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81
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int64 0
59.7M
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Alireza1044/albert-base-v2-wnli | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
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} | 164 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_distilgpt2_sst2_negation0.001_pretrainedTrue_epochs1
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. -->
# finetuned_distilgpt2_sst2_negation0.001_pretrainedTrue_epochs1
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2806
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7115 | 1.0 | 1322 | 3.2806 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Alireza1044/bert_classification_lm | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 35 | 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: threite/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
Alireza1044/michael_bert_lm | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny-vanilla-target-conll2003
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. -->
# tiny-vanilla-target-conll2003
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1431
- Precision: 0.7507
- Recall: 0.8177
- F1: 0.7828
- Accuracy: 0.9581
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7673 | 1.14 | 500 | 0.4291 | 0.4793 | 0.5160 | 0.4970 | 0.8920 |
| 0.3746 | 2.28 | 1000 | 0.2869 | 0.5976 | 0.6572 | 0.6260 | 0.9256 |
| 0.2869 | 3.42 | 1500 | 0.2292 | 0.6411 | 0.7184 | 0.6776 | 0.9370 |
| 0.236 | 4.56 | 2000 | 0.1988 | 0.6805 | 0.7516 | 0.7143 | 0.9438 |
| 0.2026 | 5.69 | 2500 | 0.1772 | 0.7047 | 0.7718 | 0.7367 | 0.9482 |
| 0.1798 | 6.83 | 3000 | 0.1649 | 0.7179 | 0.7864 | 0.7506 | 0.9514 |
| 0.158 | 7.97 | 3500 | 0.1559 | 0.7256 | 0.7987 | 0.7604 | 0.9543 |
| 0.1415 | 9.11 | 4000 | 0.1500 | 0.7379 | 0.8034 | 0.7693 | 0.9563 |
| 0.127 | 10.25 | 4500 | 0.1462 | 0.7532 | 0.8134 | 0.7821 | 0.9573 |
| 0.1173 | 11.39 | 5000 | 0.1431 | 0.7507 | 0.8177 | 0.7828 | 0.9581 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AlirezaBaneshi/testPersianQA | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"BertForQuestionAnswering"
],
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_distilgpt2_sst2_negation0.01_pretrainedTrue_epochs1
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. -->
# finetuned_distilgpt2_sst2_negation0.01_pretrainedTrue_epochs1
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2761
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7102 | 1.0 | 1323 | 3.2761 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Aliskin/xlm-roberta-base-finetuned-marc | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_distilgpt2_sst2_negation0.1_pretrainedTrue_epochs1
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. -->
# finetuned_distilgpt2_sst2_negation0.1_pretrainedTrue_epochs1
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2806
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7067 | 1.0 | 1329 | 3.2806 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Aliyyu/Keren | []
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} | 0 | 2023-01-16T10:15:36Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: tiny-mlm-snli-target-glue-qqp
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. -->
# tiny-mlm-snli-target-glue-qqp
This model is a fine-tuned version of [muhtasham/tiny-mlm-snli](https://huggingface.co/muhtasham/tiny-mlm-snli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4222
- Accuracy: 0.7880
- F1: 0.7489
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5788 | 0.04 | 500 | 0.5176 | 0.7289 | 0.6776 |
| 0.5105 | 0.09 | 1000 | 0.4825 | 0.7519 | 0.7012 |
| 0.4971 | 0.13 | 1500 | 0.4913 | 0.7412 | 0.7140 |
| 0.4815 | 0.18 | 2000 | 0.4669 | 0.7540 | 0.7200 |
| 0.4726 | 0.22 | 2500 | 0.4566 | 0.7630 | 0.7273 |
| 0.4572 | 0.26 | 3000 | 0.4494 | 0.7682 | 0.7348 |
| 0.4579 | 0.31 | 3500 | 0.4467 | 0.7694 | 0.7384 |
| 0.451 | 0.35 | 4000 | 0.4459 | 0.7696 | 0.7400 |
| 0.4519 | 0.4 | 4500 | 0.4442 | 0.7686 | 0.7411 |
| 0.4417 | 0.44 | 5000 | 0.4222 | 0.7880 | 0.7489 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AllwynJ/HarryBoy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9183870967741935
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2890 | 0.7432 |
| 3.7868 | 2.0 | 636 | 1.8756 | 0.8377 |
| 3.7868 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.6929 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.9058 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Aloka/mbart50-ft-si-en | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: tiny-vanilla-target-rotten_tomatoes
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. -->
# tiny-vanilla-target-rotten_tomatoes
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7243
- Accuracy: 0.7674
- F1: 0.7672
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.628 | 1.87 | 500 | 0.5538 | 0.7195 | 0.7194 |
| 0.5067 | 3.75 | 1000 | 0.5213 | 0.7411 | 0.7398 |
| 0.4249 | 5.62 | 1500 | 0.5142 | 0.7570 | 0.7562 |
| 0.3566 | 7.49 | 2000 | 0.5391 | 0.7608 | 0.7598 |
| 0.3012 | 9.36 | 2500 | 0.5747 | 0.7720 | 0.7719 |
| 0.2553 | 11.24 | 3000 | 0.6101 | 0.7655 | 0.7650 |
| 0.2106 | 13.11 | 3500 | 0.7000 | 0.7636 | 0.7627 |
| 0.1766 | 14.98 | 4000 | 0.7243 | 0.7674 | 0.7672 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Alstractor/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
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} | 40 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: basic-q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.74
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="pneubauer/basic-q-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"])
```
|
Altidore/DuggFace | []
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} | 0 | null | ---
license: apache-2.0
---
Use at your own risk:
```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from datasets import load_dataset
import torch
feature_extractor = AutoFeatureExtractor.from_pretrained("fxmarty/tiny-testing-remote-code")
model = AutoModelForImageClassification.from_pretrained("fxmarty/tiny-testing-remote-code", trust_remote_code=True)
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```
|
Amalq/distilroberta-base-finetuned-MentalHealth | []
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} | 0 | null | ---
license: apache-2.0
language:
- en
- fr
- es
- multilingual
widget:
- text: "Critical levels of out of school children were reported, with 72% of respondents pointing to moderate to high numbers of primary school age not accessing <mask>"
---
# HumBert
HumBert (Humanitarian Bert) is a [XLM-Roberta](https://huggingface.co/xlm-roberta-base) model trained on humanitarian texts - approximately 50 million textual examples (roughly 2 billion tokens) from public humanitarian reports, law cases and news articles.
Data were collected from three main sources: [Reliefweb](https://reliefweb.int/), [UNHCR Refworld](https://www.refworld.org/) and [Europe Media Monitor News Brief](https://emm.newsbrief.eu/).
Although XLM-Roberta was trained on 100 different languages, this fine-tuning was performed on three languages, English, French and Spanish, due to the impossibility of finding a good amount of such kind of humanitarian data in other languages.
## Intended uses
To the best of our knowledge, HumBert is the first language model adapted on humanitarian topics, which often use a very specific language, making adaptation to downstream tasks (such as dister responses text classification) more effective.
This model is primarily aimed at being fine-tuned on tasks such as sequence classification or token classification.
## Benchmarks
Soon...
