.732 | 0.794 | 0.856 |
| 7 | 0.512 | 0.674 | 0.732 | 0.796 | 0.859 |
| 8 | 0.515 | 0.678 | 0.736 | 0.799 | 0.861 |
| 9 | 0.514 | 0.679 | 0.737 | 0.8 | 0.862 |
| 10 | 0.52 | 0.683 | 0.741 | 0.803 | 0.864 |
| 11 | 0.522 | 0.686 | 0.744 | 0.806 | 0.866 |
| 12 | 0.519 | 0.683 | 0.741 | 0.804 | 0.864 |
| 13 | 0.522 | 0.685 | 0.743 | 0.806 | 0.865 |
## Framework versions
- Transformers: 4.25.1
- PyTorch: 2.0.0.dev20230210+cu118
- PyTorch Lightning: 1.8.6
- Datasets: 2.7.1
- Tokenizers: 0.13.1
- Sentence Transformers: 2.2.2
## Additional Information
This model was trained as part of my Master's Thesis **'Evaluation of transformer based language models for use in service information systems'**. The source code is available on [Github](https://github.com/LLukas22/Retrieval-Augmented-QA).
|
BeIR/query-gen-msmarco-t5-base-v1
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
"model_type": "t5",
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| 1,816 | 2023-02-07T08:58:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: function-arg-swap-model-148k-files-365k-samples
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. -->
# function-arg-swap-model-148k-files-365k-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4822
- Accuracy: 0.8850
- Precision: 0.8850
- Recall: 0.8842
- F1 score: 0.8846
## 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: 10
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BeIR/query-gen-msmarco-t5-large-v1
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
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}
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}
| 1,225 | 2023-02-07T08:59:09Z |
---
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: -3.28 +/- 1.03
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
...
```
|
Beatriz/model_name
|
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}
| 0 | 2023-02-07T09:00:25Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### henry Dreambooth model trained by raw-vitor 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:
|
Beelow/model
|
[] | null |
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| 0 | 2023-02-07T09:07:10Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8405963302752294
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_256
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5583
- Accuracy: 0.8406
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5009 | 1.0 | 4374 | 0.6370 | 0.8165 |
| 0.3329 | 2.0 | 8748 | 0.6643 | 0.8257 |
| 0.2804 | 3.0 | 13122 | 0.6192 | 0.8326 |
| 0.249 | 4.0 | 17496 | 0.6205 | 0.8372 |
| 0.2279 | 5.0 | 21870 | 0.6250 | 0.8349 |
| 0.2122 | 6.0 | 26244 | 0.6644 | 0.8280 |
| 0.2008 | 7.0 | 30618 | 0.5707 | 0.8440 |
| 0.1918 | 8.0 | 34992 | 0.5863 | 0.8360 |
| 0.1847 | 9.0 | 39366 | 0.5779 | 0.8394 |
| 0.1784 | 10.0 | 43740 | 0.5662 | 0.8349 |
| 0.1734 | 11.0 | 48114 | 0.5619 | 0.8394 |
| 0.169 | 12.0 | 52488 | 0.5583 | 0.8406 |
| 0.1653 | 13.0 | 56862 | 0.5830 | 0.8303 |
| 0.1619 | 14.0 | 61236 | 0.5773 | 0.8372 |
| 0.1591 | 15.0 | 65610 | 0.5728 | 0.8291 |
| 0.1564 | 16.0 | 69984 | 0.5631 | 0.8383 |
| 0.154 | 17.0 | 74358 | 0.5628 | 0.8452 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Beri/legal-qa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
],
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| 10 | null |
# CoCoSoDa: Effective Contrastive Learning for Code Search
Our approach adopts the pre-trained model as the base code/query encoder and optimizes it using multimodal contrastive learning and soft data augmentation.
CoCoSoDa is comprised of the following four components:
* **Pre-trained code/query encoder** captures the semantic information of a code snippet or a natural language query and maps it into a high-dimensional embedding space.
as the code/query encoder.
* **Momentum code/query encoder** encodes the samples (code snippets or queries) of current and previous mini-batches to enrich the negative samples.
* **Soft data augmentation** is to dynamically mask or replace some tokens in a sample (code/query) to generate a similar sample as a form of data augmentation.
* **Multimodal contrastive learning loss function** is used as the optimization objective and consists of inter-modal and intra-modal contrastive learning loss. They are used to minimize the distance of the representations of similar samples and maximize the distance of different samples in the embedding space.
