modelId
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81
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list | pipeline_tag
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int64 0
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| first_commit
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Contrastive-Tension/BERT-Base-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 16 | null | ---
tags:
- generated_from_trainer
model-index:
- name: libri-alpha-0.25-Temp-1-att
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. -->
# libri-alpha-0.25-Temp-1-att
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 14.3802
- eval_wer: 0.1156
- eval_runtime: 171.0996
- eval_samples_per_second: 15.798
- eval_steps_per_second: 1.975
- epoch: 4.48
- step: 4000
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 40
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Contrastive-Tension/BERT-Base-NLI-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 9 | 2023-01-11T08:44:36Z |
---
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: andrei-saceleanu/ppo-PyramidRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Contrastive-Tension/BERT-Base-Swe-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
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}
} | 126 | 2023-01-11T08:44:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-response-gen
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-response-gen
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9994
- Rouge1: 19.7051
- Rouge2: 6.4371
- Rougel: 16.1965
- Rougelsum: 18.3535
- Gen Len: 18.94
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6936 | 0.2 | 250 | 2.3362 | 17.8551 | 5.8099 | 15.0919 | 16.3812 | 19.0 |
| 2.498 | 0.4 | 500 | 2.2190 | 18.3273 | 6.523 | 15.493 | 16.8039 | 19.0 |
| 2.4146 | 0.6 | 750 | 2.1489 | 17.6972 | 5.8853 | 14.9319 | 16.2418 | 19.0 |
| 2.3687 | 0.8 | 1000 | 2.1025 | 18.2632 | 6.5316 | 15.387 | 16.8129 | 19.0 |
| 2.3183 | 1.0 | 1250 | 2.0706 | 19.3629 | 7.0224 | 16.3958 | 17.7654 | 19.0 |
| 2.2796 | 1.2 | 1500 | 2.0421 | 18.9182 | 6.3816 | 15.2466 | 17.0433 | 19.0 |
| 2.2434 | 1.4 | 1750 | 2.0169 | 19.0978 | 6.859 | 16.1165 | 17.5023 | 18.84 |
| 2.2207 | 1.6 | 2000 | 1.9941 | 19.7175 | 6.6763 | 15.9078 | 17.922 | 18.84 |
| 2.2123 | 1.8 | 2250 | 1.9794 | 18.6988 | 6.4585 | 15.7348 | 17.2209 | 18.84 |
| 2.1911 | 2.0 | 2500 | 1.9637 | 18.2629 | 6.2245 | 15.3337 | 16.7855 | 19.0 |
| 2.1429 | 2.2 | 2750 | 1.9496 | 19.7263 | 7.1514 | 16.3016 | 17.9426 | 19.0 |
| 2.144 | 2.4 | 3000 | 1.9356 | 19.2725 | 6.5964 | 16.0789 | 17.499 | 19.0 |
| 2.1473 | 2.6 | 3250 | 1.9261 | 19.7699 | 6.8235 | 16.1405 | 17.9833 | 19.0 |
| 2.1053 | 2.8 | 3500 | 1.9156 | 19.1541 | 6.6579 | 15.8724 | 17.4114 | 19.0 |
| 2.0992 | 3.0 | 3750 | 1.9060 | 19.2953 | 6.7249 | 16.1245 | 17.5537 | 19.0 |
| 2.0767 | 3.2 | 4000 | 1.8990 | 20.23 | 7.3383 | 16.9756 | 18.6396 | 19.0 |
| 2.0874 | 3.4 | 4250 | 1.8910 | 19.595 | 7.1441 | 16.4483 | 18.1055 | 19.0 |
| 2.107 | 3.6 | 4500 | 1.8826 | 19.0376 | 6.2637 | 15.7023 | 17.0623 | 19.0 |
| 2.1078 | 3.8 | 4750 | 1.8779 | 19.7408 | 6.8087 | 16.332 | 17.9344 | 19.0 |
| 2.1134 | 4.0 | 5000 | 1.8927 | 19.8948 | 7.3009 | 16.7798 | 18.368 | 19.0 |
| 2.1336 | 4.2 | 5250 | 1.9100 | 20.1098 | 6.7404 | 16.9506 | 18.7358 | 18.93 |
| 2.1441 | 4.4 | 5500 | 1.9266 | 19.7587 | 6.2587 | 16.3207 | 18.4483 | 18.93 |
| 2.1626 | 4.6 | 5750 | 1.9609 | 19.8193 | 6.2365 | 16.3354 | 18.4035 | 18.93 |
| 2.1664 | 4.8 | 6000 | 1.9878 | 19.5972 | 6.2904 | 16.1372 | 18.3641 | 18.94 |
| 2.2302 | 5.0 | 6250 | 1.9994 | 19.7051 | 6.4371 | 16.1965 | 18.3535 | 18.94 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Contrastive-Tension/BERT-Distil-CT-STSb | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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} | 1 | 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: 485.70 +/- 36.72
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
|
Contrastive-Tension/BERT-Distil-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: semeval23-t3-st1-en-babe-distilbert-base-uncased-finetuned-sst-2-english
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. -->
# semeval23-t3-st1-en-babe-distilbert-base-uncased-finetuned-sst-2-english
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5298
- F1: 0.4120
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 22 | 3.4120 | 0.3965 |
| No log | 2.0 | 44 | 3.4436 | 0.3834 |
| No log | 3.0 | 66 | 3.5298 | 0.4120 |
| No log | 4.0 | 88 | 3.5558 | 0.4018 |
| No log | 5.0 | 110 | 3.6086 | 0.4002 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Contrastive-Tension/BERT-Distil-NLI-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
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} | 6 | 2023-01-11T08:51:09Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: results3
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. -->
# results3
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 27 | 1.8564 | 35.5763 | 12.1495 | 24.0011 | 32.6505 | 93.5333 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.6.1
- Tokenizers 0.12.1
|
Contrastive-Tension/BERT-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
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} | 7 | 2023-01-11T08:55:31Z |
---
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: FabioDataGeek/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Cooker/cicero-similis | []
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} | 0 | 2023-01-11T09:04:42Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 490.88 +/- 62.43
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
|
Coolhand/Abuela | [
"en",
"image_restoration",
"superresolution",
"license:mit"
]
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} | 0 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 9.14 +/- 10.91
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
|
CouchCat/ma_sa_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
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} | 38 | 2023-01-11T09:30:49Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### xolovelyayanos Dreambooth model trained by Maharaghi 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:
|
Coverage/sakurajimamai | []
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} | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- custom_squad_v2
model-index:
- name: kobigbird-pure31-27981638
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kobigbird-pure31-27981638
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the custom_squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0172
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 31
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 84 | 1.0241 |
| No log | 1.99 | 168 | 0.9186 |
| No log | 2.99 | 252 | 1.0172 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Coyotl/DialoGPT-test2-arthurmorgan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 7 | 2023-01-11T09:36:24Z | ---
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
|
Craak/GJ0001 | []
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}
} | 0 | null | ---
pipeline_tag: text-generation
library_name: transformers
---
An AI clone of me :) |
CracklesCreeper/Piglin-Talks-Harry-Potter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 10 | 2023-01-11T09:38:37Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 15.58 +/- 15.89
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Craftified/Bob | []
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} | 0 | 2023-01-11T09:40:45Z | ---
language:
- zh
license: apache-2.0
tags:
- mt5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/crosswoz
metrics:
- Dialog acts Accuracy
- Dialog acts F1
model-index:
- name: mt5-small-nlu-all-crosswoz
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/crosswoz
name: CrossWOZ
split: test
revision: 4a3e56082543ed9eecb9c76ef5eadc1aa0cc5ca0
metrics:
- type: Dialog acts Accuracy
value: 84.0
name: Accuracy
- type: Dialog acts F1
value: 90.1
name: F1
widget:
- text: "user: δ½ ε₯½οΌη»ζζ¨θδΈδΈͺθ―εζ―5εοΌδ»·ζ Όε¨100-200ε
ηι
εΊγ"
- text: "system: ζ±ζοΌδΈΊζ¨ζη΄’δΊδΈδΊη»ζ΅ει
εΊι½ζ²‘ζε₯θΊ«ζΏγε
Άδ»η±»εηδΈδΊι
εΊθ‘εοΌζ―ε¦εδΊ¬θ΄΅ι½ε€§ι
εΊγεδΊ¬δΊ¬δ»ͺ倧ι
εΊθΏδΊδΉζ―εΎε₯½ηοΌε°±ζ―δ»·ζ Όι«δΊδΈδΊγ"
inference:
parameters:
max_length: 100
---
# mt5-small-nlu-all-crosswoz
This model is a fine-tuned version of [mt5-small](https://huggingface.co/mt5-small) on [CrossWOZ](https://huggingface.co/datasets/ConvLab/crosswoz) both user and system utterances.
Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1 |
Craig/mGqFiPhu | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| feature-extraction | {
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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
|
Craig/paraphrase-MiniLM-L6-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| feature-extraction | {
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} | 1,026 | 2023-01-11T09:44:45Z | ---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: HME_pickle
widget:
structuredData:
anger:
- 0.13340177
- 0.26429585
- 0.75805366
disgust:
- 0.07661828
- 0.14570697
- 0.21044387
fear:
- 0.094705686
- 0.057977196
- 0.003689876
joy:
- 0.006762238
- 0.2627153
- 0.001755206
neutral:
- 0.03295978
- 0.019884355
- 0.013996695
sadness:
- 0.6507381
- 0.24445744
- 0.011482558
surprise:
- 0.004814104
- 0.00496282
- 0.000578273
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|------------------|---------|
| alpha | 1 |
| class_prior | |
| fit_prior | 1 |
| norm | 0 |
</details>
### Model Plot
The model plot is below.
<style>#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 {color: black;background-color: white;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 pre{padding: 0;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-toggleable {background-color: white;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 label.sk-toggleable__label-arrow:before {content: "βΈ";float: left;margin-right: 0.25em;color: #696969;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "βΎ";}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-item {z-index: 1;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-parallel-item:only-child::after {width: 0;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-ef10a45c-ab11-415f-9e34-128e48ded9d8 div.sk-text-repr-fallback {display: none;}</style><div id="sk-ef10a45c-ab11-415f-9e34-128e48ded9d8" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>ComplementNB()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="d067c8d8-88f7-4b0b-8cdd-d1723995b7c2" type="checkbox" checked><label for="d067c8d8-88f7-4b0b-8cdd-d1723995b7c2" class="sk-toggleable__label sk-toggleable__label-arrow">ComplementNB</label><div class="sk-toggleable__content"><pre>ComplementNB()</pre></div></div></div></div></div>
## Evaluation Results
[More Information Needed]
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
# citation_bibtex
bibtex
@inproceedings{...,year={2022}}
# model_card_authors
skops_user
# limitations
This model is purely for academic purposes.
