modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-30 00:39:08
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
facebook/regnet-x-004
|
facebook
| 2022-06-30T10:14:47Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-15T19:34:54Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
facebook/regnet-x-064
|
facebook
| 2022-06-30T10:14:43Z | 69 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-15T19:38:56Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
facebook/regnet-x-080
|
facebook
| 2022-06-30T10:14:32Z | 67 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-18T15:25:24Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
facebook/regnet-x-032
|
facebook
| 2022-06-30T10:14:28Z | 68 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-15T19:37:18Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
ubikpt/t5-small-finetuned-cnn
|
ubikpt
| 2022-06-30T10:07:16Z | 85 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-29T07:19:18Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnn
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 33.2082
---
<!-- 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-cnn
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8436
- Rouge1: 33.2082
- Rouge2: 16.798
- Rougel: 28.9573
- Rougelsum: 31.1044
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.3793 | 1.0 | 359 | 1.8885 | 33.0321 | 16.7798 | 28.9367 | 30.9509 |
| 2.1432 | 2.0 | 718 | 1.8481 | 33.1559 | 16.8557 | 29.015 | 31.1122 |
| 2.0571 | 3.0 | 1077 | 1.8391 | 32.99 | 16.716 | 28.8118 | 30.9178 |
| 2.0001 | 4.0 | 1436 | 1.8357 | 33.0543 | 16.6731 | 28.8375 | 30.9604 |
| 1.9609 | 5.0 | 1795 | 1.8437 | 33.1019 | 16.7576 | 28.8669 | 31.001 |
| 1.925 | 6.0 | 2154 | 1.8402 | 33.1388 | 16.7539 | 28.8887 | 31.0262 |
| 1.9036 | 7.0 | 2513 | 1.8423 | 33.1825 | 16.759 | 28.9154 | 31.0656 |
| 1.8821 | 8.0 | 2872 | 1.8436 | 33.2082 | 16.798 | 28.9573 | 31.1044 |
### Framework versions
- Transformers 4.14.0
- Pytorch 1.5.0
- Datasets 2.3.2
- Tokenizers 0.10.3
|
ms12345/distilbert-base-cased-distilled-squad-finetuned-squad
|
ms12345
| 2022-06-30T09:52:08Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-29T07:40:29Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ms12345/distilbert-base-cased-distilled-squad-finetuned-squad
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. -->
# ms12345/distilbert-base-cased-distilled-squad-finetuned-squad
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.7381
- Validation Loss: 1.3996
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 46, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.7381 | 1.3996 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Mytios919/Mytios
|
Mytios919
| 2022-06-30T08:40:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-30T08:31:02Z |
git lfs install
git clone https://huggingface.co/Mytios919/Mytios
|
fxmarty/donotdelete3
|
fxmarty
| 2022-06-30T08:15:26Z | 0 | 0 | null |
[
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"region:us"
] |
text-classification
| 2022-06-30T08:15:10Z |
---
pipeline_tag: text-classification
datasets:
- glue
metrics:
- accuracy
tags:
- roberta
---
**task**: `text-classification`
Fixed parameters:
* **model_name_or_path**: `Bhumika/roberta-base-finetuned-sst2`
* **dataset**:
* **path**: `glue`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'sentence'}`
* **ref_keys**: `['label']`
* **name**: `sst2`
* **quantization_approach**: `dynamic`
* **node_exclusion**: `[]`
* **per_channel**: `False`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `15`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **operators_to_quantize**: `['Add', 'MatMul']`, `['Add']`
## Evaluation
Below, time metrics for
* Batch size: 8
* Input length: 128
| operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) | | accuracy (original) | accuracy (optimized) |
| :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: | :-: | :-----------------: | :------------------: |
| `['Add', 'MatMul']` | \| | 619.76 | 161.66 | \| | 1.80 | 6.20 | \| | 1.000 | 1.000 |
| `['Add']` | \| | 611.74 | 478.48 | \| | 1.80 | 2.20 | \| | 1.000 | 1.000 |