## Usage
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained('nlp-thedeep/humbert')
model = AutoModelForMaskedLM.from_pretrained("nlp-thedeep/humbert")
# prepare input
text = "YOUR TEXT"
encoded_input = tokenizer(text, return_tensors='pt')
# forward pass
output = model(**encoded_input)
``` |
Amalq/distilroberta-base-finetuned-anxiety-depression | []
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} | 0 | null |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- wildcard
datasets:
- TrpFrog/trpfrog-icons
widget:
- text: an icon of trpfrog
---
# DreamBooth model for the trpfrog concept trained by Prgckwb on the TrpFrog/trpfrog-icons dataset.
This is a Stable Diffusion model fine-tuned on the trpfrog concept with DreamBooth. It can be used by modifying the `instance_prompt`: **an icon of trpfrog** |
Amalq/roberta-base-finetuned-schizophreniaReddit2 | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: small-vanilla-target-conll2003
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. -->
# small-vanilla-target-conll2003
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1128
- Precision: 0.8947
- Recall: 0.9165
- F1: 0.9055
- Accuracy: 0.9782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.217 | 1.14 | 500 | 0.0895 | 0.8414 | 0.8846 | 0.8624 | 0.9725 |
| 0.0723 | 2.28 | 1000 | 0.0808 | 0.8853 | 0.8975 | 0.8914 | 0.9762 |
| 0.0427 | 3.42 | 1500 | 0.0807 | 0.8886 | 0.9118 | 0.9001 | 0.9778 |
| 0.0274 | 4.56 | 2000 | 0.0816 | 0.8983 | 0.9142 | 0.9062 | 0.9790 |
| 0.0186 | 5.69 | 2500 | 0.0910 | 0.8990 | 0.9201 | 0.9094 | 0.9788 |
| 0.0132 | 6.83 | 3000 | 0.0933 | 0.8978 | 0.9180 | 0.9078 | 0.9789 |
| 0.0097 | 7.97 | 3500 | 0.0954 | 0.9025 | 0.9211 | 0.9117 | 0.9798 |
| 0.0076 | 9.11 | 4000 | 0.1003 | 0.9007 | 0.9221 | 0.9113 | 0.9794 |
| 0.0058 | 10.25 | 4500 | 0.1037 | 0.9032 | 0.9222 | 0.9126 | 0.9798 |
| 0.005 | 11.39 | 5000 | 0.1128 | 0.8947 | 0.9165 | 0.9055 | 0.9782 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AmazonScience/qanlu | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:atis",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
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} | 494 | 2023-01-16T10:35:17Z | ---
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="RisiPisi/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"])
```
|
AmitT/test | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.11 +/- 22.80
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Andres2015/HiggingFaceTest | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: tiny-mlm-wikitext-target-rotten_tomatoes
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. -->
# tiny-mlm-wikitext-target-rotten_tomatoes
This model is a fine-tuned version of [muhtasham/tiny-mlm-wikitext](https://huggingface.co/muhtasham/tiny-mlm-wikitext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8868
- Accuracy: 0.7533
- F1: 0.7528
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6236 | 1.87 | 500 | 0.5413 | 0.7289 | 0.7285 |
| 0.4892 | 3.75 | 1000 | 0.5137 | 0.7448 | 0.7435 |
| 0.4112 | 5.62 | 1500 | 0.5224 | 0.7636 | 0.7629 |
| 0.3454 | 7.49 | 2000 | 0.5365 | 0.7627 | 0.7624 |
| 0.2899 | 9.36 | 2500 | 0.5962 | 0.7655 | 0.7651 |
| 0.2447 | 11.24 | 3000 | 0.6489 | 0.7561 | 0.7554 |
| 0.2025 | 13.11 | 3500 | 0.6943 | 0.7692 | 0.7688 |
| 0.1671 | 14.98 | 4000 | 0.7455 | 0.7627 | 0.7621 |
| 0.1418 | 16.85 | 4500 | 0.7962 | 0.7608 | 0.7600 |
| 0.1239 | 18.73 | 5000 | 0.8868 | 0.7533 | 0.7528 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:common_voice",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0"
]
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],
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}
} | 6 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/4452/futaallv7 |
AnonymousSub/AR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: amyeroberts/my_food_classifier
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. -->
# amyeroberts/my_food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.5833
- Validation Loss: 4.5438
- Train Accuracy: 0.125
- 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': 3e-05, 'decay_steps': 20000, '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: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 4.5833 | 4.5438 | 0.125 | 0 |
### Framework versions
- Transformers 4.26.0.dev0
- TensorFlow 2.10.0
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AnonymousSub/AR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 6 | null | ---
language: en
inference: false
tags:
- onnx
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ONNX export of bert-base-uncased
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Model variations
BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.
Chinese and multilingual uncased and cased versions followed shortly after.
Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.
Other 24 smaller models are released afterward.
The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github.
| Model | #params | Language |
|------------------------|--------------------------------|-------|
| [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M | English |
| [`bert-large-uncased`](https://huggingface.co/bert-large-uncased) | 340M | English | sub
| [`bert-base-cased`](https://huggingface.co/bert-base-cased) | 110M | English |
| [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M | English |
| [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M | Chinese |
| [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple |
| [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English |
| [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English |
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
fine-tuned versions of a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.08774490654468536,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a new model. [SEP]",
'score': 0.05338378623127937,
'token': 2047,
'token_str': 'new'},
{'sequence': "[CLS] hello i'm a super model. [SEP]",
'score': 0.04667217284440994,
'token': 3565,
'token_str': 'super'},
{'sequence': "[CLS] hello i'm a fine model. [SEP]",
'score': 0.027095865458250046,
'token': 2986,
'token_str': 'fine'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")
[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
'score': 0.09747550636529922,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the man worked as a waiter. [SEP]',
'score': 0.0523831807076931,
'token': 15610,
'token_str': 'waiter'},
{'sequence': '[CLS] the man worked as a barber. [SEP]',
'score': 0.04962705448269844,
'token': 13362,
'token_str': 'barber'},
{'sequence': '[CLS] the man worked as a mechanic. [SEP]',
'score': 0.03788609802722931,
'token': 15893,
'token_str': 'mechanic'},
{'sequence': '[CLS] the man worked as a salesman. [SEP]',
'score': 0.037680890411138535,
'token': 18968,
'token_str': 'salesman'}]
>>> unmasker("The woman worked as a [MASK].")