# model_description
This is a Complement NB model trained on a poetry dataset.
|
Crasher222/kaggle-comp-test | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | {
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"BertForSequenceClassification"
],
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} | 29 | null | ---
license: apache-2.0
language: fr
library_name: transformers
thumbnail: null
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
- facebook/multilingual_librispeech
- facebook/voxpopuli
- google/fleurs
- gigant/african_accented_french
metrics:
- wer
model-index:
- name: Fine-tuned whisper-large-v2 model for ASR in French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: fr
split: test
args: fr
metrics:
- name: WER (Greedy)
type: wer
value: 8.15
- name: WER (Beam 5)
type: wer
value: 7.83
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech (MLS)
type: facebook/multilingual_librispeech
config: french
split: test
args: french
metrics:
- name: WER (Greedy)
type: wer
value: 4.20
- name: WER (Beam 5)
type: wer
value: 4.03
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: VoxPopuli
type: facebook/voxpopuli
config: fr
split: test
args: fr
metrics:
- name: WER (Greedy)
type: wer
value: 9.10
- name: WER (Beam 5)
type: wer
value: 8.66
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Fleurs
type: google/fleurs
config: fr_fr
split: test
args: fr_fr
metrics:
- name: WER (Greedy)
type: wer
value: 5.22
- name: WER (Beam 5)
type: wer
value: 4.98
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: African Accented French
type: gigant/african_accented_french
config: fr
split: test
args: fr
metrics:
- name: WER (Greedy)
type: wer
value: 4.58
- name: WER (Beam 5)
type: wer
value: 4.31
---
<style>
img {
display: inline;
}
</style>
# Fine-tuned whisper-large-v2 model for ASR in French
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2), trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and the validation splits of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Fleurs](https://huggingface.co/datasets/google/fleurs), [Multilingual TEDx](http://www.openslr.org/100), [MediaSpeech](https://www.openslr.org/108), and [African Accented French](https://huggingface.co/datasets/gigant/african_accented_french). When using the model make sure that your speech input is sampled at 16Khz. **This model doesn't predict casing or punctuation.**
## Performance
*Below are the WERs of the pre-trained models on the [Common Voice 9.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli) and [Fleurs](https://huggingface.co/datasets/google/fleurs). These results are reported in the original [paper](https://cdn.openai.com/papers/whisper.pdf).*
| Model | Common Voice 9.0 | MLS | VoxPopuli | Fleurs |
| --- | :---: | :---: | :---: | :---: |
| [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 22.7 | 16.2 | 15.7 | 15.0 |
| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 16.0 | 8.9 | 12.2 | 8.7 |
| [openai/whisper-large](https://huggingface.co/openai/whisper-large) | 14.7 | 8.9 | **11.0** | **7.7** |
| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | **13.9** | **7.3** | 11.4 | 8.3 |
*Below are the WERs of the fine-tuned models on the [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), and [Fleurs](https://huggingface.co/datasets/google/fleurs). Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of apostrophe. The results in the table are reported as `WER (greedy search) / WER (beam search with beam width 5)`.*
| Model | Common Voice 11.0 | MLS | VoxPopuli | Fleurs |
| --- | :---: | :---: | :---: | :---: |
| [bofenghuang/whisper-small-cv11-french](https://huggingface.co/bofenghuang/whisper-small-cv11-french) | 11.76 / 10.99 | 9.65 / 8.91 | 14.45 / 13.66 | 10.76 / 9.83 |
| [bofenghuang/whisper-medium-cv11-french](https://huggingface.co/bofenghuang/whisper-medium-cv11-french) | 9.03 / 8.54 | 6.34 / 5.86 | 11.64 / 11.35 | 7.13 / 6.85 |
| [bofenghuang/whisper-medium-french](https://huggingface.co/bofenghuang/whisper-medium-french) | 9.03 / 8.73 | 4.60 / 4.44 | 9.53 / 9.46 | 6.33 / 5.94 |
| [bofenghuang/whisper-large-v2-cv11-french](https://huggingface.co/bofenghuang/whisper-large-v2-cv11-french) | **8.05** / **7.67** | 5.56 / 5.28 | 11.50 / 10.69 | 5.42 / 5.05 |
| [bofenghuang/whisper-large-v2-french](https://huggingface.co/bofenghuang/whisper-large-v2-french) | 8.15 / 7.83 | **4.20** / **4.03** | **9.10** / **8.66** | **5.22** / **4.98** |
## Usage
Inference with π€ Pipeline
```python
import torch
from datasets import load_dataset
from transformers import pipeline
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load pipeline
pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-large-v2-french", device=device)
# NB: set forced_decoder_ids for generation utils
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="fr", task="transcribe")
# Load data
ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True)
test_segment = next(iter(ds_mcv_test))
waveform = test_segment["audio"]
# Run
generated_sentences = pipe(waveform, max_new_tokens=225)["text"] # greedy
# generated_sentences = pipe(waveform, max_new_tokens=225, generate_kwargs={"num_beams": 5})["text"] # beam search
# Normalise predicted sentences if necessary
```
Inference with π€ low-level APIs
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load model
model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-large-v2-french").to(device)
processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-french", language="french", task="transcribe")
# NB: set forced_decoder_ids for generation utils
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="fr", task="transcribe")
# 16_000
model_sample_rate = processor.feature_extractor.sampling_rate
# Load data
ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True)
test_segment = next(iter(ds_mcv_test))
waveform = torch.from_numpy(test_segment["audio"]["array"])
sample_rate = test_segment["audio"]["sampling_rate"]
# Resample
if sample_rate != model_sample_rate:
resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate)
waveform = resampler(waveform)
# Get feat
inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt")
input_features = inputs.input_features
input_features = input_features.to(device)
# Generate
generated_ids = model.generate(inputs=input_features, max_new_tokens=225) # greedy
# generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5) # beam search
# Detokenize
generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# Normalise predicted sentences if necessary
``` |
CrayonShinchan/fine_tune_try_1 | []
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} | 0 | 2023-01-11T09:47:15Z | ---
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: 245.64 +/- 23.52
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
...
```
|
CrisLeaf/generador-de-historias-de-tolkien | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 8 | 2023-01-11T09:47:36Z |
---
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: Art-phys/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Crives/distilbert-base-uncased-finetuned-emotion | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
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"DistilBertForSequenceClassification"
],
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}
} | 31 | 2023-01-11T09:49:27Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8638300289723342
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1358
- F1: 0.8638
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CrypticT1tan/DialoGPT-medium-harrypotter | []
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} | 0 | 2023-01-11T09:52:17Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: sdcid
---
###
Sample pictures of:
sdcid (use that on your prompt)
|
Cthyllax/DialoGPT-medium-PaladinDanse | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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}
} | 10 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.72 +/- 22.29
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
|
Culmenus/opus-mt-de-is-finetuned-de-to-is | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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}
} | 1 | 2023-01-11T10:00:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: t5-small-finetuned-eng-book-review
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-eng-book-review
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the amazon_reviews_multi dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 386 | 3.3464 | 17.6525 | 9.1043 | 16.6246 | 16.3747 | 12.3556 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_ancc | []
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} | 0 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter-v4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 40.20 +/- 25.51
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
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_ekkicc | []
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} | 0 | 2023-01-11T10:23:21Z | ---
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
|
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} | 0 | null | ---
license: apache-2.0
datasets:
- code_search_net
- neulab/conala
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
---
# T5 Code Summary
T5 Code Summary is a code model which takes python code as an input and summarizes it. |
CurtisBowser/DialoGPT-medium-sora-two | [
"pytorch",
"conversational"
]
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} | 0 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 16.90 +/- 13.16
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
|
CurtisBowser/DialoGPT-medium-sora | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 7 | 2023-01-11T10:36:11Z |
---
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: mnavas/hf-rl-snowballv1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
CyberMuffin/DialoGPT-small-ChandlerBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 9 | 2023-01-11T10:47:12Z | ---
language: en
thumbnail: http://www.huggingtweets.com/redtube/1673434106195/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/877893072729845761/dOE9f-Hy_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">RedTube</div>
<div style="text-align: center; font-size: 14px;">@redtube</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from RedTube.
| Data | RedTube |
| --- | --- |
| Tweets downloaded | 3199 |
| Retweets | 171 |
| Short tweets | 312 |
| Tweets kept | 2716 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tkrxle6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @redtube's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cejrjv7n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cejrjv7n/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/redtube')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Cyrell/Cyrell | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: out
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. -->
# out
This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
## 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
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave | []
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} | 11 | null | ---
license: apache-2.0
---
Trained on MS COCO dataset.
This model is used to classify objects in images.
Training dataset: COCO 2017
Training classes: 80 |
D3vil/DialoGPT-smaall-harrypottery | []
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} | 0 | 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: tayfen/ppo_snowballtarget_baseline
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
D3xter1922/distilbert-base-uncased-finetuned-cola | []
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} | 0 | 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: RajMoodley/PPO-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
D4RL1NG/yes | []
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} | 0 | 2023-01-11T11:14:47Z |
---
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: mnavas/hf-rl-pyramidsv1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
DHBaek/gpt2-stackoverflow-question-contents-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 14 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="vicfeuga/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"])
```
|
DJStomp/TestingSalvoNET | [
"transformers"
]
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} | 1 | 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: 4mosot/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
DKpro000/DialoGPT-small-harrypotter | []
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} | 0 | 2023-01-11T11:41:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: valence-english-distilroberta-base
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. -->
# valence-english-distilroberta-base
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0251
- Rmse: 0.1584
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
DSI/TweetBasedSA | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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} | 29 | 2023-01-11T11:42:55Z |
---
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: tayfen/ppo_pyramids_baseline
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Dutch",
"French",
"English",
"Tweets",
"Topic classification"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
} | 167 | 2023-01-11T11:53:56Z | ---
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="Abdelrahman123/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"])
```
|
DTAI-KULeuven/robbertje-1-gb-bort | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
} | 6 | 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: 284.73 +/- 14.67
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
...