|
ThomasSimonini/Reinforce-Pixelcopter-PLE-v0
|
ThomasSimonini
| 2022-06-30T08:13:08Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-30T08:13:02Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- metrics:
- type: mean_reward
value: 13.00 +/- 16.24
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Corianas/ppo_lstm-LunarLander-v2.loadbest_
|
Corianas
| 2022-06-30T07:21:26Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-30T07:21:01Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: RecurrentPPO
results:
- metrics:
- type: mean_reward
value: 289.02 +/- 17.90
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **RecurrentPPO** Agent playing **LunarLander-v2**
This is a trained model of a **RecurrentPPO** agent playing **LunarLander-v2**
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
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo ppo_lstm --env LunarLander-v2 -orga Corianas -f logs/
python enjoy.py --algo ppo_lstm --env LunarLander-v2 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo_lstm --env LunarLander-v2 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo_lstm --env LunarLander-v2 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('ent_coef', 0.01),
('gae_lambda', 0.98),
('gamma', 0.999),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 512),
('n_timesteps', 5000000.0),
('normalize', True),
('policy', 'MlpLstmPolicy'),
('policy_kwargs',
'dict( ortho_init=False, activation_fn=nn.ReLU, '
'lstm_hidden_size=64, enable_critic_lstm=True, '
'net_arch=[dict(pi=[64], vf=[64])] )'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
Akihiro2/bert-finetuned-squad
|
Akihiro2
| 2022-06-30T07:20:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-30T04:50:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Shivagowri/vit-snacks
|
Shivagowri
| 2022-06-30T06:56:00Z | 56 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:snacks",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-29T16:05:52Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- snacks
metrics:
- accuracy
model-index:
- name: vit-snacks
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Matthijs/snacks
type: snacks
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9392670157068063
---
<!-- 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. -->
# vit-snacks
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Matthijs/snacks dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2754
- Accuracy: 0.9393
## Model description
upload any image of your fave yummy snack
## Intended uses & limitations
there are only 20 different varieties of snacks
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8724 | 0.33 | 100 | 0.9118 | 0.8670 |
| 0.5628 | 0.66 | 200 | 0.6873 | 0.8471 |
| 0.4421 | 0.99 | 300 | 0.4995 | 0.8691 |
| 0.2837 | 1.32 | 400 | 0.4008 | 0.9026 |
| 0.1645 | 1.65 | 500 | 0.3702 | 0.9058 |
| 0.1604 | 1.98 | 600 | 0.3981 | 0.8921 |
| 0.0498 | 2.31 | 700 | 0.3185 | 0.9204 |
| 0.0406 | 2.64 | 800 | 0.3427 | 0.9141 |
| 0.1049 | 2.97 | 900 | 0.3444 | 0.9173 |
| 0.0272 | 3.3 | 1000 | 0.3168 | 0.9246 |
| 0.0186 | 3.63 | 1100 | 0.3142 | 0.9288 |
| 0.0203 | 3.96 | 1200 | 0.2931 | 0.9298 |
| 0.007 | 4.29 | 1300 | 0.2754 | 0.9393 |
| 0.0072 | 4.62 | 1400 | 0.2778 | 0.9403 |
| 0.0073 | 4.95 | 1500 | 0.2782 | 0.9393 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
xenergy/indobert-finetuned-ner
|
xenergy
| 2022-06-30T06:48:16Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-30T06:37:30Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: xenery/indobert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# xenery/indobert-finetuned-ner
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2219
- Validation Loss: 0.2306
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 315, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.6526 | 0.3607 | 0 |
| 0.2980 | 0.2497 | 1 |
| 0.2219 | 0.2306 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed
|
domenicrosati
| 2022-06-30T06:46:17Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-29T21:03:23Z |
---
license: mit