[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
'score': 0.21981462836265564,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the woman worked as a waitress. [SEP]',
'score': 0.1597415804862976,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the woman worked as a maid. [SEP]',
'score': 0.1154729500412941,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
'score': 0.037968918681144714,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the woman worked as a cook. [SEP]',
'score': 0.03042375110089779,
'token': 5660,
'token_str': 'cook'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus, and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
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}
}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.91 +/- 16.52
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/SR_rule_based_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
}
} | 6 | 2023-01-16T15:30:39Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1653
- F1: 0.8564
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2913 | 1.0 | 715 | 0.1796 | 0.8264 |
| 0.1461 | 2.0 | 1430 | 0.1617 | 0.8468 |
| 0.0945 | 3.0 | 2145 | 0.1653 | 0.8564 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
}
} | 2 | 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: NielsV/ppo-snowballtarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AnonymousSub/consert-s10-SR | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
} | 28 | 2023-01-16T16:17:25Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: sdcid
---
###
Sample pictures of:
sdcid (use that on your prompt)
|
AnonymousSub/consert-techqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
} | 4 | 2023-01-16T16:19:48Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->
# distilbert-base-uncased-finetuned-imdb
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: 2.4721
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/declutr-biomed-roberta-papers | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
}
} | 7 | 2023-01-16T16:20:44Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-second
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 96.70 +/- 61.30
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-emanuals-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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},
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}
}
} | 29 | 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: VladDe/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AnonymousSub/declutr-emanuals-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
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}
} | 4 | 2023-01-16T16:31:39Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reforce-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/declutr-model-emanuals | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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}
} | 4 | 2023-01-16T16:33:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: small-mlm-snli-target-glue-mrpc
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. -->
# small-mlm-snli-target-glue-mrpc
This model is a fine-tuned version of [muhtasham/small-mlm-snli](https://huggingface.co/muhtasham/small-mlm-snli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0653
- Accuracy: 0.7672
- F1: 0.8435
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3892 | 4.35 | 500 | 0.8390 | 0.7696 | 0.8513 |
| 0.07 | 8.7 | 1000 | 1.4754 | 0.7623 | 0.8407 |
| 0.0271 | 13.04 | 1500 | 1.5892 | 0.7843 | 0.8523 |
| 0.0167 | 17.39 | 2000 | 1.8798 | 0.7672 | 0.8440 |
| 0.0165 | 21.74 | 2500 | 1.8089 | 0.7721 | 0.8463 |
| 0.0123 | 26.09 | 3000 | 2.0653 | 0.7672 | 0.8435 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AnonymousSub/declutr-model | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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}
} | 4 | null | ---
license: bigscience-openrail-m
---
This model is part of an experiment of pre-training one very deep and another very shallow gpt2 models with similar sizes:
* https://huggingface.co/HuggingFaceM4/gpt2-deep-1b56-ratio-33
* https://huggingface.co/HuggingFaceM4/gpt2-shallow-1b32-ratio-85
Size/ratio calculations:
- shallow:
```
NHIDDEN=2048; NLAYERS=24; SEQ_LEN=2048; VOCAB_SIZE=50257; python -c "h=$NHIDDEN; l=$NLAYERS; s=$SEQ_LEN; v=$VOCAB_SIZE; print(f'Model size: {(l*(12*h**2 + 13*h) + v*h + s*h + 2*h) / 10**9 :.2f}B, ratio={int(h/l)}')"
Model size: 1.32B, ratio=85
```
- narrow:
```
NHIDDEN=1600; NLAYERS=48; SEQ_LEN=2048; VOCAB_SIZE=50257; python -c "h=$NHIDDEN; l=$NLAYERS; s=$SEQ_LEN; v=$VOCAB_SIZE; print(f'Model size: {(l*(12*h**2 + 13*h) + v*h + s*h + 2*h) / 10**9 :.2f}B, ratio={int(h/l)}')"
Model size: 1.56B, ratio=33
```
The Megatron-Deepspeed training scripts that created these can be found here:
- https://github.com/huggingface/m4/blob/text_pretraining/experiments/pretraining/text_pretraining/shallow_gpt.slurm
- https://github.com/huggingface/m4/blob/text_pretraining/experiments/pretraining/text_pretraining/narrow_gpt.slurm
The last Megatron-Deepspeed checkpoint dumps can be found here:
- https://huggingface.co/HuggingFaceM4/gpt2-shallow-1b32-ratio-85-meg-ds-checkpoint
- https://huggingface.co/HuggingFaceM4/gpt2-deep-1b56-ratio-33-meg-ds-checkpoint
Tensorboard:
- https://huggingface.co/HuggingFaceM4/gpt2-1.35b-pile-shallow/tensorboard
- https://huggingface.co/HuggingFaceM4/gpt2-1.56b-pile-deep/tensorboard
Validate checkpoint:
```
$ python -c '\
import sys; \
mname = sys.argv[1]; \
from transformers import AutoTokenizer, AutoModelForCausalLM; \
tokenizer = AutoTokenizer.from_pretrained(mname); \
tokenizer.add_special_tokens({"pad_token": tokenizer.eos_token}); \
model = AutoModelForCausalLM.from_pretrained(mname); \
inputs = ["Hello, my dog is cute"]; \
input_tokens = tokenizer.batch_encode_plus(inputs, return_tensors="pt", padding=True)
outputs = model.generate(**input_tokens, do_sample=False); \
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True); \
print(outputs); \
' HuggingFaceM4/gpt2-shallow-1b32-ratio-85
[...]
['Hello, my dog is cute." "I\'m gonna take him home." "I\'m gonna take']
```
|
AnonymousSub/declutr-model_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
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}
} | 2 | null | ---
license: bigscience-openrail-m
---
This model is part of an experiment of pre-training one very deep and another very shallow gpt2 models with similar sizes:
* https://huggingface.co/HuggingFaceM4/gpt2-deep-1b56-ratio-33
* https://huggingface.co/HuggingFaceM4/gpt2-shallow-1b32-ratio-85
Size/ratio calculations:
- shallow:
```
NHIDDEN=2048; NLAYERS=24; SEQ_LEN=2048; VOCAB_SIZE=50257; python -c "h=$NHIDDEN; l=$NLAYERS; s=$SEQ_LEN; v=$VOCAB_SIZE; print(f'Model size: {(l*(12*h**2 + 13*h) + v*h + s*h + 2*h) / 10**9 :.2f}B, ratio={int(h/l)}')"
Model size: 1.32B, ratio=85
```
- narrow:
```
NHIDDEN=1600; NLAYERS=48; SEQ_LEN=2048; VOCAB_SIZE=50257; python -c "h=$NHIDDEN; l=$NLAYERS; s=$SEQ_LEN; v=$VOCAB_SIZE; print(f'Model size: {(l*(12*h**2 + 13*h) + v*h + s*h + 2*h) / 10**9 :.2f}B, ratio={int(h/l)}')"
Model size: 1.56B, ratio=33
```
The Megatron-Deepspeed training scripts that created these can be found here:
- https://github.com/huggingface/m4/blob/text_pretraining/experiments/pretraining/text_pretraining/shallow_gpt.slurm
- https://github.com/huggingface/m4/blob/text_pretraining/experiments/pretraining/text_pretraining/narrow_gpt.slurm
The last Megatron-Deepspeed checkpoint dumps can be found here:
- https://huggingface.co/HuggingFaceM4/gpt2-shallow-1b32-ratio-85-meg-ds-checkpoint
- https://huggingface.co/HuggingFaceM4/gpt2-deep-1b56-ratio-33-meg-ds-checkpoint
Tensorboard:
- https://huggingface.co/HuggingFaceM4/gpt2-1.35b-pile-shallow/tensorboard
- https://huggingface.co/HuggingFaceM4/gpt2-1.56b-pile-deep/tensorboard
Validate checkpoint:
```
$ python -c '\
import sys; \
mname = sys.argv[1]; \
from transformers import AutoTokenizer, AutoModelForCausalLM; \
tokenizer = AutoTokenizer.from_pretrained(mname); \
tokenizer.add_special_tokens({"pad_token": tokenizer.eos_token}); \
model = AutoModelForCausalLM.from_pretrained(mname); \
inputs = ["Hello, my dog is cute"]; \
input_tokens = tokenizer.batch_encode_plus(inputs, return_tensors="pt", padding=True)
outputs = model.generate(**input_tokens, do_sample=False); \
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True); \
print(outputs); \
' HuggingFaceM4/gpt2-deep-1b56-ratio-33
[...]