```
|
DTAI-KULeuven/robbertje-1-gb-merged | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
} | 1 | 2023-01-11T11:57:15Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### TarikHaboob Dreambooth model trained by TarikHaboob 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:
|
DTAI-KULeuven/robbertje-1-gb-non-shuffled | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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},
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}
}
} | 53 | 2023-01-11T11:57:30Z | ---
tags:
- generated_from_trainer
model-index:
- name: libri-alpha-0.5-Temp-1-att
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. -->
# libri-alpha-0.5-Temp-1-att
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 31.9525
- eval_wer: 0.0993
- eval_runtime: 165.9829
- eval_samples_per_second: 16.285
- eval_steps_per_second: 4.073
- epoch: 13.45
- step: 12000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 40
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
DTAI-KULeuven/robbertje-1-gb-shuffled | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"translation_en_to_fr": {
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}
}
} | 7 | 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: 7.46 +/- 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="Abdelrahman123/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"])
```
|
alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
} | 866 | 2023-01-11T12:08:47Z |
---
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: 4mosot/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
alexandrainst/da-hatespeech-detection-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
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}
}
} | 1,719 | 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: 670.00 +/- 256.75
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 alvarobb -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 alvarobb -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 alvarobb
```
## 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)])
```
|
alexandrainst/da-subjectivivity-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"dataset:DDSC/twitter-sent",
"dataset:DDSC/europarl",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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} | 846 | 2023-01-11T12:13:33Z | ---
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: 652.00 +/- 223.91
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 cfalholt -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 cfalholt -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 cfalholt
```
## 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)])
```
|
DaWang/demo | []
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} | 0 | null | ---
license: apache-2.0
pipeline_tag: fill-mask
--- |
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"BertForQuestionAnswering"
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} | 7 | 2023-01-11T12:29:57Z | ---
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="OcelotBane/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"])
```
|
Daivakai/DialoGPT-small-saitama | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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}
} | 9 | 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
|
DamolaMack/Classyfied | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: indobert-finetuned-pos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indonlu
type: indonlu
config: posp
split: train
args: posp
metrics:
- name: Precision
type: precision
value: 0.9477284686897035
- name: Recall
type: recall
value: 0.9477284686897035
- name: F1
type: f1
value: 0.9477284686897035
- name: Accuracy
type: accuracy
value: 0.9477284686897035
---
<!-- 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. -->
# indobert-finetuned-pos
This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1762
- Precision: 0.9477
- Recall: 0.9477
- F1: 0.9477
- Accuracy: 0.9477
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 420 | 0.2238 | 0.9278 | 0.9278 | 0.9278 | 0.9278 |
| 0.3621 | 2.0 | 840 | 0.1806 | 0.9437 | 0.9437 | 0.9437 | 0.9437 |
| 0.1504 | 3.0 | 1260 | 0.1762 | 0.9477 | 0.9477 | 0.9477 | 0.9477 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Darkrider/covidbert_mednli | [
"transformers"
]
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} | 3 | 2023-01-11T13:16:15Z | ---
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: 294.16 +/- 21.42
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
...
```
|
DavidAMcIntosh/small-rick | []
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} | 0 | 2023-01-11T13:43:15Z | ---
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: 267.58 +/- 8.99
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
...
```
|
Declan/ChicagoTribune_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: DAVIDNAI-T5-HUG-93520798
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. -->
# DAVIDNAI-T5-HUG-93520798
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9216
- Validation Loss: 2.5848
- Train Rougel: tf.Tensor(0.20213354, shape=(), dtype=float32)
- 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rougel | Epoch |
|:----------:|:---------------:|:----------------------------------------------:|:-----:|
| 2.9216 | 2.5848 | tf.Tensor(0.20213354, shape=(), dtype=float32) | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Declan/FoxNews_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 3 | 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: saikiranp/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Declan/HuffPost_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: switch-base-16-finetuned-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 47.2139
widget:
- text: |-
Sid: Wanna catch a movie?
Annie: sure what do you have in mind?
Sid; the Aquaman? :D
Annie: haha isn't it a bit childish
Sid: noooooo I mean yes but it's the highest grossing movie this week
Annie: seriously?
Sid: yeah?
Annie: okay let's see what the fuss is all about
---
<!-- 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. -->
# Switch Transformer (base-16) fine-tuned om samsum for conversation summarization
This model is a fine-tuned version of [google/switch-base-16](https://huggingface.co/google/switch-base-16) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4434
- Rouge1: 47.2139
- Rouge2: 23.3399
- Rougel: 39.8364
- Rougelsum: 43.2592
- Gen Len: 16.9194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.846 | 1.0 | 3683 | 1.4857 | 45.9134 | 22.4258 | 38.9716 | 42.6169 | 17.0623 |
| 1.5734 | 2.0 | 7366 | 1.4346 | 47.574 | 24.2967 | 40.3749 | 44.2636 | 17.3790 |
| 1.38 | 3.0 | 11049 | 1.4277 | 47.9915 | 24.9077 | 40.658 | 44.5301 | 17.1406 |
| 1.2388 | 4.0 | 14732 | 1.4223 | 48.3444 | 25.4061 | 41.2776 | 45.0434 | 16.9254 |
| 1.1629 | 5.0 | 18415 | 1.4372 | 48.5991 | 25.5464 | 41.3726 | 45.0784 | 16.9890 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Declan/NPR_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | ---
tags:
- roberta
- adapter-transformers
datasets:
- glue
---
# Adapter `WillHeld/pfadapter-roberta-base-tada` 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", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
Declan/WallStreetJournal_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
tags:
- Venture-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Venture-v5
type: Venture-v5
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Venture-v5**
This is a trained model of a PPO agent playing Venture-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id Venture-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Venture-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/Venture-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Venture-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id Venture-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Venture-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
Declan/WallStreetJournal_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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}
} | 9 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5-base-news-summary-generation-2
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. -->
# t5-base-news-summary-generation-2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
]
| feature-extraction | {
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}
} | 1,614 | null | ---
license: osl-3.0
language:
- en
tags:
- conversational
---
# DialoGPT but Peppa Pig (GPTPP)
First version of GPTPP (GPT Peppa Pig) |
DeepPavlov/bert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
} | 3,009 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### ppeeddrriittoo Dreambooth model trained by sasa30 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:
|
DeepPavlov/distilrubert-base-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
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} | 6,324 | 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: Bhaskarbha/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
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}
} | 9,141 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-ft-cy
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-ft-cy
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3752
- Wer: 22.6542
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4353 | 0.83 | 1000 | 0.4644 | 30.1879 |
| 0.2679 | 1.66 | 2000 | 0.3911 | 24.5253 |
| 0.166 | 2.49 | 3000 | 0.3739 | 22.9167 |
| 0.1179 | 3.32 | 4000 | 0.3752 | 22.6542 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
DeepPavlov/xlm-roberta-large-en-ru-mnli | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
]
| text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
} | 227 | null | ---
tags:
- espnet
- audio
- speech-recognition
- openai-whisper
language: en
datasets:
- chime4
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/shihlun_asr_whisper_medium_finetuned_chime4`
This model was trained by Shih-Lun Wu (slseanwu) using the chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/chime4/asr1
train_set=tr05_multi_noisy_si284 # tr05_multi_noisy (original training data) or tr05_multi_noisy_si284 (add si284 data)
valid_set=dt05_multi_isolated_1ch_track
test_sets="dt05_real_isolated_1ch_track dt05_simu_isolated_1ch_track et05_real_isolated_1ch_track et05_simu_isolated_1ch_track"
asr_tag=whisper_medium_finetune_lr1e-5_adamw_wd1e-2_3epochs
asr_config=conf/tuning/train_asr_whisper_full.yaml
inference_config=conf/decode_asr_whisper_noctc_greedy.yaml
./asr.sh \
--skip_data_prep false \
--skip_train true \
--skip_eval false \
--lang en \
--ngpu 1 \
--nj 4 \
--stage 1 \
--stop_stage 13 \
--gpu_inference true \
--inference_nj 1 \
--token_type whisper_multilingual \
--feats_normalize '' \
--max_wav_duration 30 \
--feats_type raw \
--use_lm false \
--cleaner whisper_en \
--asr_tag "${asr_tag}" \
--asr_config "${asr_config}" \
--inference_config "${inference_config}" \
--inference_asr_model valid.acc.ave.pth \
--train_set "${train_set}" \
--valid_set "${valid_set}" \
--test_sets "${test_sets}" "$@"
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue Jan 10 04:15:30 CST 2023`
- python version: `3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0]`
- espnet version: `espnet 202211`
- pytorch version: `pytorch 1.12.1`
- Git hash: `d89be931dcc8f61437ac49cbe39a773f2054c50c`
- Commit date: `Mon Jan 9 11:06:45 2023 -0600`
## asr_whisper_medium_finetune_lr1e-5_adamw_wd1e-2_3epochs
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|24791|97.8|1.7|0.5|0.3|2.5|24.5|
|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|24792|96.1|3.0|0.9|0.5|4.4|35.6|
|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|19341|96.4|2.9|0.7|0.5|4.1|33.0|
|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|19344|93.4|5.0|1.7|0.8|7.4|41.8|
|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|24791|97.7|1.8|0.5|0.4|2.8|25.5|
|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|24792|96.0|3.3|0.8|0.7|4.8|36.0|
|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|19341|96.1|3.3|0.6|0.7|4.6|34.9|
|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|19344|92.9|5.8|1.3|1.2|8.3|43.2|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|141889|99.1|0.3|0.5|0.3|1.2|24.5|
|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|141900|98.2|0.8|1.0|0.5|2.3|35.6|
|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|110558|98.5|0.7|0.8|0.5|1.9|33.0|
|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|110572|96.5|1.6|1.9|0.8|4.3|41.8|
|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|141889|99.1|0.4|0.5|0.5|1.3|25.5|
|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|141900|98.2|0.9|0.9|0.6|2.4|36.0|
|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|110558|98.4|0.9|0.7|0.6|2.2|34.9|
|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|110572|96.3|2.0|1.7|1.2|4.9|43.2|
|
DeskDown/MarianMixFT_en-my | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"MarianMTModel"
],
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}
} | 7 | null | ---
language: cs
widget:
- text: Univerzita je zΓ‘kladem
license: mit
datasets:
- oscar
---
# Czech GPT-2 small model trained on the OSCAR dataset
This model was trained as a part of the [master thesis](https://dspace.cuni.cz/handle/20.500.11956/176356?locale-attribute=en) on the Czech part of the [OSCAR](https://huggingface.co/datasets/oscar) dataset.