tags:
- fill-mask
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large-dapt-scientific-papers-pubmed
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. -->
# deberta-v3-large-dapt-scientific-papers-pubmed
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4729
- Accuracy: 0.3510
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 21600
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 12.0315 | 0.02 | 500 | 11.6840 | 0.0 |
| 11.0675 | 0.05 | 1000 | 8.9471 | 0.0226 |
| 8.6646 | 0.07 | 1500 | 8.0093 | 0.0344 |
| 8.3625 | 0.09 | 2000 | 7.9624 | 0.0274 |
| 8.2467 | 0.12 | 2500 | 7.6599 | 0.0376 |
| 7.9714 | 0.14 | 3000 | 7.6716 | 0.0316 |
| 7.9852 | 0.16 | 3500 | 7.4535 | 0.0385 |
| 7.7502 | 0.19 | 4000 | 7.4293 | 0.0429 |
| 7.7016 | 0.21 | 4500 | 7.3576 | 0.0397 |
| 7.5789 | 0.23 | 5000 | 7.3124 | 0.0513 |
| 7.4141 | 0.25 | 5500 | 7.1353 | 0.0634 |
| 7.2365 | 0.28 | 6000 | 6.8600 | 0.0959 |
| 7.0725 | 0.3 | 6500 | 6.5743 | 0.1150 |
| 6.934 | 0.32 | 7000 | 6.3674 | 0.1415 |
| 6.7219 | 0.35 | 7500 | 6.3467 | 0.1581 |
| 6.5039 | 0.37 | 8000 | 6.1312 | 0.1815 |
| 6.3096 | 0.39 | 8500 | 5.9080 | 0.2134 |
| 6.1835 | 0.42 | 9000 | 5.8414 | 0.2137 |
| 6.0939 | 0.44 | 9500 | 5.5137 | 0.2553 |
| 6.0457 | 0.46 | 10000 | 5.5881 | 0.2545 |
| 5.8851 | 0.49 | 10500 | 5.5134 | 0.2497 |
| 5.7277 | 0.51 | 11000 | 5.3023 | 0.2699 |
| 5.6183 | 0.53 | 11500 | 5.0074 | 0.3019 |
| 5.4978 | 0.56 | 12000 | 5.1822 | 0.2814 |
| 5.5916 | 0.58 | 12500 | 5.1211 | 0.2808 |
| 5.4749 | 0.6 | 13000 | 4.9126 | 0.2972 |
| 5.3765 | 0.62 | 13500 | 5.0468 | 0.2899 |
| 5.3529 | 0.65 | 14000 | 4.8160 | 0.3037 |
| 5.2993 | 0.67 | 14500 | 4.8598 | 0.3141 |
| 5.2929 | 0.69 | 15000 | 4.9669 | 0.3052 |
| 5.2649 | 0.72 | 15500 | 4.7849 | 0.3270 |
| 5.162 | 0.74 | 16000 | 4.6819 | 0.3357 |
| 5.1639 | 0.76 | 16500 | 4.6056 | 0.3275 |
| 5.1245 | 0.79 | 17000 | 4.5473 | 0.3311 |
| 5.1596 | 0.81 | 17500 | 4.7008 | 0.3212 |
| 5.1346 | 0.83 | 18000 | 4.7932 | 0.3192 |
| 5.1174 | 0.86 | 18500 | 4.7624 | 0.3208 |
| 5.1152 | 0.88 | 19000 | 4.6388 | 0.3274 |
| 5.0852 | 0.9 | 19500 | 4.5247 | 0.3305 |
| 5.0564 | 0.93 | 20000 | 4.6982 | 0.3161 |
| 5.0179 | 0.95 | 20500 | 4.5363 | 0.3389 |
| 5.07 | 0.97 | 21000 | 4.6647 | 0.3307 |
| 5.0781 | 1.0 | 21500 | 4.4729 | 0.3510 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
RuiqianLi/Malaya-speech_fine-tune_realcase_30_Jun_lm
|
RuiqianLi
| 2022-06-30T05:43:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-30T04:06:51Z |
---
tags:
- generated_from_trainer
datasets:
- uob_singlish
model-index:
- name: Malaya-speech_fine-tune_realcase_30_Jun_lm
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. -->
# Malaya-speech_fine-tune_realcase_30_Jun_lm
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https://huggingface.co/malay-huggingface/wav2vec2-xls-r-300m-mixed) on the uob_singlish dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7669
- Wer: 0.3194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.2487 | 1.82 | 20 | 0.7188 | 0.3403 |
| 0.6386 | 3.64 | 40 | 0.7061 | 0.3264 |
| 0.3525 | 5.45 | 60 | 0.7403 | 0.3542 |
| 0.3088 | 7.27 | 80 | 0.7483 | 0.2986 |
| 0.2609 | 9.09 | 100 | 0.7669 | 0.3194 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
speechcolab/icefall-asr-gigaspeech-conformer-ctc
|
speechcolab
| 2022-06-30T03:41:47Z | 0 | 0 |
k2
|
[
"k2",
"icefall",
"audio",
"automatic-speech-recognition",
"en",
"dataset:gigaspeech",
"region:us"
] |
automatic-speech-recognition
| 2022-06-30T03:34:14Z |
---
tags:
- k2
- icefall
- audio
- automatic-speech-recognition
language: en
datasets:
- gigaspeech
---
|
Corianas/qrdqn-3frame-BreakoutNoFrameskip-v4_scoretest
|
Corianas
| 2022-06-30T02:56:39Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-30T02:56:08Z |
---
library_name: stable-baselines3
tags:
- BreakoutNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- metrics:
- type: mean_reward
value: 49.10 +/- 66.60
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BreakoutNoFrameskip-v4
type: BreakoutNoFrameskip-v4
---