['Hello, my dog is cute!" "I\'m sorry, I\'m not a dog person." "']
```
|
AnonymousSub/declutr-model_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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}
} | 26 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo-LunarLander-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 280.28 +/- 13.35
name: mean_reward
verified: false
---
# **ppo-LunarLander-v2** Agent playing **LunarLander-v2**
This is a trained model of a **ppo-LunarLander-v2** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/declutr-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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}
} | 5 | null | ---
license: bigscience-openrail-m
---
This is last Megatron-Deepspeed checkpoint dump for this model: https://huggingface.co/HuggingFaceM4/gpt2-deep-1b56-ratio-33/
To convert to HF `transformers` format using https://github.com/bigscience-workshop/Megatron-DeepSpeed
```
cd Megatron-DeepSpeed
PYTHONPATH=. tools/convert_checkpoint/deepspeed_to_transformers.py \
--input_folder global_step298611 \
--output_folder narrow-gpt2-298611
``` |
AnonymousSub/dummy_1 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 33 | null | ---
license: bigscience-openrail-m
---
This is last Megatron-Deepspeed checkpoint dump for this model: https://huggingface.co/HuggingFaceM4/gpt2-shallow-1b32-ratio-85
To convert to HF `transformers` format using https://github.com/bigscience-workshop/Megatron-DeepSpeed
```
cd Megatron-DeepSpeed
PYTHONPATH=. tools/convert_checkpoint/deepspeed_to_transformers.py \
--input_folder global_step298611 \
--output_folder deep-gpt2-298611
``` |
AnonymousSub/hier_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
} | 8 | null | ## How to use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("Forturne/Nursing_XR_after_sur")
tokenizer = AutoTokenizer.from_pretrained("Forturne/Nursing_XR_after_sur")
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer)
pipe("ํ์๋ถ ์ฑํจ์ด ์ด๋ป๊ฒ ๋์ธ์?")
``` |
AnonymousSub/hier_triplet_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
],
"model_type": "bert",
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}
}
} | 8 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- crows_pairs
metrics:
- accuracy
model-index:
- name: crowspairs_trainer_roberta-large_finetuned
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: crows_pairs
type: crows_pairs
config: crows_pairs
split: test
args: crows_pairs
metrics:
- name: Accuracy
type: accuracy
value: 0.4966887417218543
---
<!-- 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. -->
# crowspairs_trainer_roberta-large_finetuned
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the crows_pairs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6933
- Accuracy: 0.4967
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.53 | 20 | 0.6942 | 0.5033 |
| No log | 1.05 | 40 | 0.6943 | 0.4967 |
| No log | 1.58 | 60 | 0.7100 | 0.4967 |
| No log | 2.11 | 80 | 0.6937 | 0.4967 |
| No log | 2.63 | 100 | 0.6937 | 0.4967 |
| No log | 3.16 | 120 | 0.6936 | 0.4967 |
| No log | 3.68 | 140 | 0.6931 | 0.5033 |
| No log | 4.21 | 160 | 0.6938 | 0.4967 |
| No log | 4.74 | 180 | 0.6933 | 0.4967 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/roberta-base_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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}
} | 25 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: winobias_trainer_roberta-large_finetuned
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. -->
# winobias_trainer_roberta-large_finetuned
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.4 | 20 | 0.6966 | 0.5 |
| No log | 0.8 | 40 | 0.7068 | 0.5 |
| No log | 1.2 | 60 | 0.7124 | 0.5 |
| No log | 1.6 | 80 | 0.6931 | 0.5 |
| No log | 2.0 | 100 | 0.7072 | 0.5 |
| No log | 2.4 | 120 | 0.6989 | 0.5 |
| No log | 2.8 | 140 | 0.6956 | 0.5 |
| No log | 3.2 | 160 | 0.6967 | 0.5 |
| No log | 3.6 | 180 | 0.6939 | 0.5 |
| No log | 4.0 | 200 | 0.6933 | 0.5 |
| No log | 4.4 | 220 | 0.6934 | 0.5 |
| No log | 4.8 | 240 | 0.6932 | 0.5 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_10 | [
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} | 4 | 2023-01-16T17:19:13Z |
---
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: jamiet1139/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 1 | 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: ashrek/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AnonymousSub/rule_based_hier_quadruplet_0.1_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 4 | 2023-01-16T17:35:21Z |
---
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: AndreMitri/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_squad2.0 | [
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"question-answering",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
tags:
- fastai
---
# Amazing!
๐ฅณ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using ๐ค Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner ๐ค! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 4 | null | ---
license: creativeml-openrail-m
---
# Psychanime v1(FRED)
I attempted to fine-tune SD 1.4, resulting in the creation of this particular model.
## Acknowledgements
- [Invokeai(UI)](https://invoke-ai.github.io/InvokeAI/)
- [SD 1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
## Documentation
[N/A]
## Installation
Download the model and run it in invokeai
## Demo Images
[Here](https://imgur.com/a/cvnjxaG)
|
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: bert-base-uncased-finetuned-math_punctuation-ignore_word_parts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-math_punctuation-ignore_word_parts
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1981
- Precision: 0.7843
- Recall: 0.7485
- F Score: 0.7648
- Auc: 0.9248
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F Score | Auc |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:-------:|:------:|
| 0.1064 | 0.64 | 500 | 0.1082 | 0.7558 | 0.6580 | 0.6964 | 0.9086 |
| 0.0781 | 1.27 | 1000 | 0.1025 | 0.7594 | 0.7226 | 0.7365 | 0.9261 |
| 0.0757 | 1.91 | 1500 | 0.1001 | 0.7945 | 0.6899 | 0.7302 | 0.9272 |
| 0.0538 | 2.54 | 2000 | 0.1061 | 0.7689 | 0.7348 | 0.7480 | 0.9298 |
| 0.0425 | 3.18 | 2500 | 0.1123 | 0.7806 | 0.7361 | 0.7560 | 0.9300 |
| 0.0377 | 3.81 | 3000 | 0.1159 | 0.7841 | 0.7437 | 0.7610 | 0.9292 |
| 0.0235 | 4.45 | 3500 | 0.1259 | 0.7786 | 0.7368 | 0.7561 | 0.9276 |
| 0.0227 | 5.08 | 4000 | 0.1436 | 0.7699 | 0.7448 | 0.7555 | 0.9277 |
| 0.0159 | 5.72 | 4500 | 0.1466 | 0.7715 | 0.7333 | 0.7514 | 0.9252 |
| 0.0106 | 6.35 | 5000 | 0.1574 | 0.7710 | 0.7456 | 0.7566 | 0.9276 |
| 0.0111 | 6.99 | 5500 | 0.1560 | 0.7694 | 0.7500 | 0.7595 | 0.9286 |
| 0.0074 | 7.62 | 6000 | 0.1645 | 0.7789 | 0.7511 | 0.7639 | 0.9305 |
| 0.0056 | 8.26 | 6500 | 0.1745 | 0.7887 | 0.7453 | 0.7648 | 0.9265 |
| 0.005 | 8.89 | 7000 | 0.1760 | 0.7779 | 0.7497 | 0.7629 | 0.9281 |
| 0.0038 | 9.53 | 7500 | 0.1873 | 0.7826 | 0.7505 | 0.7634 | 0.9273 |
| 0.0031 | 10.17 | 8000 | 0.1896 | 0.7855 | 0.7477 | 0.7644 | 0.9258 |
| 0.0026 | 10.8 | 8500 | 0.1929 | 0.7849 | 0.7485 | 0.7650 | 0.9263 |
| 0.0017 | 11.44 | 9000 | 0.1981 | 0.7843 | 0.7485 | 0.7648 | 0.9248 |
### Framework versions
- Transformers 4.25.1
- Pytorch 2.0.0.dev20230111
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1 | [
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} | 6 | null | ---
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
---
# **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/rule_based_only_classfn_epochs_1_shard_1_squad2.0 | [
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"question-answering",
"transformers",
"autotrain_compatible"
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} | 2 | 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: kestrel256/HuggyTheDog
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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} | 2 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 50.00 +/- 44.20
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/rule_based_roberta_hier_triplet_epochs_1_shard_1_squad2.0 | [