## Introduction
Czech-GPT2-OSCAR (Czech GPT-2 small) is a state-of-the-art language model for Czech based on the GPT-2 small model. Unlike the original GPT-2 small model, this model is trained to predict only 512 tokens instead of 1024 as it serves as a basis for the [Czech-GPT2-Medical](https://huggingface.co/lchaloupsky/czech-gpt2-medical]).
The model was trained the Czech part of the [OSCAR](https://huggingface.co/datasets/oscar) dataset using Transfer Learning and Fine-tuning techniques in about a week on one NVIDIA A100 SXM4 40GB and with a total of 21 GB of training data.
This model was trained as a part of the master thesis as a proof-of-concept that it is possible to get a state-of-the-art language model in Czech language with smaller ressources than the original one, and in a significantly shorter time and mainly as a basis for the [Czech-GPT2-Medical](https://huggingface.co/lchaloupsky/czech-gpt2-medical) model. There was no Czech GPT-2 model available at the time the master thesis began.
It was fine-tuned from the English pre-trained GPT-2 small using the Hugging Face libraries (Transformers and Tokenizers) wrapped into the fastai v2 Deep Learning framework. All the fine-tuning fastai v2 techniques were used.
The solution is based on the [Faster than training from scratch β Fine-tuning the English GPT-2 in any language with Hugging Face and fastai v2 (practical case with Portuguese)](https://medium.com/@pierre_guillou/faster-than-training-from-scratch-fine-tuning-the-english-gpt-2-in-any-language-with-hugging-f2ec05c98787) article.
Trained model is now available on Hugging Face under [czech-gpt2-oscar](https://huggingface.co/lchaloupsky/czech-gpt2-oscar/). For more information please let me know in the discussion.
## Training/Evaluation
For more information on training the model or its evaluation, please have a look at the [thesis](https://dspace.cuni.cz/handle/20.500.11956/176356?locale-attribute=en) itself.
## GPT-2 Model description
*Note: information copied/pasted from [Model: gpt2 >> Model description](https://huggingface.co/gpt2#model-description)*
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
## How to use Czech-GPT2-OSCAR with HuggingFace (PyTorch)
*The following code use PyTorch. To use TensorFlow, check the below corresponding paragraph.*
### Load Czech-GPT2-OSCAR and its sub-word tokenizer (Byte-level BPE)
```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch
tokenizer = GPT2Tokenizer.from_pretrained("lchaloupsky/czech-gpt2-oscar")
model = GPT2LMHeadModel.from_pretrained("lchaloupsky/czech-gpt2-oscar")
# Get sequence length max of 1024
tokenizer.model_max_length=1024
# For older versions of the 'transformers' library use this
# tokenizer.max_len=1024
model.eval() # disable dropout (or leave in train mode to finetune)
```
### Generate one word
```python
# input sequence
text = "Univerzita je zΓ‘kladem"
inputs = tokenizer(text, return_tensors="pt")
# model output
outputs = model(**inputs, labels=inputs["input_ids"])
loss, logits = outputs[:2]
predicted_index = torch.argmax(logits[0, -1, :]).item()
predicted_text = tokenizer.decode([predicted_index])
# results
print('input text:', text)
print('predicted text:', predicted_text)
```
### Generate one full sequence
```python
# input sequence
text = "Univerzita je zΓ‘kladem"
inputs = tokenizer(text, return_tensors="pt") # tokenizer.encode(text, return_tensors="pt") directly for input_ids
# model output using Top-k sampling text generation method
sample_outputs = model.generate(inputs.input_ids,
pad_token_id=50256,
do_sample=True,
max_length=50, # put the token number you want
top_k=40,
num_return_sequences=1)
# generated sequence
for i, sample_output in enumerate(sample_outputs):
print("{}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist()))) # tokenizer.decode(sample_output, skip_special_tokens=True)
```
## How to use Czech-GPT2-OSCAR with HuggingFace (TensorFlow)
*The following code use TensorFlow. To use PyTorch, check the above corresponding paragraph.*
### Load Czech-GPT2-OSCAR and its sub-word tokenizer (Byte-level BPE)
```python
from transformers import GPT2Tokenizer, TFGPT2LMHeadModel
import tensorflow as tf
tokenizer = GPT2Tokenizer.from_pretrained("lchaloupsky/czech-gpt2-oscar")
model = TFGPT2LMHeadModel.from_pretrained("lchaloupsky/czech-gpt2-oscar")
# Get sequence length max of 1024
tokenizer.model_max_length=1024
# For older versions of the 'transformers' library use this
# tokenizer.max_len=1024
model.eval() # disable dropout (or leave in train mode to finetune)
```
### Generate one full sequence
```python
# input sequence
text = "Univerzita je zΓ‘kladem"
input_ids = tokenizer.encode(text, return_tensors="tf")
# model output using Top-k sampling text generation method
outputs = model.generate(input_ids, eos_token_id=50256, pad_token_id=50256,
do_sample=True,
max_length=40,
top_k=40)
print(tokenizer.decode(outputs[0])) # tokenizer.decode(outputs[0], skip_special_tokens=True)
```
## Limitations and bias
The training data used for this model come from the Czech part of the OSCAR dataset. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card:
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we donβt support use-cases that require the generated text to be true. Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes.
## Author
Czech-GPT2-OSCAR was trained and evaluated by [LukΓ‘Ε‘ ChaloupskΓ½](https://cz.linkedin.com/in/luk%C3%A1%C5%A1-chaloupsk%C3%BD-0016b8226?original_referer=https%3A%2F%2Fwww.google.com%2F) thanks to the computing power of the GPU (NVIDIA A100 SXM4 40GB) cluster of [IT4I](https://www.it4i.cz/) (VSB - Technical University of Ostrava).
## Citation
```
@article{chaloupsky2022automatic,
title={Automatic generation of medical reports from chest X-rays in Czech},
author={Chaloupsk{\`y}, Luk{\'a}{\v{s}}},
year={2022},
publisher={Charles University, Faculty of Mathematics and Physics}
}
```
|
DewiBrynJones/wav2vec2-large-xlsr-welsh | [
"cy",
"dataset:common_voice",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
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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
|
Digakive/Hsgshs | []
| null | {
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}
} | 0 | null | ---
license: openrail
---
import openai
# First, set your API key
openai.api_key = "YOUR_API_KEY"
# Next, define the prompt
prompt = 'create a workout plan for someone who wants to lose weight'
# Then, send the request to the GPT-3 API
response = openai.Completion.create(engine="text-davinci-002",
prompt=prompt,
max_tokens=1024)
# And finally, print the response
print(response["choices"][0]["text"])
|
Dilmk2/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"max_length": 1000
},
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}
} | 13 | null | ---
language:
- en
---
"Summarize: [paper]", produces a machine-written abstraction. Proof-of-concept dummy model for my SwitchTransformer finetuning code. Finetuned from Google's [google/switch-base-8](https://huggingface.co/google/switch-base-8)
Low-quality results, should not be used in a productin setting. This is a very small model that trained on the free version of Google Colab. (if you'd like to donate so I don't have to be limited by that, my github has ETH, BTC, and XTZ addresses linked) |
DimaOrekhov/cubert-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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}
} | 10 | null | ---
language:
- de
license: apache-2.0
tags:
- sbb-asr
- generated_from_trainer
datasets:
- marccgrau/sbbdata_allSNR
metrics:
- wer
model-index:
- name: Whisper Small German SBB all SNR - v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: SBB Dataset 05.01.2023
type: marccgrau/sbbdata_allSNR
args: 'config: German, split: train, test, val'
metrics:
- name: Wer
type: wer
value: 0.18325935320228282
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small German SBB all SNR - v2
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4018
- Wer: 0.1833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6097 | 0.71 | 100 | 0.9753 | 0.7838 |
| 0.754 | 1.42 | 200 | 0.6018 | 0.6906 |
| 0.5414 | 2.13 | 300 | 0.4864 | 0.5149 |
| 0.4521 | 2.84 | 400 | 0.4234 | 0.2372 |
| 0.4131 | 3.55 | 500 | 0.4018 | 0.1833 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.12.1
|
Dimedrolza/DialoGPT-small-cyberpunk | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 9 | null | ---
license: mit
datasets:
- AhmedSSoliman/DJANGO
language:
- en
metrics:
- bleu
- accuracy
pipeline_tag: translation
tags:
- Code Generation
- Machine translation
- Text generation
--- |
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
} | 38,156 | 2023-01-11T22:15:56Z | ---
license: mit
datasets:
- AhmedSSoliman/DJANGO
language:
- en
metrics:
- bleu
- accuracy
pipeline_tag: translation
tags:
- Machine Translation
- Code Generation
- Text Generation
--- |
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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},
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},
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}
}
} | 42,640 | 2023-01-11T22:18:29Z | ---
license: mit
datasets:
- AhmedSSoliman/DJANGO
language:
- en
metrics:
- bleu
- accuracy
pipeline_tag: translation
tags:
- 'Machine Translation '
- Code Generation
- Text Generation
--- |
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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"BertForMaskedLM"
],
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},
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},
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},
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}
}
} | 388,769 | null | Access to model 3mmaw/chat-app is restricted and you are not in the authorized list. Visit https://huggingface.co/3mmaw/chat-app to ask for access. |
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
} | 480,510 | 2023-01-11T22:30:35Z | ---
license: apache-2.0
datasets:
- gcaillaut/cora
metrics:
- mse
- accuracy
--- |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 76,685 | 2023-01-11T22:35:12Z | ---
tags:
- image-classification
- timm
library_tag: timm
license: apache-2.0
datasets:
- imagenet-12k
---
# Model card for convnext_tiny.in12k
A ConvNeXt image classification model. Trained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) by Ross Wightman.
ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 36.9
- GMACs: 4.5
- Activations (M): 13.4
- Image size: 224 x 224
- **Papers:**
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- **Original:** https://github.com/huggingface/pytorch-image-models
- **Dataset:** ImageNet-12k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_tiny.in12k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_tiny.in12k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_tiny.in12k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 768, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
bert-large-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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}
}
} | 1,058,496 | 2023-01-11T22:35:26Z | ---
tags:
- image-classification
- timm
library_tag: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-12k
---
# Model card for convnext_tiny.in12k_ft_in1k
A ConvNeXt image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman.
ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program.