# **QRDQN** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **BreakoutNoFrameskip-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
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo qrdqn --env BreakoutNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo qrdqn --env BreakoutNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env BreakoutNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo qrdqn --env BreakoutNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 3),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('normalize', False)])
```
|
gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-2
|
gary109
| 2022-06-30T02:25:27Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-29T06:40:38Z |
---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-2
This model is a fine-tuned version of [gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-1](https://huggingface.co/gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-1) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2691
- Wer: 0.0910
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2664 | 1.0 | 8969 | 0.3347 | 0.1645 |
| 0.2032 | 2.0 | 17938 | 0.3170 | 0.1662 |
| 0.1888 | 3.0 | 26907 | 0.3188 | 0.1317 |
| 0.1774 | 4.0 | 35876 | 0.2885 | 0.1195 |
| 0.0696 | 5.0 | 44845 | 0.2703 | 0.1105 |
| 0.254 | 6.0 | 53814 | 0.2817 | 0.0972 |
| 0.0464 | 7.0 | 62783 | 0.2691 | 0.0910 |
| 0.0426 | 8.0 | 71752 | 0.3033 | 0.0875 |
| 0.035 | 9.0 | 80721 | 0.3150 | 0.0841 |
| 0.0274 | 10.0 | 89690 | 0.3073 | 0.0816 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
skpawar1305/wav2vec2-large-xlsr-53-german-finetuned-ks-de
|
skpawar1305
| 2022-06-30T02:18:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-06-30T01:21:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-large-xlsr-53-german-finetuned-ks-de
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. -->
# wav2vec2-large-xlsr-53-german-finetuned-ks-de
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8681
- Accuracy: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 1.9490 | 0.0833 |
| No log | 2.0 | 2 | 1.9128 | 0.25 |
| No log | 3.0 | 3 | 1.8861 | 0.5833 |
| No log | 4.0 | 4 | 1.8681 | 0.6667 |
| No log | 5.0 | 5 | 1.8590 | 0.6667 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
RuiqianLi/Malaya-speech_fine-tune_realcase_27_Jun
|
RuiqianLi
| 2022-06-30T02:09:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-27T05:21:19Z |
---
tags:
- generated_from_trainer
datasets:
- uob_singlish
model-index:
- name: Malaya-speech_fine-tune_realcase_27_Jun
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. -->
# Malaya-speech_fine-tune_realcase_27_Jun
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https://huggingface.co/malay-huggingface/wav2vec2-xls-r-300m-mixed) on the uob_singlish dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9159
- Wer: 0.3819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3176 | 1.82 | 20 | 0.8928 | 0.3542 |
| 0.6716 | 3.64 | 40 | 0.9123 | 0.3681 |
| 0.3484 | 5.45 | 60 | 0.9509 | 0.3681 |
| 0.3064 | 7.27 | 80 | 0.9227 | 0.3958 |
| 0.3017 | 9.09 | 100 | 0.9159 | 0.3819 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
karthiksv/vit-base-patch16-224-cifar10
|
karthiksv
| 2022-06-30T02:05:56Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-05-13T13:41:59Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- cifar10
model-index:
- name: vit-base-patch16-224-cifar10
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: cifar10
type: cifar10
config: plain_text
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.1004
verified: true
- name: Precision Macro
type: precision
value: 0.07725693204097324
verified: true
- name: Precision Micro
type: precision
value: 0.1004
verified: true
- name: Precision Weighted
type: precision
value: 0.07725693204097323
verified: true
- name: Recall Macro
type: recall
value: 0.1004
verified: true
- name: Recall Micro
type: recall
value: 0.1004
verified: true
- name: Recall Weighted
type: recall
value: 0.1004
verified: true
- name: F1 Macro
type: f1
value: 0.07942008420616108
verified: true
- name: F1 Micro
type: f1
value: 0.1004
verified: true
- name: F1 Weighted