## Usage
```
import torch
from transformers import RobertaTokenizer, RobertaConfig, RobertaModel
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = RobertaTokenizer.from_pretrained("DeepSoftwareAnalytics/CoCoSoDa")
model = RobertaModel.from_pretrained("DeepSoftwareAnalytics/CoCoSoDa")
```
## Reference
Shi, E., Wang, Y., Gu, W., Du, L., Zhang, H., Han, S., ... & Sun, H. (2022). [CoCoSoDa: Effective Contrastive Learning for Code Search](https://arxiv.org/abs/2204.03293). ICSE2023.
|
BertChristiaens/EmojiPredictor
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"DistilBertForTokenClassification"
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| 6 | null |
Flan-T5 model was trined with section 32 documents to build an extractive QA system
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi2-colab
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model
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: 0.8566666666666667
- name: F1
type: f1
value: 0.858085808580858
---
<!-- 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. -->
# finetuning-sentiment-model
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: 0.7491
- Accuracy: 0.8567
- F1: 0.8581
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigDaddyNe1L/Hhaa
|
[] | null |
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}
}
| 0 | null |
---
library_name: transformers
---
# Example 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. -->
```
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("luqh/ClinicalT5-large")
model = T5ForConditionalGeneration.from_pretrained("luqh/ClinicalT5-large", from_flax=True)
```
# Citation
If you find this resource useful, please consider citing our work: [ClinicalT5: A Generative Language Model for Clinical Text](https://aclanthology.org/2022.findings-emnlp.398/)
```
@inproceedings{lu-etal-2022-clinicalt5,
title = "{C}linical{T}5: A Generative Language Model for Clinical Text",
author = "Lu, Qiuhao and
Dou, Dejing and
Nguyen, Thien",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.398",
pages = "5436--5443",
abstract = "In the past few years, large pre-trained language models (PLMs) have been widely adopted in different areas and have made fundamental improvements over a variety of downstream tasks in natural language processing (NLP). Meanwhile, domain-specific variants of PLMs are being proposed to address the needs of domains that demonstrate a specific pattern of writing and vocabulary, e.g., BioBERT for the biomedical domain and ClinicalBERT for the clinical domain. Recently, generative language models like BART and T5 are gaining popularity with their competitive performance on text generation as well as on tasks cast as generative problems. However, in the clinical domain, such domain-specific generative variants are still underexplored. To address this need, our work introduces a T5-based text-to-text transformer model pre-trained on clinical text, i.e., ClinicalT5. We evaluate the proposed model both intrinsically and extrinsically over a diverse set of tasks across multiple datasets, and show that ClinicalT5 dramatically outperforms T5 in the domain-specific tasks and compares favorably with its close baselines.",
}
'''
|
BigSalmon/FormalBerta2
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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},
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},
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},
"translation_en_to_fr": {
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}
}
| 16 | null |
---
license: mit
language:
- en
widget:
- text: >-
A nervous passenger is about to book a flight ticket, and he asks the
airlines' ticket seller, 'I hope your planes are safe. Do they have a good
track record for safety?' The airline agent replies, 'Sir, I can guarantee
you, we've never had a plane that has crashed more than once.'
example_title: A joke
- text: >-
Let me, however, hasten to assure that I am the same Gandhi as I was in
1920. I have not changed in any fundamental respect. I attach the same
importance to nonviolence that I did then. If at all, my emphasis on it has
grown stronger. There is no real contradiction between the present
resolution and my previous writings and utterances.
example_title: Not a joke
tags:
- deberta
- deberta-v3
---
### What is this?
This model has been developed to detect "narrative-style" jokes, stories and anecdotes (i.e. they are narrated as a story) spoken during speeches or conversations etc. It works best when jokes/anecdotes are at least 40 words or longer. It is based on [Moritz Laurer's DeBERTa-v3](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli).
The training dataset was a private collection of around 2000 jokes. This model has not been trained or tested on one-liners, puns or Reddit-style language-manipulation jokes such as knock-knock, Q&A jokes etc.
See the example in the inference widget or How to use section for what constitues a narrative-style joke.
For a slightly less accurate model (0.4% less) that is 65% faster at inference, see the [Roberta model](https://huggingface.co/Reggie/muppet-roberta-base-joke_detector). For a much more inaccurate model (2.9% less) that is way faster at inference, see the [distilbert model](https://huggingface.co/Reggie/distilbert-joke_detector).