Fine-tuning performed on 8x GPU [Lambda Labs](https://lambdalabs.com/) cloud instances.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 28.6
- GMACs: 4.5
- Activations (M): 13.4
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- **Original:** https://github.com/huggingface/pytorch-image-models
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-12k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_tiny.in12k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_tiny.in12k_ft_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_tiny.in12k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 768, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1,440,898 | 2023-01-11T22:35:40Z | ---
tags:
- image-classification
- timm
library_tag: timm
license: apache-2.0
datasets:
- imagenet-12k
---
# Model card for convnext_small.in12k
A ConvNeXt image classification model. Trained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) by Ross Wightman.
ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 58.5
- GMACs: 8.7
- Activations (M): 21.6
- Image size: 224 x 224
- **Papers:**
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- **Original:** https://github.com/huggingface/pytorch-image-models
- **Dataset:** ImageNet-12k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_small.in12k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_small.in12k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_small.in12k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 768, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
]
| question-answering | {
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"DistilBertForQuestionAnswering"
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}
} | 257,745 | 2023-01-11T22:40:57Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8638300289723342
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1358
- F1: 0.8638
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.1+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
distilbert-base-uncased-finetuned-sst-2-english | [
"pytorch",
"tf",
"rust",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"transformers",
"license:apache-2.0",
"model-index",
"has_space"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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},
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},
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},
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}
}
} | 3,060,704 | 2023-01-11T23:00:53Z | ---
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details> |
AdapterHub/bert-base-uncased-pf-emo | [
"bert",
"en",
"dataset:emo",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
]
| text-classification | {
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"model_type": "bert",
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}
}
} | 5 | null | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-large-squad
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. -->
# spanbert-large-squad
This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.co/SpanBERT/spanbert-large-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Adharsh2608/DialoGPT-small-harrypotter | []
| null | {
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},
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}
} | 0 | null | ---
tags:
- YarsRevenge-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: YarsRevenge-v5
type: YarsRevenge-v5
metrics:
- type: mean_reward
value: 103460.90 +/- 9549.37
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **YarsRevenge-v5**
This is a trained model of a PPO agent playing YarsRevenge-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id YarsRevenge-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id YarsRevenge-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'YarsRevenge-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
Ahmedahmed/Wewe | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-5
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. -->
# libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-5
This model is a fine-tuned version of [rohitp1/libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-3](https://huggingface.co/rohitp1/libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 30.3050
- Wer: 0.2650
## 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: 0.002
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 239.4865 | 1.12 | 400 | 31.0836 | 0.2908 |
| 210.2046 | 2.25 | 800 | 29.7831 | 0.2742 |
| 195.0478 | 3.37 | 1200 | 28.8794 | 0.2636 |
| 188.8096 | 4.49 | 1600 | 28.7458 | 0.2600 |
| 183.6592 | 5.62 | 2000 | 29.1159 | 0.2573 |
| 181.5025 | 6.74 | 2400 | 29.0081 | 0.2564 |
| 181.9954 | 7.86 | 2800 | 28.7132 | 0.2588 |
| 181.8548 | 8.99 | 3200 | 29.3207 | 0.2630 |
| 186.2524 | 10.11 | 3600 | 29.9119 | 0.2593 |
| 188.3838 | 11.24 | 4000 | 30.2963 | 0.2627 |
| 192.2623 | 12.36 | 4400 | 30.3050 | 0.2650 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0
|
Aidan8756/stephenKingModel | []
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} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
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
|
Ajaykannan6/autonlp-manthan-16122692 | [
"pytorch",
"bart",
"text2text-generation",
"unk",
"dataset:Ajaykannan6/autonlp-data-manthan",
"transformers",
"autonlp",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
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},
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}
}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- wer
model-index:
- name: my_asr_model_2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: minds14
type: minds14
config: en-US
split: train[:100]
args: en-US
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_asr_model_2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1785
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.0949 | 20.0 | 100 | 3.1923 | 1.0 |
| 3.0836 | 40.0 | 200 | 3.1769 | 1.0 |
| 3.0539 | 60.0 | 300 | 3.1766 | 1.0 |
| 3.0687 | 80.0 | 400 | 3.1853 | 1.0 |
| 3.0649 | 100.0 | 500 | 3.1785 | 1.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Akash7897/fill_mask_model | []
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} | 0 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1300458931135877125/Y9a23PV__400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Terzake</div>
<div style="text-align: center; font-size: 14px;">@terzaketv</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Terzake.
| Data | Terzake |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 122 |
| Short tweets | 1 |
| Tweets kept | 3126 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ixhmxgxm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @terzaketv's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/abekh4a2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/abekh4a2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/terzaketv')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Akashpb13/Hausa_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"has_space"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
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}
} | 31 | 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: Dharkelf/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Akashpb13/xlsr_kurmanji_kurdish | [
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kmr",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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}
} | 10 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: /content/drive/MyDrive/ColabNotebooks/img_align_celeba
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-celb-faces
## Model description
This diffusion model is trained with the [π€ Diffusers](https://github.com/huggingface/diffusers) library
on the `/content/drive/MyDrive/ColabNotebooks/img_align_celeba` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
π [TensorBoard logs](https://huggingface.co/Srivathsava/ddpm-celb-faces/tensorboard?#scalars)
|
Akashpb13/xlsr_maltese_wav2vec2 | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"mt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
} | 8 | 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: 27.00 +/- 21.74
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
|
Akbarariza/Anjar | []
| null | {
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} | 0 | 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: FBM/ppo-SnowballTarget2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Akjder/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 8 | 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: massimowww/ML-Agents-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
AlanDev/DallEMiniButBetter | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.84
- name: F1
type: f1
value: 0.8431372549019608
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3908
- Accuracy: 0.84
- F1: 0.8431
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AlanDev/dall-e-better | []
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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
|
AlbertHSU/ChineseFoodBert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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"BertModel"
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}
} | 15 | null | ---
tags:
- generated_from_trainer
model-index:
- name: model_WS_CV0
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. -->
# model_WS_CV0
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2306
- Ame: {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26}
- Anguage: {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42}
- Du Degree: {'precision': 0.8, 'recall': 0.9411764705882353, 'f1': 0.8648648648648648, 'number': 51}
- Du End Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Du University: {'precision': 0.8676470588235294, 'recall': 0.921875, 'f1': 0.893939393939394, 'number': 64}
- Ears Ex: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 22}
- Er Name: {'precision': 0.4166666666666667, 'recall': 0.8333333333333334, 'f1': 0.5555555555555556, 'number': 6}
- Kill: {'precision': 0.9810725552050473, 'recall': 0.9452887537993921, 'f1': 0.9628482972136223, 'number': 329}
- Ractice: {'precision': 0.59375, 'recall': 0.6785714285714286, 'f1': 0.6333333333333334, 'number': 28}
- Rade: {'precision': 0.8260869565217391, 'recall': 0.8260869565217391, 'f1': 0.8260869565217391, 'number': 23}
- Ummarize: {'precision': 0.9935064935064936, 'recall': 1.0, 'f1': 0.9967426710097721, 'number': 459}
- Xpertise: {'precision': 0.5555555555555556, 'recall': 0.9375, 'f1': 0.6976744186046512, 'number': 32}
- X Company: {'precision': 0.884, 'recall': 0.9525862068965517, 'f1': 0.91701244813278, 'number': 232}
- X Description: {'precision': 0.9895202608290639, 'recall': 0.9707562257253827, 'f1': 0.9800484373198017, 'number': 4377}
- X End Date: {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3}
- X Location: {'precision': 1.0, 'recall': 0.125, 'f1': 0.2222222222222222, 'number': 8}