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"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 4 | 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: odiaz1066/PPO-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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} | 2 | 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: Hatman/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"RobertaModel"
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}
} | 7 | 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 playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Felipe474/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
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}
} | 27 | 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: 7.50 +/- 2.75
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="mertyazan/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"])
```
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
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}
}
} | 10 | 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="phonenix/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_only_classfn_twostage_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
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}
} | 2 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: phonenix-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
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="phonenix/phonenix-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"])
```
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
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}
} | 2 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-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 playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Felipe474/q-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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}
} | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-snli-target-glue-rte
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. -->
# small-mlm-snli-target-glue-rte
This model is a fine-tuned version of [muhtasham/small-mlm-snli](https://huggingface.co/muhtasham/small-mlm-snli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3146
- Accuracy: 0.5921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4047 | 6.41 | 500 | 1.4847 | 0.6318 |
| 0.0588 | 12.82 | 1000 | 2.5459 | 0.6245 |
| 0.0304 | 19.23 | 1500 | 2.8570 | 0.6101 |
| 0.0182 | 25.64 | 2000 | 3.3146 | 0.5921 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"RobertaModel"
],
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}
} | 1 | 2023-01-16T19:35:49Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
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-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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}
} | 4 | 2023-01-16T19:44:09Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: unit4_reinforce_cp
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 468.40 +/- 94.80
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/rule_based_roberta_twostagetriplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
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}
} | 24 | 2023-01-16T19:44:52Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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"RobertaModel"
],
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}
} | 5 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1649
- F1: 0.8573
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2905 | 1.0 | 715 | 0.1733 | 0.8271 |
| 0.1455 | 2.0 | 1430 | 0.1660 | 0.8429 |
| 0.0923 | 3.0 | 2145 | 0.1649 | 0.8573 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-snli-target-glue-sst2
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. -->
# small-mlm-snli-target-glue-sst2
This model is a fine-tuned version of [muhtasham/small-mlm-snli](https://huggingface.co/muhtasham/small-mlm-snli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3698
- Accuracy: 0.8842
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3914 | 0.24 | 500 | 0.3591 | 0.8406 |
| 0.3025 | 0.48 | 1000 | 0.3262 | 0.8567 |
| 0.2576 | 0.71 | 1500 | 0.3541 | 0.8601 |
| 0.2378 | 0.95 | 2000 | 0.3003 | 0.8853 |
| 0.1826 | 1.19 | 2500 | 0.3614 | 0.8716 |
| 0.1691 | 1.43 | 3000 | 0.3817 | 0.8704 |
| 0.1671 | 1.66 | 3500 | 0.3698 | 0.8842 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 27 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8416624600370183
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2643
- F1: 0.8417
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5689 | 1.0 | 191 | 0.3469 | 0.7746 |
| 0.2605 | 2.0 | 382 | 0.2802 | 0.8362 |
| 0.1695 | 3.0 | 573 | 0.2643 | 0.8417 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 5 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.77
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="bjarlestam/q-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"])
```
|
AnonymousSub/specter-bert-model | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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}
} | 6 | 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: 10.50 +/- 15.09
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/specter-bert-model_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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}
} | 2 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8282910874897791
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2494
- F1: 0.8283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8159 | 1.0 | 70 | 0.3258 | 0.7278 |
| 0.2872 | 2.0 | 140 | 0.2736 | 0.8007 |
| 0.1855 | 3.0 | 210 | 0.2494 | 0.8283 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/specter-bert-model_copy_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 26 | null | ---
tags:
- generated_from_trainer
model-index:
- name: Analysis_on_socialmedia_sentiment_on_vaccines
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. -->
# Analysis_on_socialmedia_sentiment_on_vaccines
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6998
- eval_accuracy: 0.713
- eval_runtime: 64.2098
- eval_samples_per_second: 31.148
- eval_steps_per_second: 3.893
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/specter-bert-model_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
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} | 1 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-2
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="bjarlestam/q-Taxi-v3-2", 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/specter-emanuals-model | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 6 | null | ---
datasets:
- oscar
language:
- sr
metrics:
- accuracy
pipeline_tag: fill-mask
tags:
- legal
--- |
AnonymousSub/unsup-consert-base_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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} | 6 | null | ---
datasets:
- Linaqruf/pixiv-niji-journey
- openai/webgpt_comparisons
- allenai/objaverse
language:
- de
library_name: diffusers
--- |
AnonymousSub/unsup-consert-base_squad2.0 | [
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"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 2 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6771476698483998
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3822
- F1: 0.6771
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1307 | 1.0 | 50 | 0.5745 | 0.4939 |
| 0.5178 | 2.0 | 100 | 0.4389 | 0.6472 |
| 0.3716 | 3.0 | 150 | 0.3822 | 0.6771 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/unsup-consert-emanuals | [
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} | 2 | null | ---
tags:
- conversational
---
# Bender DialoGPT Model |
AnonymousSub/unsup-consert-papers-bert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- spearmanr
model-index:
- name: small-mlm-snli-target-glue-stsb
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. -->
# small-mlm-snli-target-glue-stsb
This model is a fine-tuned version of [muhtasham/small-mlm-snli](https://huggingface.co/muhtasham/small-mlm-snli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5736
- Pearson: 0.8731
- Spearmanr: 0.8693
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 0.851 | 2.78 | 500 | 0.6505 | 0.8616 | 0.8636 |
| 0.3135 | 5.56 | 1000 | 0.5968 | 0.8663 | 0.8646 |