type: f1
value: 0.07942008420616108
verified: true
- name: loss
type: loss
value: 2.3154706954956055
verified: true
---
<!-- 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. -->
# vit-base-patch16-224-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jdang/distilbert-base-uncased-finetuned-imdb
|
jdang
| 2022-06-30T01:56:51Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-30T01:49:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
thelou1s/yamnet
|
thelou1s
| 2022-06-30T01:48:03Z | 0 | 2 | null |
[
"tflite",
"audio-classification",
"multilingual",
"dataset:AudioSet",
"license:apache-2.0",
"region:us"
] |
audio-classification
| 2022-06-27T01:39:58Z |
---
language: multilingual
thumbnail:
tags:
- audio-classification
license: "apache-2.0"
datasets:
- AudioSet
---
copy of https://tfhub.dev/google/yamnet/1, https://tfhub.dev/google/coral-model/yamnet/classification/coral/1
|
Corianas/qrdqn-3frame-SpaceInvadersNoFrameskip-v4_3
|
Corianas
| 2022-06-30T01:47:36Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-30T01:34:03Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- metrics:
- type: mean_reward
value: 3941.50 +/- 3501.31
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
[Here is a video of the Agent playing for longer than the included video](https://rumble.com/v1aiaj7-qrdqn-agent-playing-spaceinvaders-final.html)
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
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 3),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('normalize', False)])
```
|
ThomasSimonini/Reinforce-Pix
|
ThomasSimonini
| 2022-06-30T00:11:51Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T23:39:06Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pix
results:
- metrics:
- type: mean_reward
value: 8.00 +/- 4.88
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
lo
# **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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
tbasic5/distilbert-base-uncased-finetuned-emotion
|
tbasic5
| 2022-06-29T22:21:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-29T22:07:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.925022224520608
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2222
- Accuracy: 0.925
- F1: 0.9250
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8521 | 1.0 | 250 | 0.3164 | 0.907 | 0.9038 |
| 0.2549 | 2.0 | 500 | 0.2222 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
austinmw/distilbert-base-uncased-finetuned-tweets-sentiment
|
austinmw
| 2022-06-29T22:18:47Z | 3,297 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-29T21:23:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-tweets-sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: Accuracy
type: accuracy
value: 0.7295
- name: F1
type: f1
value: 0.7303196028048928
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-tweets-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8192
- Accuracy: 0.7295
- F1: 0.7303
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7126 | 1.0 | 713 | 0.6578 | 0.7185 | 0.7181 |
| 0.5514 | 2.0 | 1426 | 0.6249 | 0.7005 | 0.7046 |
| 0.4406 | 3.0 | 2139 | 0.7053 | 0.731 | 0.7296 |
| 0.3511 | 4.0 | 2852 | 0.7580 | 0.718 | 0.7180 |
| 0.2809 | 5.0 | 3565 | 0.8192 | 0.7295 | 0.7303 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jdang/bert-finetuned-ner
|
jdang
| 2022-06-29T22:07:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-29T21:47:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9357509521443947
- name: Recall
type: recall
value: 0.9510265903736116
- name: F1
type: f1
value: 0.9433269343126617
- name: Accuracy
type: accuracy
value: 0.9864160828869135
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0629
- Precision: 0.9358
- Recall: 0.9510
- F1: 0.9433
- Accuracy: 0.9864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0855 | 1.0 | 1756 | 0.0632 | 0.9152 | 0.9387 | 0.9268 | 0.9833 |
| 0.0387 | 2.0 | 3512 | 0.0589 | 0.9322 | 0.9505 | 0.9413 | 0.9859 |
| 0.0193 | 3.0 | 5268 | 0.0629 | 0.9358 | 0.9510 | 0.9433 | 0.9864 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net