- X Position: {'precision': 0.7441860465116279, 'recall': 0.9171974522292994, 'f1': 0.8216833095577747, 'number': 314}
- X Start Date: {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9}
- Overall Precision: 0.9573
- Overall Recall: 0.9623
- Overall F1: 0.9598
- Overall Accuracy: 0.9532
## 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: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ame | Anguage | Du Degree | Du End Date | Du University | Ears Ex | Er Name | Kill | Ractice | Rade | Ummarize | Xpertise | X Company | X Description | X End Date | X Location | X Position | X Start Date | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------:|:--------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.3587 | 1.0 | 54 | 0.6110 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26} | {'precision': 0.5454545454545454, 'recall': 0.14285714285714285, 'f1': 0.22641509433962262, 'number': 42} | {'precision': 0.7142857142857143, 'recall': 0.6862745098039216, 'f1': 0.7000000000000001, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.7241379310344828, 'recall': 0.328125, 'f1': 0.45161290322580655, 'number': 64} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 329} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 28} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23} | {'precision': 0.822262118491921, 'recall': 0.9978213507625272, 'f1': 0.9015748031496064, 'number': 459} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32} | {'precision': 0.7619047619047619, 'recall': 0.06896551724137931, 'f1': 0.12648221343873517, 'number': 232} | {'precision': 0.893684881990219, 'recall': 0.9602467443454421, 'f1': 0.9257709251101323, 'number': 4377} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.3929889298892989, 'recall': 0.678343949044586, 'f1': 0.4976635514018692, 'number': 314} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | 0.8373 | 0.8216 | 0.8294 | 0.8152 |
| 0.5019 | 2.0 | 108 | 0.3503 | {'precision': 0.4, 'recall': 0.3076923076923077, 'f1': 0.34782608695652173, 'number': 26} | {'precision': 0.8809523809523809, 'recall': 0.8809523809523809, 'f1': 0.8809523809523809, 'number': 42} | {'precision': 0.6388888888888888, 'recall': 0.9019607843137255, 'f1': 0.7479674796747967, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8142857142857143, 'recall': 0.890625, 'f1': 0.8507462686567163, 'number': 64} | {'precision': 0.7727272727272727, 'recall': 0.7727272727272727, 'f1': 0.7727272727272727, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9172413793103448, 'recall': 0.8085106382978723, 'f1': 0.8594507269789984, 'number': 329} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 28} | {'precision': 1.0, 'recall': 0.08695652173913043, 'f1': 0.16, 'number': 23} | {'precision': 0.9744680851063829, 'recall': 0.9978213507625272, 'f1': 0.9860064585575886, 'number': 459} | {'precision': 0.216, 'recall': 0.84375, 'f1': 0.34394904458598724, 'number': 32} | {'precision': 0.8194444444444444, 'recall': 0.7629310344827587, 'f1': 0.7901785714285715, 'number': 232} | {'precision': 0.9679888656924147, 'recall': 0.9533927347498287, 'f1': 0.9606353591160222, 'number': 4377} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.6461988304093568, 'recall': 0.7038216560509554, 'f1': 0.673780487804878, 'number': 314} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | 0.9176 | 0.9107 | 0.9141 | 0.9051 |
| 0.3064 | 3.0 | 162 | 0.2548 | {'precision': 0.95, 'recall': 0.7307692307692307, 'f1': 0.8260869565217392, 'number': 26} | {'precision': 0.8809523809523809, 'recall': 0.8809523809523809, 'f1': 0.8809523809523809, 'number': 42} | {'precision': 0.8979591836734694, 'recall': 0.8627450980392157, 'f1': 0.8799999999999999, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.7093023255813954, 'recall': 0.953125, 'f1': 0.8133333333333335, 'number': 64} | {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9365558912386707, 'recall': 0.9422492401215805, 'f1': 0.9393939393939393, 'number': 329} | {'precision': 1.0, 'recall': 0.07142857142857142, 'f1': 0.13333333333333333, 'number': 28} | {'precision': 0.875, 'recall': 0.6086956521739131, 'f1': 0.717948717948718, 'number': 23} | {'precision': 0.9870689655172413, 'recall': 0.9978213507625272, 'f1': 0.9924160346695556, 'number': 459} | {'precision': 0.575, 'recall': 0.71875, 'f1': 0.6388888888888888, 'number': 32} | {'precision': 0.8373983739837398, 'recall': 0.8879310344827587, 'f1': 0.8619246861924686, 'number': 232} | {'precision': 0.9857549857549858, 'recall': 0.9485949280328992, 'f1': 0.9668180230527419, 'number': 4377} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.5778688524590164, 'recall': 0.8980891719745223, 'f1': 0.7032418952618456, 'number': 314} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | 0.9349 | 0.9338 | 0.9343 | 0.9253 |
| 0.2141 | 4.0 | 216 | 0.2290 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.9069767441860465, 'recall': 0.9285714285714286, 'f1': 0.9176470588235294, 'number': 42} | {'precision': 0.8823529411764706, 'recall': 0.8823529411764706, 'f1': 0.8823529411764706, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.7560975609756098, 'recall': 0.96875, 'f1': 0.8493150684931506, 'number': 64} | {'precision': 0.7916666666666666, 'recall': 0.8636363636363636, 'f1': 0.8260869565217391, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.8155844155844156, 'recall': 0.9544072948328267, 'f1': 0.8795518207282913, 'number': 329} | {'precision': 0.64, 'recall': 0.5714285714285714, 'f1': 0.6037735849056605, 'number': 28} | {'precision': 0.8181818181818182, 'recall': 0.782608695652174, 'f1': 0.8, 'number': 23} | {'precision': 0.9912663755458515, 'recall': 0.9891067538126361, 'f1': 0.9901853871319519, 'number': 459} | {'precision': 0.5238095238095238, 'recall': 0.6875, 'f1': 0.5945945945945946, 'number': 32} | {'precision': 0.8308823529411765, 'recall': 0.9741379310344828, 'f1': 0.8968253968253969, 'number': 232} | {'precision': 0.9812195687456526, 'recall': 0.9668722869545351, 'f1': 0.9739930955120828, 'number': 4377} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7662721893491125, 'recall': 0.8248407643312102, 'f1': 0.794478527607362, 'number': 314} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | 0.9426 | 0.9502 | 0.9464 | 0.9355 |
| 0.1639 | 5.0 | 270 | 0.2122 | {'precision': 0.9523809523809523, 'recall': 0.7692307692307693, 'f1': 0.8510638297872339, 'number': 26} | {'precision': 0.9090909090909091, 'recall': 0.9523809523809523, 'f1': 0.9302325581395349, 'number': 42} | {'precision': 0.7903225806451613, 'recall': 0.9607843137254902, 'f1': 0.8672566371681416, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8472222222222222, 'recall': 0.953125, 'f1': 0.8970588235294118, 'number': 64} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9025787965616046, 'recall': 0.9574468085106383, 'f1': 0.929203539823009, 'number': 329} | {'precision': 0.5588235294117647, 'recall': 0.6785714285714286, 'f1': 0.6129032258064516, 'number': 28} | {'precision': 0.85, 'recall': 0.7391304347826086, 'f1': 0.7906976744186046, 'number': 23} | {'precision': 0.9848156182212582, 'recall': 0.9891067538126361, 'f1': 0.9869565217391305, 'number': 459} | {'precision': 0.46938775510204084, 'recall': 0.71875, 'f1': 0.5679012345679012, 'number': 32} | {'precision': 0.846441947565543, 'recall': 0.9741379310344828, 'f1': 0.905811623246493, 'number': 232} | {'precision': 0.9837775202780996, 'recall': 0.969842357779301, 'f1': 0.9767602393005063, 'number': 4377} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7296587926509186, 'recall': 0.8853503184713376, 'f1': 0.7999999999999999, 'number': 314} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | 0.9454 | 0.9569 | 0.9511 | 0.9417 |
| 0.1416 | 6.0 | 324 | 0.2282 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8478260869565217, 'recall': 0.9285714285714286, 'f1': 0.8863636363636365, 'number': 42} | {'precision': 0.8888888888888888, 'recall': 0.9411764705882353, 'f1': 0.9142857142857143, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8840579710144928, 'recall': 0.953125, 'f1': 0.9172932330827068, 'number': 64} | {'precision': 0.7916666666666666, 'recall': 0.8636363636363636, 'f1': 0.8260869565217391, 'number': 22} | {'precision': 0.3333333333333333, 'recall': 0.3333333333333333, 'f1': 0.3333333333333333, 'number': 6} | {'precision': 0.9147058823529411, 'recall': 0.9452887537993921, 'f1': 0.9297458893871449, 'number': 329} | {'precision': 0.6785714285714286, 'recall': 0.6785714285714286, 'f1': 0.6785714285714286, 'number': 28} | {'precision': 0.782608695652174, 'recall': 0.782608695652174, 'f1': 0.782608695652174, 'number': 23} | {'precision': 0.9912280701754386, 'recall': 0.9847494553376906, 'f1': 0.9879781420765027, 'number': 459} | {'precision': 0.5178571428571429, 'recall': 0.90625, 'f1': 0.6590909090909092, 'number': 32} | {'precision': 0.8643410852713178, 'recall': 0.9612068965517241, 'f1': 0.9102040816326531, 'number': 232} | {'precision': 0.9940405244338498, 'recall': 0.9527073337902673, 'f1': 0.9729351376574894, 'number': 4377} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.6681514476614699, 'recall': 0.9554140127388535, 'f1': 0.7863695937090432, 'number': 314} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | 0.9481 | 0.9492 | 0.9487 | 0.9405 |
| 0.1221 | 7.0 | 378 | 0.2031 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.9090909090909091, 'recall': 0.9523809523809523, 'f1': 0.9302325581395349, 'number': 42} | {'precision': 0.8679245283018868, 'recall': 0.9019607843137255, 'f1': 0.8846153846153846, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8493150684931506, 'recall': 0.96875, 'f1': 0.9051094890510949, 'number': 64} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 0.375, 'recall': 0.5, 'f1': 0.42857142857142855, 'number': 6} | {'precision': 0.9716981132075472, 'recall': 0.939209726443769, 'f1': 0.955177743431221, 'number': 329} | {'precision': 0.5555555555555556, 'recall': 0.8928571428571429, 'f1': 0.684931506849315, 'number': 28} | {'precision': 0.7096774193548387, 'recall': 0.9565217391304348, 'f1': 0.8148148148148149, 'number': 23} | {'precision': 0.993421052631579, 'recall': 0.9869281045751634, 'f1': 0.9901639344262295, 'number': 459} | {'precision': 0.5087719298245614, 'recall': 0.90625, 'f1': 0.651685393258427, 'number': 32} | {'precision': 0.8599221789883269, 'recall': 0.9525862068965517, 'f1': 0.9038854805725972, 'number': 232} | {'precision': 0.9863394304237092, 'recall': 0.9732693625771076, 'f1': 0.9797608095676172, 'number': 4377} | {'precision': 1.0, 'recall': 0.3333333333333333, 'f1': 0.5, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7547169811320755, 'recall': 0.89171974522293, 'f1': 0.8175182481751825, 'number': 314} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | 0.9533 | 0.9627 | 0.9580 | 0.9505 |