| 0.1838 | 8.33 | 1500 | 0.6331 | 0.8648 | 0.8614 |
| 0.1314 | 11.11 | 2000 | 0.6045 | 0.8680 | 0.8640 |
| 0.1046 | 13.89 | 2500 | 0.5871 | 0.8693 | 0.8660 |
| 0.0859 | 16.67 | 3000 | 0.6002 | 0.8690 | 0.8645 |
| 0.073 | 19.44 | 3500 | 0.5644 | 0.8720 | 0.8676 |
| 0.064 | 22.22 | 4000 | 0.5672 | 0.8734 | 0.8690 |
| 0.0576 | 25.0 | 4500 | 0.5835 | 0.8715 | 0.8680 |
| 0.052 | 27.78 | 5000 | 0.5736 | 0.8731 | 0.8693 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
AnonymousSub/unsup-consert-papers | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_distilgpt2_sst2_negation0.01_pretrainedFalse_epochs10
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. -->
# finetuned_distilgpt2_sst2_negation0.01_pretrainedFalse_epochs10
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6737 | 1.0 | 1323 | 3.2501 |
| 2.4583 | 2.0 | 2646 | 3.2498 |
| 2.3574 | 3.0 | 3969 | 3.2873 |
| 2.2477 | 4.0 | 5292 | 3.3294 |
| 2.163 | 5.0 | 6615 | 3.3888 |
| 2.1028 | 6.0 | 7938 | 3.4272 |
| 2.0517 | 7.0 | 9261 | 3.4665 |
| 2.0083 | 8.0 | 10584 | 3.5063 |
| 1.9902 | 9.0 | 11907 | 3.5309 |
| 1.9722 | 10.0 | 13230 | 3.5445 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSubmission/pretrained-model-1 | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_gpt2-medium_sst2_negation0.01_pretrainedFalse_epochs10
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. -->
# finetuned_gpt2-medium_sst2_negation0.01_pretrainedFalse_epochs10
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2693 | 1.0 | 1323 | 2.9043 |
| 1.8782 | 2.0 | 2646 | 3.0877 |
| 1.6671 | 3.0 | 3969 | 3.3430 |
| 1.4607 | 4.0 | 5292 | 3.5276 |
| 1.3258 | 5.0 | 6615 | 3.7558 |
| 1.2423 | 6.0 | 7938 | 3.8430 |
| 1.1748 | 7.0 | 9261 | 3.9389 |
| 1.124 | 8.0 | 10584 | 4.0047 |
| 1.0988 | 9.0 | 11907 | 4.0659 |
| 1.0751 | 10.0 | 13230 | 4.0937 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Anonymreign/savagebeta | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_gpt2-medium_sst2_negation0.1_pretrainedFalse_epochs10
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. -->
# finetuned_gpt2-medium_sst2_negation0.1_pretrainedFalse_epochs10
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0780
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2694 | 1.0 | 1329 | 2.9075 |
| 1.8892 | 2.0 | 2658 | 3.0947 |
| 1.6728 | 3.0 | 3987 | 3.3248 |
| 1.4683 | 4.0 | 5316 | 3.5732 |
| 1.3375 | 5.0 | 6645 | 3.7248 |
| 1.2545 | 6.0 | 7974 | 3.8060 |
| 1.1868 | 7.0 | 9303 | 3.9507 |
| 1.1398 | 8.0 | 10632 | 4.0056 |
| 1.1107 | 9.0 | 11961 | 4.0431 |
| 1.0932 | 10.0 | 13290 | 4.0780 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Anorak/nirvana | [
"pytorch",
"pegasus",
"text2text-generation",
"unk",
"dataset:Anorak/autonlp-data-Niravana-test2",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
]
| text2text-generation | {
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_distilgpt2_sst2_negation0.1_pretrainedFalse_epochs10
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. -->
# finetuned_distilgpt2_sst2_negation0.1_pretrainedFalse_epochs10
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6705 | 1.0 | 1329 | 3.2639 |
| 2.4693 | 2.0 | 2658 | 3.2593 |
| 2.3592 | 3.0 | 3987 | 3.2837 |
| 2.2476 | 4.0 | 5316 | 3.3442 |
| 2.1698 | 5.0 | 6645 | 3.3836 |
| 2.1115 | 6.0 | 7974 | 3.4261 |
| 2.06 | 7.0 | 9303 | 3.4804 |
| 2.012 | 8.0 | 10632 | 3.5077 |
| 1.9914 | 9.0 | 11961 | 3.5341 |
| 1.9781 | 10.0 | 13290 | 3.5480 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnthonyNelson/DialoGPT-small-ricksanchez | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 12 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_gpt2-large_sst2_negation0.01_pretrainedFalse_epochs10
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. -->
# finetuned_gpt2-large_sst2_negation0.01_pretrainedFalse_epochs10
This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0467
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.9716 | 1.0 | 1323 | 3.0340 |
| 1.3263 | 2.0 | 2646 | 3.2788 |
| 1.075 | 3.0 | 3969 | 3.4934 |
| 0.9094 | 4.0 | 5292 | 3.6262 |
| 0.8177 | 5.0 | 6615 | 3.7459 |
| 0.7604 | 6.0 | 7938 | 3.8292 |
| 0.7198 | 7.0 | 9261 | 3.9058 |
| 0.6912 | 8.0 | 10584 | 3.9670 |
| 0.6662 | 9.0 | 11907 | 4.0187 |
| 0.6458 | 10.0 | 13230 | 4.0467 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Anthos23/FS-distilroberta-fine-tuned | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"has_space"
]
| text-classification | {
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} | 33 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_gpt2-xl_sst2_negation0.1_pretrainedFalse_epochs10
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. -->
# finetuned_gpt2-xl_sst2_negation0.1_pretrainedFalse_epochs10
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2784
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.7811 | 1.0 | 1329 | 3.1311 |
| 1.0842 | 2.0 | 2658 | 3.4312 |
| 0.8781 | 3.0 | 3987 | 3.6260 |
| 0.7678 | 4.0 | 5316 | 3.7834 |
| 0.706 | 5.0 | 6645 | 3.9070 |
| 0.6531 | 6.0 | 7974 | 3.9999 |
| 0.6115 | 7.0 | 9303 | 4.0954 |
| 0.5744 | 8.0 | 10632 | 4.1809 |
| 0.5402 | 9.0 | 11961 | 4.2368 |
| 0.5158 | 10.0 | 13290 | 4.2784 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Anthos23/distilbert-base-uncased-finetuned-sst2 | [
"tf",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_keras_callback",
"license:apache-2.0"
]
| text-classification | {
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} | 21 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_gpt2-large_sst2_negation0.1_pretrainedFalse_epochs10
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. -->
# finetuned_gpt2-large_sst2_negation0.1_pretrainedFalse_epochs10
This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0544
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.9792 | 1.0 | 1329 | 3.0083 |
| 1.3396 | 2.0 | 2658 | 3.3183 |
| 1.0851 | 3.0 | 3987 | 3.5044 |
| 0.9187 | 4.0 | 5316 | 3.6375 |
| 0.828 | 5.0 | 6645 | 3.7741 |
| 0.7698 | 6.0 | 7974 | 3.8374 |
| 0.73 | 7.0 | 9303 | 3.9100 |
| 0.7027 | 8.0 | 10632 | 3.9658 |
| 0.6776 | 9.0 | 11961 | 4.0164 |
| 0.6614 | 10.0 | 13290 | 4.0544 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Anthos23/my-awesome-model | [
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
]
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} | 30 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_gpt2-xl_sst2_negation0.01_pretrainedFalse_epochs10
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. -->
# finetuned_gpt2-xl_sst2_negation0.01_pretrainedFalse_epochs10
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2831
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.7704 | 1.0 | 1323 | 3.1202 |
| 1.0733 | 2.0 | 2646 | 3.4126 |
| 0.867 | 3.0 | 3969 | 3.6512 |
| 0.7585 | 4.0 | 5292 | 3.7861 |
| 0.6945 | 5.0 | 6615 | 3.8881 |
| 0.6429 | 6.0 | 7938 | 3.9924 |
| 0.5997 | 7.0 | 9261 | 4.0998 |
| 0.5643 | 8.0 | 10584 | 4.1730 |
| 0.5279 | 9.0 | 11907 | 4.2446 |
| 0.4999 | 10.0 | 13230 | 4.2831 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Anthos23/sentiment-roberta-large-english-finetuned-sentiment-analysis | []
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} | 0 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/pii-pile-chunk3-0-50000
- tomekkorbak/pii-pile-chunk3-50000-100000
- tomekkorbak/pii-pile-chunk3-100000-150000
- tomekkorbak/pii-pile-chunk3-150000-200000
- tomekkorbak/pii-pile-chunk3-200000-250000
- tomekkorbak/pii-pile-chunk3-250000-300000
- tomekkorbak/pii-pile-chunk3-300000-350000
- tomekkorbak/pii-pile-chunk3-350000-400000
- tomekkorbak/pii-pile-chunk3-400000-450000
- tomekkorbak/pii-pile-chunk3-450000-500000
- tomekkorbak/pii-pile-chunk3-500000-550000
- tomekkorbak/pii-pile-chunk3-550000-600000
- tomekkorbak/pii-pile-chunk3-600000-650000
- tomekkorbak/pii-pile-chunk3-650000-700000
- tomekkorbak/pii-pile-chunk3-700000-750000
- tomekkorbak/pii-pile-chunk3-750000-800000
- tomekkorbak/pii-pile-chunk3-800000-850000
- tomekkorbak/pii-pile-chunk3-850000-900000
- tomekkorbak/pii-pile-chunk3-900000-950000
- tomekkorbak/pii-pile-chunk3-950000-1000000
- tomekkorbak/pii-pile-chunk3-1000000-1050000
- tomekkorbak/pii-pile-chunk3-1050000-1100000
- tomekkorbak/pii-pile-chunk3-1100000-1150000
- tomekkorbak/pii-pile-chunk3-1150000-1200000
- tomekkorbak/pii-pile-chunk3-1200000-1250000