|
SerdarHelli
| 2022-06-29T21:45:11Z | 34 | 9 |
tf-keras
|
[
"tf-keras",
"segmentation",
"dentalimaging",
"medicalimaging",
"image-segmentation",
"dataset:SerdarHelli/SegmentationOfTeethPanoramicXRayImages",
"region:us"
] |
image-segmentation
| 2022-03-07T20:50:27Z |
---
tags:
- segmentation
- dentalimaging
- medicalimaging
- image-segmentation
metrics:
- f1
- accuracy
datasets:
- SerdarHelli/SegmentationOfTeethPanoramicXRayImages
---
# Semantic-Segmentation-of-Teeth-in-Panoramic-X-ray-Image
The aim of this study is automatic semantic segmentation and measurement total length of teeth in one-shot panoramic x-ray image by using deep learning method with U-Net Model and binary image analysis in order to provide diagnostic information for the management of dental disorders, diseases, and conditions.
[***Github Link***](https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net)
***Original Dataset***
DATASET ref - H. Abdi, S. Kasaei, and M. Mehdizadeh, “Automatic segmentation of mandible in panoramic x-ray,” J. Med. Imaging, vol. 2, no. 4, p. 44003, 2015
[Link DATASET for only original images.](https://data.mendeley.com/datasets/hxt48yk462/1)
### Paper
[The authors of this article are Selahattin Serdar Helli and Andaç Hamamcı with the Department of Biomedical Engineering, Faculty of Engineering, Yeditepe University, Istanbul, Turkey](https://dergipark.org.tr/tr/pub/dubited/issue/68307/950568)
### BibTeX Entry and Citation Info
```
@article{helli10tooth,
title={Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing},
author={HELL{\.I}, Serdar and HAMAMCI, Anda{\c{c}}},
journal={D{\"u}zce {\"U}niversitesi Bilim ve Teknoloji Dergisi},
volume={10},
number={1},
pages={39--50}
}
```
|
TheDiamondKing/Discord-Philosophy-Medium
|
TheDiamondKing
| 2022-06-29T21:26:01Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-29T21:16:21Z |
---
license: mit
---
Medium-Sized model trained with philosophical questions ( mainly from discord )
~11000 Messages
|
TheDiamondKing/Discord-Message-Small
|
TheDiamondKing
| 2022-06-29T21:06:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-29T20:10:42Z |
---
license: mit
---
Simple model trained with 2790 Discord messages
( Might have some NSFW responses )
|
BK-V/xlm-roberta-base-finetuned-peyma-fa
|
BK-V
| 2022-06-29T20:59:53Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-10T14:11:45Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-peyma-fa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-peyma-fa
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0937
- F1: 0.9249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1562 | 1.0 | 998 | 0.0691 | 0.8777 |
| 0.0638 | 2.0 | 1996 | 0.0703 | 0.8908 |
| 0.0457 | 3.0 | 2994 | 0.0645 | 0.8975 |
| 0.0281 | 4.0 | 3992 | 0.0842 | 0.8994 |
| 0.0206 | 5.0 | 4990 | 0.0651 | 0.9164 |
| 0.0139 | 6.0 | 5988 | 0.0787 | 0.9148 |
| 0.0083 | 7.0 | 6986 | 0.0838 | 0.9253 |
| 0.0052 | 8.0 | 7984 | 0.0833 | 0.9221 |
| 0.0031 | 9.0 | 8982 | 0.0947 | 0.9230 |
| 0.0028 | 10.0 | 9980 | 0.0937 | 0.9249 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ThomasSimonini/PixelCopter
|
ThomasSimonini
| 2022-06-29T20:37:55Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"Pixelcopter-PLE-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T20:37:43Z |
---
library_name: stable-baselines3
tags:
- Pixelcopter-PLE-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -2.90 +/- 0.30
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **PPO** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **PPO** agent playing **Pixelcopter-PLE-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
edbeeching/decision-transformer-gym-walker2d-medium-replay
|
edbeeching
| 2022-06-29T19:22:05Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2022-03-16T08:21:17Z |
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
---
# Decision Transformer model trained on medium-replay trajectories sampled from the Gym Walker2d environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium-replay trajectories sampled from the Gym Walker2d environment.