| 0.0928 | 8.0 | 432 | 0.1995 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.9090909090909091, 'recall': 0.9523809523809523, 'f1': 0.9302325581395349, 'number': 42} | {'precision': 0.875, 'recall': 0.9607843137254902, 'f1': 0.9158878504672897, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8787878787878788, 'recall': 0.90625, 'f1': 0.8923076923076922, 'number': 64} | {'precision': 0.9545454545454546, 'recall': 0.9545454545454546, 'f1': 0.9545454545454546, 'number': 22} | {'precision': 0.4, 'recall': 0.3333333333333333, 'f1': 0.3636363636363636, 'number': 6} | {'precision': 0.9683544303797469, 'recall': 0.9300911854103343, 'f1': 0.9488372093023255, 'number': 329} | {'precision': 0.6451612903225806, 'recall': 0.7142857142857143, 'f1': 0.6779661016949152, 'number': 28} | {'precision': 0.8636363636363636, 'recall': 0.8260869565217391, 'f1': 0.8444444444444444, 'number': 23} | {'precision': 0.9827956989247312, 'recall': 0.9956427015250545, 'f1': 0.9891774891774892, 'number': 459} | {'precision': 0.4888888888888889, 'recall': 0.6875, 'f1': 0.5714285714285714, 'number': 32} | {'precision': 0.884, 'recall': 0.9525862068965517, 'f1': 0.91701244813278, 'number': 232} | {'precision': 0.9854335260115606, 'recall': 0.9737262965501485, 'f1': 0.9795449321994945, 'number': 4377} | {'precision': 1.0, 'recall': 0.3333333333333333, 'f1': 0.5, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7265822784810126, 'recall': 0.9140127388535032, 'f1': 0.8095909732016926, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9541 | 0.9615 | 0.9578 | 0.9519 |
| 0.0725 | 9.0 | 486 | 0.2153 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.9090909090909091, 'recall': 0.9523809523809523, 'f1': 0.9302325581395349, 'number': 42} | {'precision': 0.9038461538461539, 'recall': 0.9215686274509803, 'f1': 0.9126213592233009, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8955223880597015, 'recall': 0.9375, 'f1': 0.9160305343511451, 'number': 64} | {'precision': 0.875, 'recall': 0.9545454545454546, 'f1': 0.9130434782608695, 'number': 22} | {'precision': 0.2727272727272727, 'recall': 0.5, 'f1': 0.3529411764705882, 'number': 6} | {'precision': 0.9808917197452229, 'recall': 0.9361702127659575, 'f1': 0.95800933125972, 'number': 329} | {'precision': 0.6333333333333333, 'recall': 0.6785714285714286, 'f1': 0.6551724137931035, 'number': 28} | {'precision': 0.9047619047619048, 'recall': 0.8260869565217391, 'f1': 0.8636363636363636, 'number': 23} | {'precision': 0.9848484848484849, 'recall': 0.9912854030501089, 'f1': 0.9880564603691641, 'number': 459} | {'precision': 0.4791666666666667, 'recall': 0.71875, 'f1': 0.575, 'number': 32} | {'precision': 0.8866396761133604, 'recall': 0.9439655172413793, 'f1': 0.9144050104384134, 'number': 232} | {'precision': 0.9776459854014599, 'recall': 0.9792095042266392, 'f1': 0.978427120191759, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7682926829268293, 'recall': 0.802547770700637, 'f1': 0.7850467289719627, 'number': 314} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | 0.9538 | 0.9597 | 0.9567 | 0.9506 |
| 0.0601 | 10.0 | 540 | 0.2304 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8478260869565217, 'recall': 0.9285714285714286, 'f1': 0.8863636363636365, 'number': 42} | {'precision': 0.8135593220338984, 'recall': 0.9411764705882353, 'f1': 0.8727272727272728, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8591549295774648, 'recall': 0.953125, 'f1': 0.9037037037037037, 'number': 64} | {'precision': 0.875, 'recall': 0.9545454545454546, 'f1': 0.9130434782608695, 'number': 22} | {'precision': 0.36363636363636365, 'recall': 0.6666666666666666, 'f1': 0.4705882352941177, 'number': 6} | {'precision': 0.9808917197452229, 'recall': 0.9361702127659575, 'f1': 0.95800933125972, 'number': 329} | {'precision': 0.625, 'recall': 0.7142857142857143, 'f1': 0.6666666666666666, 'number': 28} | {'precision': 0.8636363636363636, 'recall': 0.8260869565217391, 'f1': 0.8444444444444444, 'number': 23} | {'precision': 0.9934782608695653, 'recall': 0.9956427015250545, 'f1': 0.9945593035908598, 'number': 459} | {'precision': 0.5535714285714286, 'recall': 0.96875, 'f1': 0.7045454545454546, 'number': 32} | {'precision': 0.8625954198473282, 'recall': 0.9741379310344828, 'f1': 0.9149797570850203, 'number': 232} | {'precision': 0.9780621572212066, 'recall': 0.9778387023075166, 'f1': 0.9779504169998858, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7757009345794392, 'recall': 0.7929936305732485, 'f1': 0.7842519685039371, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9520 | 0.9615 | 0.9567 | 0.9492 |
| 0.0558 | 11.0 | 594 | 0.2202 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42} | {'precision': 0.8888888888888888, 'recall': 0.9411764705882353, 'f1': 0.9142857142857143, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8823529411764706, 'recall': 0.9375, 'f1': 0.9090909090909091, 'number': 64} | {'precision': 0.875, 'recall': 0.9545454545454546, 'f1': 0.9130434782608695, 'number': 22} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 6} | {'precision': 0.9810126582278481, 'recall': 0.9422492401215805, 'f1': 0.9612403100775193, 'number': 329} | {'precision': 0.6129032258064516, 'recall': 0.6785714285714286, 'f1': 0.6440677966101694, 'number': 28} | {'precision': 0.8260869565217391, 'recall': 0.8260869565217391, 'f1': 0.8260869565217391, 'number': 23} | {'precision': 0.9870410367170627, 'recall': 0.9956427015250545, 'f1': 0.9913232104121475, 'number': 459} | {'precision': 0.5471698113207547, 'recall': 0.90625, 'f1': 0.6823529411764705, 'number': 32} | {'precision': 0.8599221789883269, 'recall': 0.9525862068965517, 'f1': 0.9038854805725972, 'number': 232} | {'precision': 0.9813234955038045, 'recall': 0.9723554946310258, 'f1': 0.9768189120954786, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7352941176470589, 'recall': 0.8757961783439491, 'f1': 0.7994186046511628, 'number': 314} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | 0.9520 | 0.9607 | 0.9563 | 0.9489 |
| 0.0418 | 12.0 | 648 | 0.2433 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.9090909090909091, 'recall': 0.9523809523809523, 'f1': 0.9302325581395349, 'number': 42} | {'precision': 0.8166666666666667, 'recall': 0.9607843137254902, 'f1': 0.882882882882883, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8805970149253731, 'recall': 0.921875, 'f1': 0.9007633587786259, 'number': 64} | {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 22} | {'precision': 0.375, 'recall': 0.5, 'f1': 0.42857142857142855, 'number': 6} | {'precision': 0.9935275080906149, 'recall': 0.9331306990881459, 'f1': 0.9623824451410657, 'number': 329} | {'precision': 0.5609756097560976, 'recall': 0.8214285714285714, 'f1': 0.6666666666666667, 'number': 28} | {'precision': 0.7692307692307693, 'recall': 0.8695652173913043, 'f1': 0.8163265306122449, 'number': 23} | {'precision': 0.9934354485776805, 'recall': 0.9891067538126361, 'f1': 0.9912663755458516, 'number': 459} | {'precision': 0.5576923076923077, 'recall': 0.90625, 'f1': 0.6904761904761905, 'number': 32} | {'precision': 0.86328125, 'recall': 0.9525862068965517, 'f1': 0.9057377049180328, 'number': 232} | {'precision': 0.9892120075046904, 'recall': 0.9636737491432488, 'f1': 0.9762758939937507, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7357512953367875, 'recall': 0.9044585987261147, 'f1': 0.8114285714285714, 'number': 314} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | 0.9557 | 0.9555 | 0.9556 | 0.9473 |
| 0.0375 | 13.0 | 702 | 0.2226 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42} | {'precision': 0.7903225806451613, 'recall': 0.9607843137254902, 'f1': 0.8672566371681416, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8787878787878788, 'recall': 0.90625, 'f1': 0.8923076923076922, 'number': 64} | {'precision': 0.875, 'recall': 0.9545454545454546, 'f1': 0.9130434782608695, 'number': 22} | {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6} | {'precision': 0.9539877300613497, 'recall': 0.9452887537993921, 'f1': 0.9496183206106871, 'number': 329} | {'precision': 0.6060606060606061, 'recall': 0.7142857142857143, 'f1': 0.6557377049180327, 'number': 28} | {'precision': 0.8076923076923077, 'recall': 0.9130434782608695, 'f1': 0.8571428571428572, 'number': 23} | {'precision': 0.9934782608695653, 'recall': 0.9956427015250545, 'f1': 0.9945593035908598, 'number': 459} | {'precision': 0.5576923076923077, 'recall': 0.90625, 'f1': 0.6904761904761905, 'number': 32} | {'precision': 0.8705882352941177, 'recall': 0.9568965517241379, 'f1': 0.9117043121149897, 'number': 232} | {'precision': 0.9829767655854612, 'recall': 0.9762394334018735, 'f1': 0.9795965153599266, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7318435754189944, 'recall': 0.8343949044585988, 'f1': 0.7797619047619049, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9511 | 0.9620 | 0.9565 | 0.9506 |
| 0.0318 | 14.0 | 756 | 0.2380 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42} | {'precision': 0.8305084745762712, 'recall': 0.9607843137254902, 'f1': 0.890909090909091, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8787878787878788, 'recall': 0.90625, 'f1': 0.8923076923076922, 'number': 64} | {'precision': 0.875, 'recall': 0.9545454545454546, 'f1': 0.9130434782608695, 'number': 22} | {'precision': 0.5714285714285714, 'recall': 0.6666666666666666, 'f1': 0.6153846153846153, 'number': 6} | {'precision': 0.9459459459459459, 'recall': 0.9574468085106383, 'f1': 0.9516616314199395, 'number': 329} | {'precision': 0.6129032258064516, 'recall': 0.6785714285714286, 'f1': 0.6440677966101694, 'number': 28} | {'precision': 0.9090909090909091, 'recall': 0.8695652173913043, 'f1': 0.888888888888889, 'number': 23} | {'precision': 0.9870410367170627, 'recall': 0.9956427015250545, 'f1': 0.9913232104121475, 'number': 459} | {'precision': 0.5925925925925926, 'recall': 1.0, 'f1': 0.7441860465116279, 'number': 32} | {'precision': 0.8866396761133604, 'recall': 0.9439655172413793, 'f1': 0.9144050104384134, 'number': 232} | {'precision': 0.973070830504639, 'recall': 0.9824080420379255, 'f1': 0.9777171441564347, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7606060606060606, 'recall': 0.7993630573248408, 'f1': 0.7795031055900622, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9485 | 0.9650 | 0.9567 | 0.9492 |
| 0.0293 | 15.0 | 810 | 0.2356 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8666666666666667, 'recall': 0.9285714285714286, 'f1': 0.896551724137931, 'number': 42} | {'precision': 0.8, 'recall': 0.9411764705882353, 'f1': 0.8648648648648648, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8611111111111112, 'recall': 0.96875, 'f1': 0.911764705882353, 'number': 64} | {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 22} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 6} | {'precision': 0.9936102236421726, 'recall': 0.9452887537993921, 'f1': 0.9688473520249222, 'number': 329} | {'precision': 0.59375, 'recall': 0.6785714285714286, 'f1': 0.6333333333333334, 'number': 28} | {'precision': 0.7777777777777778, 'recall': 0.9130434782608695, 'f1': 0.84, 'number': 23} | {'precision': 0.9934782608695653, 'recall': 0.9956427015250545, 'f1': 0.9945593035908598, 'number': 459} | {'precision': 0.5471698113207547, 'recall': 0.90625, 'f1': 0.6823529411764705, 'number': 32} | {'precision': 0.8695652173913043, 'recall': 0.9482758620689655, 'f1': 0.9072164948453608, 'number': 232} | {'precision': 0.9829099307159354, 'recall': 0.9723554946310258, 'f1': 0.9776042264844378, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7209944751381215, 'recall': 0.8312101910828026, 'f1': 0.772189349112426, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9517 | 0.9589 | 0.9553 | 0.9485 |