### Install these first
You'll need to pip install transformers & maybe sentencepiece
### How to use
```python
from transformers import pipeline
import torch
device = 0 if torch.cuda.is_available() else -1
model_name = 'Reggie/DeBERTa-v3-base-joke_detector/'
max_seq_len = 510
pipe = pipeline(model=model_name, device=device, truncation=True, max_length=max_seq_len)
is_it_a_joke = """A nervous passenger is about to book a flight ticket, and he asks the airlines' ticket seller, "I hope your planes are safe. Do they have a good track record for safety?" The airline agent replies, "Sir, I can guarantee you, we've never had a plane that has crashed more than once." """
result = pipe(is_it_a_joke) # [{'label': 'LABEL_1', 'score': 0.7313136458396912}]
print('This is a joke') if result[0]['label'] == 'LABEL_1' else print('This is not a joke')
```
|
BigSalmon/FormalBerta3
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 4 | 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: 659.00 +/- 302.20
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 jannikskytt -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 jannikskytt -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 jannikskytt
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('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)])
```
|
BigSalmon/FormalRobertaa
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "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": {
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 5 | 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: AlexChe/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/FormalRobertaaa
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "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": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
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"prefix": null
}
}
}
| 12 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: result
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. -->
# result
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigSalmon/GPT2HardArticleEasyArticle
|
[
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
| 7 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **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: Write your model_id: cfalholt/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/GPT2HardandEasy
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 9 | null |
---
license: openrail
language:
- en
pipeline_tag: image-segmentation
---
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
### Description
Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image.
It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation.
The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities.
## Parameters
```
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/mit-b4",
num_labels=2,
id2label=id2label,
label2id=label2id, )
```
## Usage
```python
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
# Transforms
_transform = A.Compose([
A.Resize(height = 512, width=512),
ToTensorV2(),
])
trans_image = _transform(image=np.array(image))
outputs = model(trans_image['image'].float().unsqueeze(0))
logits = outputs.logits.cpu()
print(logits.shape)
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.size[::-1], # (height, width)
mode='bilinear',
align_corners=False)
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
palette = np.array([[0, 0, 0],[255, 255, 255]])
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]
```
<img src = 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'>
#Metric
Todo
#Note
This model was not built by using Huggingface based feature extractor, so automatic api could not work.
|
BigSalmon/GPTHeHe
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"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
}
}
}
| 8 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.20 +/- 25.45
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
BigSalmon/GPTNeo350MInformalToFormalLincoln2
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"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
}
}
}
| 8 | null |
---
license: apache-2.0
---
This model is trained on `LibriSpeech` dataset and can only be used for English ASR.
It's a very small model, which means it is suitable for embedded devices.
|
BigSalmon/InformalToFormalLincoln16
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"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
}
}
}
| 8 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Website_design_mockup_1 Dreambooth model trained by JacobPerera 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:
|
BigSalmon/MrLincoln6
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"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
}
}
}
| 9 | null |
---
tags:
- generated_from_trainer
model-index:
- name: rabbiberel-finetuned-xsum
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. -->
# rabbiberel-finetuned-xsum
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 223 | 5.8673 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.11.0
|
BigSalmon/MrLincoln8
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
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| 12 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds-1
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. -->
# codeparrot-ds-1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8410
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.991 | 0.33 | 1000 | 2.5183 |
| 2.2592 | 0.65 | 2000 | 2.0328 |
| 1.9112 | 0.98 | 3000 | 1.8410 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Blerrrry/Kkk
|
[] | null |
{
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetunedt
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: train
split: train
args: train
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beit-base-patch16-224-pt22k-ft22k-finetunedt
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0147
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4714 | 1.0 | 25 | 0.0147 | 1.0 |
| 0.0089 | 2.0 | 50 | 0.0008 | 1.0 |
| 0.0101 | 3.0 | 75 | 0.0003 | 1.0 |
| 0.0021 | 4.0 | 100 | 0.0002 | 1.0 |
| 0.0028 | 5.0 | 125 | 0.0001 | 1.0 |
| 0.0016 | 6.0 | 150 | 0.0001 | 1.0 |
| 0.0044 | 7.0 | 175 | 0.0001 | 1.0 |
| 0.0007 | 8.0 | 200 | 0.0001 | 1.0 |
| 0.0013 | 9.0 | 225 | 0.0001 | 1.0 |
| 0.0004 | 10.0 | 250 | 0.0001 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BlightZz/MakiseKurisu
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
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,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
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}
}
| 14 | null |
# Wav2Vec2-XLS-R-300-GL
Model train with common voice 12.0 in Galician.
|
Bloodwarrior/Chikfalay
|
[] | null |
{
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"model_type": null,
"task_specific_params": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
| 0 | null |
---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-tr-demo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: COMMON_VOICE - TR
type: common_voice
config: tr
split: train+validation
args: 'Config: tr, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4626
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| No log | 0.92 | 100 | 3.6030 | 1.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.0
- Tokenizers 0.13.2
|
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