The following normlization coeficients are required to use this model:
mean = [1.2093647, 0.13264023, -0.14371201, -0.20465161, 0.55776125, -0.03231537, -0.2784661, 0.19130707, 1.4701707, -0.12504704, 0.05649531, -0.09991033, -0.34034026, 0.03546293, -0.08934259, -0.2992438, -0.5984178 ]
std = [0.11929835, 0.3562574, 0.258522, 0.42075422, 0.5202291, 0.15685083, 0.3677098, 0.7161388, 1.3763766, 0.8632222, 2.6364644, 3.0134118, 3.720684, 4.867284, 2.6681626, 3.845187, 5.47683867]
See our [Blog Post](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing), [Colab notebook](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing) or [Example Script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/decision_transformer) for usage.
|
edbeeching/decision-transformer-gym-halfcheetah-medium-replay
|
edbeeching
| 2022-06-29T19:21:08Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2022-03-16T08:20:08Z |
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
---
# Decision Transformer model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment.
The following normlization coeficients are required to use this model:
mean = [-0.12880704, 0.37381196, -0.14995988, -0.23479079, -0.28412786, -0.13096535, -0.20157982, -0.06517727, 3.4768248, -0.02785066, -0.01503525, 0.07697279, 0.01266712, 0.0273253, 0.02316425, 0.01043872, -0.01583941]
std = [0.17019016, 1.2844249, 0.33442774, 0.36727592, 0.26092398, 0.4784107, 0.31814206 ,0.33552638, 2.0931616, 0.80374336, 1.9044334, 6.57321, 7.5728636, 5.0697494, 9.105554, 6.0856543, 7.253004, 5]
See our [Blog Post](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing), [Colab notebook](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing) or [Example Script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/decision_transformer) for usage.
|
edbeeching/decision-transformer-gym-halfcheetah-expert
|
edbeeching
| 2022-06-29T19:20:32Z | 18 | 1 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2022-03-16T08:19:45Z |
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
---
# Decision Transformer model trained on expert trajectories sampled from the Gym HalfCheetah environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on expert trajectories sampled from the Gym HalfCheetah environment.
The following normlization coeficients are required to use this model:
mean = [ -0.04489148, 0.03232588, 0.06034835, -0.17081226, -0.19480659, -0.05751596, 0.09701628, 0.03239211, 11.047426, -0.07997331, -0.32363534, 0.36297753, 0.42322603, 0.40836546, 1.1085187, -0.4874403, -0.0737481 ]
std = [0.04002118, 0.4107858, 0.54217845, 0.41522816, 0.23796624, 0.62036866, 0.30100912, 0.21737163, 2.2105937, 0.572586, 1.7255033, 11.844218, 12.06324, 7.0495934, 13.499867, 7.195647, 5.0264325]
See our [Blog Post](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing), [Colab notebook](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing) or [Example Script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/decision_transformer) for usage.
|
edbeeching/decision-transformer-gym-hopper-medium
|
edbeeching
| 2022-06-29T19:15:16Z | 34,485 | 6 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2022-03-16T08:20:31Z |
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
---
# Decision Transformer model trained on medium trajectories sampled from the Gym Hopper environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on medium trajectories sampled from the Gym Hopper environment.