- tomekkorbak/pii-pile-chunk3-1250000-1300000
- tomekkorbak/pii-pile-chunk3-1300000-1350000
- tomekkorbak/pii-pile-chunk3-1350000-1400000
- tomekkorbak/pii-pile-chunk3-1400000-1450000
- tomekkorbak/pii-pile-chunk3-1450000-1500000
- tomekkorbak/pii-pile-chunk3-1500000-1550000
- tomekkorbak/pii-pile-chunk3-1550000-1600000
- tomekkorbak/pii-pile-chunk3-1600000-1650000
- tomekkorbak/pii-pile-chunk3-1650000-1700000
- tomekkorbak/pii-pile-chunk3-1700000-1750000
- tomekkorbak/pii-pile-chunk3-1750000-1800000
- tomekkorbak/pii-pile-chunk3-1800000-1850000
- tomekkorbak/pii-pile-chunk3-1850000-1900000
- tomekkorbak/pii-pile-chunk3-1900000-1950000
model-index:
- name: jovial_clarke
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. -->
# jovial_clarke
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'gpt2'},
'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'jovial_clarke',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25177,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/2037bqrd |
Anthos23/test_trainer | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1739
- F1: 0.8535
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3067 | 1.0 | 835 | 0.1840 | 0.8085 |
| 0.1566 | 2.0 | 1670 | 0.1727 | 0.8447 |
| 0.1013 | 3.0 | 2505 | 0.1739 | 0.8535 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AntonClaesson/finetuning_test | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- sst2
model-index:
- name: finetuned_gpt2-xl_sst2_negation0.001_pretrainedTrue_epochs3
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. -->
# finetuned_gpt2-xl_sst2_negation0.001_pretrainedTrue_epochs3
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4780
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7931 | 1.0 | 1322 | 3.1094 |
| 1.1257 | 2.0 | 2644 | 3.3526 |
| 0.9337 | 3.0 | 3966 | 3.4780 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.0
- Datasets 2.8.0
- Tokenizers 0.13.2
|
gaurishhs/API | []
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} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Aran/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 8 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
# Updated model [here](https://huggingface.co/LucasDash/dash-wdm)
### Dash-Waifu-Diffusion Dreambooth model trained by [Lucas Dash](https://twitter.com/LucasDash_) 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:
|
Atarax/rick | []
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} | 0 | 2023-01-17T00:08:25Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v3-001
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="CitizenDick/q-FrozenLake-v3-001", 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"])
```
|
Atchuth/DialoGPT-small-MBOT | []
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} | 0 | null | ---
tags:
- adapter-transformers
- roberta
datasets:
- glue
---
# Adapter `WillHeld/pfadapter-roberta-base-tada-adv-NigerianEnglish` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("WillHeld/pfadapter-roberta-base-tada-adv-NigerianEnglish", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
Ayah/GPT2-DBpedia | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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"GPT2LMHeadModel"
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} | 6 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: 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: 45.10 +/- 27.99
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
|
Ayham/albert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 7 | null | ---
license: creativeml-openrail-m
language:
- ru
tags:
- Putin, speeches, Russian, politics, war
---
GPT2-S, fine-tuned on Putin's speeches scraped from kremlin.ru
over last 2 terms of his presidency 2012-2022.
๐บ๐ฆ Slava Ukraini ๐บ๐ฆ
|
Ayham/bert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 4 | null | ---
tags:
- adapter-transformers
- bert
datasets:
- glue
---
# Adapter `WillHeld/pfadapter-bert-base-uncased-tada-adv-aave` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("WillHeld/pfadapter-bert-base-uncased-tada-adv-aave", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
Ayta/Haha | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-labor_space_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-labor_space_v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-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: 2e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
AyushPJ/ai-club-inductions-21-nlp-ALBERT | [
"pytorch",
"albert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
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"AlbertForQuestionAnswering"
],
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: syabusyabu0141/afterabove
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. -->
# syabusyabu0141/afterabove
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2157
- Validation Loss: 0.0412
- 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': 2585, '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 |
|:----------:|:---------------:|:-----:|
| 0.2157 | 0.0412 | 0 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BOON/electra-xlnet | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: the cat called loulou, with snow in the background
language:
- zh
- en
---
# DreamBooth model for loulou cat
This is the model for a cat called "Lou Lou" (ๆฅผๆฅผ). The model is based on Stable Diffusion model, fine-tuned the cat taught to Stable Diffusion with DreamBooth. It trained by the member of tilake AIGC group on the dataset from internet, for entertainment only.
It can be used by modifying the `instance_prompt`: **the cat called loulou**
่ฟๆฏไธไธช็ๆ โๆฅผๆฅผโ ็ซ็ๆจกๅใ่ฏฅๆจกๅๅบไบ Stable Diffusion ๆจกๅ๏ผ้่ฟ DreamBooth ๆนๆณ่ฟ่กๅพฎ่ฐใ่ฏฅๆจกๅ็ฑ tilake AIGC ๅฐ็ปๆๅ่ฎญ็ปๅนถๅผๆพ็ป็คพๅบ๏ผไป
ไพๅจฑไนใ
ไฝ ๅฏไปฅ้่ฟๆฉๅ
ไปฅไธ โinstance_promptโ ๆฅ็ๆๅ
ณไบๆฅผๆฅผ็ไธๅๅบๆฏใๅงฟๆ๏ผ**the cat called loulou**
## Description
"Lou Lou" (ๆฅผๆฅผ) is a very popular cat online. At 0:25 on October 14, 2017, he left us forever because of illness and returned to his cat planet. As a loyal fan of "Lou Lou" (ๆฅผๆฅผ) and his expression pack, I trained a model specially used to generate the building as a memorial.
If you like this model, click the \[โค like\] button!
ๆฅผๆฅผๆฏไธๅชไบบๆฐ่ถ
้ซ็็ฝ็บข็ซใ2017ๅนด10ๆ14ๆฅ0ๆถ25ๅ๏ผๅฎๅ ็
ๆฐธ่ฟ็ฆปๅผไบๆไปฌๅๅฐๅตๆใไฝไธบๆฅผๆฅผๅๅ
ถ่กจๆ
ๅ
็ๅฟ ๅฎ็ฒไธ๏ผๆ่ฎญ็ปไบไธไธชไธ้จ็จไบ็ๆๆฅผๆฅผ็ๆจกๅ๏ผๅฐๅ
ถ็จๅฆไธ็งๆนๅผๆฐธ็๏ผไปฅไฝไธบ็บชๅฟตใๅฆๆๅๆฌข่ฏฅๆจกๅ๏ผๆฌข่ฟ็นไบฎไธๆนใlikeใๆ้ฎ~
## Examples
- Prompt: ```the cat called loulou; wearing a christmas hat, christmas lights and snow in the background``` ๏ผๅฃ่ฏๆฅผๆฅผ๏ผ
<img width="200px" height="200px" src="https://huggingface.co/tilake/loulou-cat-diffusion/resolve/main/example/1.png">
<img width="200px" height="200px" src="https://huggingface.co/tilake/loulou-cat-diffusion/resolve/main/example/2.png">
- Prompt: ```the cat called loulou; swimming in the river```๏ผๆธธๆณณๆฅผๆฅผ๏ผ
<img width="200px" height="200px" src="https://huggingface.co/tilake/loulou-cat-diffusion/resolve/main/example/3.png">
<img width="200px" height="200px" src="https://huggingface.co/tilake/loulou-cat-diffusion/resolve/main/example/4.png">
- Prompt: ```the cat called loulou curled up in a cardboard box```๏ผ็บธ็ฎฑๆฅผๆฅผ๏ผ
<img width="200px" height="200px" src="https://huggingface.co/tilake/loulou-cat-diffusion/resolve/main/example/5.png">
<img width="200px" height="200px" src="https://huggingface.co/tilake/loulou-cat-diffusion/resolve/main/example/6.png">
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('tilake/loulou-cat-diffusion')
image = pipeline("the cat called loulou, with snow in the background").images[0]
image
``` |
BSC-LT/roberta-base-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 | {
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}
} | 12 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
---
๐ข๐พ๐น๐น๐ธ๐ป๐ฝ ๐๐ฎ ๐๐ท\
๐ง[**Buymeacoffee**](https://www.buymeacoffee.com/TheSkinnyRat) |โ[**Ko-Fi**](https://ko-fi.com/TheSkinnyRat) |๐ต[**Saweria**](https://saweria.co/TheSkinnyRat)
# Info
> Trainer: [TheSkinnyRat](https://huggingface.co/TheSkinnyRat)\
> Type: Textual Inversion Embeddings
# Description
> Elaina (ใคใฌใคใ, Ireina) is the main protagonist of the Wandering Witch series.