| 0.0292 | 16.0 | 864 | 0.2313 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42} | {'precision': 0.8166666666666667, 'recall': 0.9607843137254902, 'f1': 0.882882882882883, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8805970149253731, 'recall': 0.921875, 'f1': 0.9007633587786259, 'number': 64} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 22} | {'precision': 0.45454545454545453, 'recall': 0.8333333333333334, 'f1': 0.5882352941176471, 'number': 6} | {'precision': 0.9810126582278481, 'recall': 0.9422492401215805, 'f1': 0.9612403100775193, 'number': 329} | {'precision': 0.6060606060606061, 'recall': 0.7142857142857143, 'f1': 0.6557377049180327, 'number': 28} | {'precision': 0.8333333333333334, 'recall': 0.8695652173913043, 'f1': 0.851063829787234, 'number': 23} | {'precision': 0.9913606911447084, 'recall': 1.0, 'f1': 0.9956616052060737, 'number': 459} | {'precision': 0.5370370370370371, 'recall': 0.90625, 'f1': 0.6744186046511628, 'number': 32} | {'precision': 0.884, 'recall': 0.9525862068965517, 'f1': 0.91701244813278, 'number': 232} | {'precision': 0.9915730337078652, 'recall': 0.9677861549006168, 'f1': 0.9795352063822407, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.7425, 'recall': 0.945859872611465, 'f1': 0.8319327731092437, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9582 | 0.9617 | 0.9599 | 0.9528 |
| 0.0225 | 17.0 | 918 | 0.2223 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42} | {'precision': 0.8032786885245902, 'recall': 0.9607843137254902, 'f1': 0.8750000000000001, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8805970149253731, 'recall': 0.921875, 'f1': 0.9007633587786259, 'number': 64} | {'precision': 0.9545454545454546, 'recall': 0.9545454545454546, 'f1': 0.9545454545454546, 'number': 22} | {'precision': 0.45454545454545453, 'recall': 0.8333333333333334, 'f1': 0.5882352941176471, 'number': 6} | {'precision': 0.9749216300940439, 'recall': 0.9452887537993921, 'f1': 0.9598765432098765, 'number': 329} | {'precision': 0.59375, 'recall': 0.6785714285714286, 'f1': 0.6333333333333334, 'number': 28} | {'precision': 0.8076923076923077, 'recall': 0.9130434782608695, 'f1': 0.8571428571428572, 'number': 23} | {'precision': 0.9935064935064936, 'recall': 1.0, 'f1': 0.9967426710097721, 'number': 459} | {'precision': 0.5576923076923077, 'recall': 0.90625, 'f1': 0.6904761904761905, 'number': 32} | {'precision': 0.884, 'recall': 0.9525862068965517, 'f1': 0.91701244813278, 'number': 232} | {'precision': 0.9853896103896104, 'recall': 0.9707562257253827, 'f1': 0.9780181839106916, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 1.0, 'recall': 0.125, 'f1': 0.2222222222222222, 'number': 8} | {'precision': 0.7431693989071039, 'recall': 0.8662420382165605, 'f1': 0.8, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9553 | 0.9602 | 0.9577 | 0.9503 |
| 0.0223 | 18.0 | 972 | 0.2300 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42} | {'precision': 0.8, 'recall': 0.9411764705882353, 'f1': 0.8648648648648648, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8676470588235294, 'recall': 0.921875, 'f1': 0.893939393939394, 'number': 64} | {'precision': 0.9545454545454546, 'recall': 0.9545454545454546, 'f1': 0.9545454545454546, 'number': 22} | {'precision': 0.5, 'recall': 0.8333333333333334, 'f1': 0.625, 'number': 6} | {'precision': 0.9935897435897436, 'recall': 0.9422492401215805, 'f1': 0.9672386895475819, 'number': 329} | {'precision': 0.59375, 'recall': 0.6785714285714286, 'f1': 0.6333333333333334, 'number': 28} | {'precision': 0.7777777777777778, 'recall': 0.9130434782608695, 'f1': 0.84, 'number': 23} | {'precision': 0.9935064935064936, 'recall': 1.0, 'f1': 0.9967426710097721, 'number': 459} | {'precision': 0.5370370370370371, 'recall': 0.90625, 'f1': 0.6744186046511628, 'number': 32} | {'precision': 0.888, 'recall': 0.9568965517241379, 'f1': 0.9211618257261411, 'number': 232} | {'precision': 0.9892948568768909, 'recall': 0.9712131596984236, 'f1': 0.9801706248558912, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 1.0, 'recall': 0.125, 'f1': 0.2222222222222222, 'number': 8} | {'precision': 0.7493540051679587, 'recall': 0.9235668789808917, 'f1': 0.8273894436519258, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9582 | 0.9633 | 0.9608 | 0.9536 |
| 0.0205 | 19.0 | 1026 | 0.2266 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42} | {'precision': 0.8, 'recall': 0.9411764705882353, 'f1': 0.8648648648648648, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8676470588235294, 'recall': 0.921875, 'f1': 0.893939393939394, 'number': 64} | {'precision': 0.8695652173913043, 'recall': 0.9090909090909091, 'f1': 0.888888888888889, 'number': 22} | {'precision': 0.45454545454545453, 'recall': 0.8333333333333334, 'f1': 0.5882352941176471, 'number': 6} | {'precision': 0.9810725552050473, 'recall': 0.9452887537993921, 'f1': 0.9628482972136223, 'number': 329} | {'precision': 0.6176470588235294, 'recall': 0.75, 'f1': 0.6774193548387097, 'number': 28} | {'precision': 0.8, 'recall': 0.8695652173913043, 'f1': 0.8333333333333333, 'number': 23} | {'precision': 0.9934924078091106, 'recall': 0.9978213507625272, 'f1': 0.9956521739130435, 'number': 459} | {'precision': 0.5555555555555556, 'recall': 0.9375, 'f1': 0.6976744186046512, 'number': 32} | {'precision': 0.8875502008032129, 'recall': 0.9525862068965517, 'f1': 0.918918918918919, 'number': 232} | {'precision': 0.9883828996282528, 'recall': 0.971898560657985, 'f1': 0.9800714203432784, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 1.0, 'recall': 0.125, 'f1': 0.2222222222222222, 'number': 8} | {'precision': 0.7428571428571429, 'recall': 0.910828025477707, 'f1': 0.8183118741058656, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9565 | 0.9632 | 0.9598 | 0.9531 |
| 0.0188 | 20.0 | 1080 | 0.2306 | {'precision': 1.0, 'recall': 0.8076923076923077, 'f1': 0.8936170212765957, 'number': 26} | {'precision': 0.8863636363636364, 'recall': 0.9285714285714286, 'f1': 0.9069767441860465, 'number': 42} | {'precision': 0.8, 'recall': 0.9411764705882353, 'f1': 0.8648648648648648, 'number': 51} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8676470588235294, 'recall': 0.921875, 'f1': 0.893939393939394, 'number': 64} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 22} | {'precision': 0.4166666666666667, 'recall': 0.8333333333333334, 'f1': 0.5555555555555556, 'number': 6} | {'precision': 0.9810725552050473, 'recall': 0.9452887537993921, 'f1': 0.9628482972136223, 'number': 329} | {'precision': 0.59375, 'recall': 0.6785714285714286, 'f1': 0.6333333333333334, 'number': 28} | {'precision': 0.8260869565217391, 'recall': 0.8260869565217391, 'f1': 0.8260869565217391, 'number': 23} | {'precision': 0.9935064935064936, 'recall': 1.0, 'f1': 0.9967426710097721, 'number': 459} | {'precision': 0.5555555555555556, 'recall': 0.9375, 'f1': 0.6976744186046512, 'number': 32} | {'precision': 0.884, 'recall': 0.9525862068965517, 'f1': 0.91701244813278, 'number': 232} | {'precision': 0.9895202608290639, 'recall': 0.9707562257253827, 'f1': 0.9800484373198017, 'number': 4377} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 1.0, 'recall': 0.125, 'f1': 0.2222222222222222, 'number': 8} | {'precision': 0.7441860465116279, 'recall': 0.9171974522292994, 'f1': 0.8216833095577747, 'number': 314} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | 0.9573 | 0.9623 | 0.9598 | 0.9532 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Alberto15Romero/GptNeo | []
| null | {
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}
} | 0 | 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: jz01/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Aleksandar/electra-srb-ner | [
"pytorch",
"safetensors",
"electra",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"ElectraForTokenClassification"
],
"model_type": "electra",
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},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 15 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base_edos_b
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. -->
# roberta-base_edos_b
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3797
- Accuracy: 0.6337
- F1: 0.6259
- Precision: 0.6395
- Recall: 0.6155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9275 | 1.0 | 638 | 0.8373 | 0.6502 | 0.6579 | 0.6348 | 0.6989 |
| 0.5744 | 2.0 | 1276 | 1.1104 | 0.6337 | 0.6187 | 0.6332 | 0.6225 |
| 0.334 | 3.0 | 1914 | 1.3797 | 0.6337 | 0.6259 | 0.6395 | 0.6155 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Aleksandar1932/gpt2-hip-hop | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"text-generation": {
"do_sample": true,
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},
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}
} | 8 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 29.00 +/- 31.12
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
|
Alerosae/SocratesGPT-2 | [
"pytorch",
"gpt2",
"feature-extraction",
"en",
"transformers",
"text-generation"
]
| text-generation | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
}
} | 7 | null | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain π€"
datasets:
- paulmbw/autotrain-data-productkit-customer-insights
co2_eq_emissions:
emissions: 0.3252141705527131
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2851583532
- CO2 Emissions (in grams): 0.3252
## Validation Metrics
- Loss: 1.044
- Rouge1: 43.528
- Rouge2: 24.232
- RougeL: 34.718
- RougeLsum: 40.574
- Gen Len: 60.206
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/paulmbw/autotrain-productkit-customer-insights-2851583532
``` |
Alessandro/model_name | []
| null | {
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"prefix": null
}
}
} | 0 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter-v5
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.30 +/- 28.60
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
|
AlexDemon/Alex | []
| null | {
"architectures": null,
"model_type": null,
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},
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},
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}
}
} | 0 | 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: NathanaelM/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
AlexN/xls-r-300m-pt | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"robust-speech-event",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
} | 15 | 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: 245.59 +/- 16.87
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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