> She is a witch with the witch name of The Ashen Witch.
> ([Fandom](https://wandering-witch.fandom.com/wiki/Elaina))
# Download
> [Model download](https://huggingface.co/TheSkinnyRat/TI-EMB_elaina/tree/main)
# Training
> Model Used: [nai-wd.ckpt](https://huggingface.co/andite/training_models/tree/main)\
> Dataset: 13 images\
> Size: 512x512\
> Steps: 7500\
> N Steps: 500
# Preview
> **Model:** [anything-v4.5-pruned.ckpt](https://huggingface.co/andite/anything-v4.0/tree/main)\
> **Model VAE:** [anything-v4.0.vae.pt](https://huggingface.co/andite/anything-v4.0/tree/main)\
> **Prompt:** masterpiece, best quality, EMB_elaina-7500\
> **Negative Prompt:** obese, (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), out of frame, extra fingers, mutated hands, (poorly drawn hands), (poorly drawn face), (mutation:1.3), (deformed:1.3), (amputee:1.3), blurry, bad anatomy, bad proportions, (extra limbs), cloned face, (disfigured:1.3), gross proportions, (malformed limbs), (missing arms), (missing legs), (extra arms), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, black and white, monochrome, censored,empty
 |
BSC-LT/roberta-base-bne-capitel-pos | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: eli5_clm-model_v1
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. -->
# eli5_clm-model_v1
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7400
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8711 | 1.0 | 1058 | 3.7586 |
| 3.7815 | 2.0 | 2116 | 3.7422 |
| 3.7321 | 3.0 | 3174 | 3.7400 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Backedman/DialoGPT-small-Anika | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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}
} | 6 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/pii-pile-chunk3-0-50000
- tomekkorbak/pii-pile-chunk3-50000-100000
- tomekkorbak/pii-pile-chunk3-100000-150000
- tomekkorbak/pii-pile-chunk3-150000-200000
- tomekkorbak/pii-pile-chunk3-200000-250000
- tomekkorbak/pii-pile-chunk3-250000-300000
- tomekkorbak/pii-pile-chunk3-300000-350000
- tomekkorbak/pii-pile-chunk3-350000-400000
- tomekkorbak/pii-pile-chunk3-400000-450000
- tomekkorbak/pii-pile-chunk3-450000-500000
- tomekkorbak/pii-pile-chunk3-500000-550000
- tomekkorbak/pii-pile-chunk3-550000-600000
- tomekkorbak/pii-pile-chunk3-600000-650000
- tomekkorbak/pii-pile-chunk3-650000-700000
- tomekkorbak/pii-pile-chunk3-700000-750000
- tomekkorbak/pii-pile-chunk3-750000-800000
- tomekkorbak/pii-pile-chunk3-800000-850000
- tomekkorbak/pii-pile-chunk3-850000-900000
- tomekkorbak/pii-pile-chunk3-900000-950000
- tomekkorbak/pii-pile-chunk3-950000-1000000
- tomekkorbak/pii-pile-chunk3-1000000-1050000
- tomekkorbak/pii-pile-chunk3-1050000-1100000
- tomekkorbak/pii-pile-chunk3-1100000-1150000
- tomekkorbak/pii-pile-chunk3-1150000-1200000
- tomekkorbak/pii-pile-chunk3-1200000-1250000
- tomekkorbak/pii-pile-chunk3-1250000-1300000
- tomekkorbak/pii-pile-chunk3-1300000-1350000
- tomekkorbak/pii-pile-chunk3-1350000-1400000
- tomekkorbak/pii-pile-chunk3-1400000-1450000
- tomekkorbak/pii-pile-chunk3-1450000-1500000
- tomekkorbak/pii-pile-chunk3-1500000-1550000
- tomekkorbak/pii-pile-chunk3-1550000-1600000
- tomekkorbak/pii-pile-chunk3-1600000-1650000
- tomekkorbak/pii-pile-chunk3-1650000-1700000
- tomekkorbak/pii-pile-chunk3-1700000-1750000
- tomekkorbak/pii-pile-chunk3-1750000-1800000
- tomekkorbak/pii-pile-chunk3-1800000-1850000
- tomekkorbak/pii-pile-chunk3-1850000-1900000
- tomekkorbak/pii-pile-chunk3-1900000-1950000
model-index:
- name: amazing_borg
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. -->
# amazing_borg
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'gpt2'},
'objective': {'alpha': 0.5, 'beta': 0.1, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'amazing_borg',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25177,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/r280m80p |
Bagus/ser-japanese | []
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}
} | 0 | 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: 31.90 +/- 16.65
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
|
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition | [
"pytorch",
"tensorboard",
"wav2vec2",
"el",
"dataset:aesdd",
"transformers",
"audio",
"audio-classification",
"speech",
"license:apache-2.0"
]
| audio-classification | {
"architectures": [
"Wav2Vec2ForSpeechClassification"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
}
} | 21 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.76
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="jwright94/q-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"])
```
|
Bala/model_name | []
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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: 539.00 +/- 147.95
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 babakc -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 babakc -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 babakc
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BaptisteDoyen/camembert-base-xnli | [
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit",
"has_space"
]
| zero-shot-classification | {
"architectures": [
"CamembertForSequenceClassification"
],
"model_type": "camembert",
"task_specific_params": {
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},
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}
} | 405,474 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of coco cat wearing awesome glasses in a forest full of sunshine
---
# DreamBooth model for the coco concept trained by avocadogogo.
This is a Stable Diffusion model fine-tuned on the coco concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of coco cat**
This model was created as part of the DreamBooth Hackathon ๐ฅ. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `cat` images for the animal theme,
for the Hugging Face DreamBooth Hackathon, from the HF CN Community,
corporated with the HeyWhale.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('avocadogogo/coco-cat-heywhale')
image = pipeline().images[0]
image
```
## cases๏ผprompt๏ผ
a photo of coco cat sitting on top of the deck of a battle ship traveling through the open sea with a lot of ships surrounding it
a photo of coco cat wearing awesome glasses in a forest full of sunshine
a cartoon digital art of coco cat
|
Barbarameerr/Barbara | []
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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: cytsai/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
Barkavi/totto-t5-base-bert-score-121K | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"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: "
}
}
} | 51 | 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: 643.50 +/- 131.28
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 juanmi1234 -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 juanmi1234 -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 juanmi1234
```
## 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)])
```
|
Barleysack/AERoberta2 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"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": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | Access to model 96harsh56/bert_t1 is restricted and you are not in the authorized list. Visit https://huggingface.co/96harsh56/bert_t1 to ask for access. |
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