The following normlization coefficients are required to use this model:
mean = [ 1.311279, -0.08469521, -0.5382719, -0.07201576, 0.04932366, 2.1066856, -0.15017354, 0.00878345, -0.2848186, -0.18540096, -0.28461286]
std = [0.17790751, 0.05444621, 0.21297139, 0.14530419, 0.6124444, 0.85174465, 1.4515252, 0.6751696, 1.536239, 1.6160746, 5.6072536 ]
See our [Blog Post](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing), [Colab notebook](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing) or [Example Script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/decision_transformer) for usage.
|
edbeeching/decision-transformer-gym-hopper-expert
|
edbeeching
| 2022-06-29T19:12:17Z | 566 | 18 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"feature-extraction",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2022-03-16T08:20:20Z |
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
---
# Decision Transformer model trained on expert trajectories sampled from the Gym Hopper environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained on expert trajectories sampled from the Gym Hopper environment.
The following normlization coefficients are required to use this model:
mean = [ 1.3490015, -0.11208222, -0.5506444, -0.13188992, -0.00378754, 2.6071432, 0.02322114, -0.01626922, -0.06840388, -0.05183131, 0.04272673]
std = [0.15980862, 0.0446214, 0.14307782, 0.17629202, 0.5912333, 0.5899924, 1.5405099, 0.8152689, 2.0173461, 2.4107876, 5.8440027 ]
See our [Blog Post](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing), [Colab notebook](https://colab.research.google.com/drive/1K3UuajwoPY1MzRKNkONNRS3gS5DxZ-qF?usp=sharing) or [Example Script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/decision_transformer) for usage.
|
ullasmrnva/LawBerta
|
ullasmrnva
| 2022-06-29T18:56:54Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-29T18:56:39Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: attempt
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. -->
# attempt
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 4.20.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
zhav1k/q-Taxi-v3
|
zhav1k
| 2022-06-29T18:56:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T18:55:53Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.69
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
nbroad/bigbird-base-health-fact
|
nbroad
| 2022-06-29T18:29:17Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"big_bird",
"text-classification",
"generated_from_trainer",
"en",
"dataset:health_fact",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-26T17:55:02Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- health_fact
model-index:
- name: bigbird-base-health-fact
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: health_fact
type: health_fact
split: test
metrics:
- name: F1
type: f1
value: 0.6694031411935434
- name: Accuracy
type: accuracy
value: 0.7948094079480941
- name: False Accuracy
type: accuracy
value: 0.8092783505154639
- name: Mixture Accuracy
type: accuracy
value: 0.4975124378109453
- name: True Accuracy
type: accuracy
value: 0.9148580968280468
- name: Unproven Accuracy
type: accuracy
value: 0.4
---
# bigbird-base-health-fact
This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the health_fact dataset.
It achieves the following results on the VALIDATION set:
- Overall Accuracy: 0.8228995057660626
- Macro F1: 0.6979224830442152
- False Accuracy: 0.8289473684210527
- Mixture Accuracy: 0.47560975609756095
- True Accuracy: 0.9332273449920508
- Unproven Accuracy: 0.4634146341463415
It achieves the following results on the TEST set:
- Overall Accuracy: 0.7948094079480941
- Macro F1: 0.6694031411935434
- Mixture Accuracy: 0.4975124378109453
- False Accuracy: 0.8092783505154639
- True Accuracy: 0.9148580968280468
- Unproven Accuracy: 0.4
## Model description
Here is how you can use the model:
```python
import torch
from transformers import pipeline
claim = "A mother revealed to her child in a letter after her death that she had just one eye because she had donated the other to him."
text = "In April 2005, we spotted a tearjerker on the Internet about a mother who gave up one of her eyes to a son who had lost one of his at an early age. By February 2007 the item was circulating in e-mail in the following shortened version: My mom only had one eye. I hated her… She was such an embarrassment. She cooked for students and teachers to support the family. There was this one day during elementary school where my mom came to say hello to me. I was so embarrassed. How could she do this to me? I ignored her, threw her a hateful look and ran out. The next day at school one of my classmates said, “EEEE, your mom only has one eye!” I wanted to bury myself. I also wanted my mom to just disappear. I confronted her that day and said, “If you’re only gonna make me a laughing stock, why don’t you just die?” My mom did not respond… I didn’t even stop to think for a second about what I had said, because I was full of anger. I was oblivious to her feelings. I wanted out of that house, and have nothing to do with her. So I studied real hard, got a chance to go abroad to study. Then, I got married. I bought a house of my own. I had kids of my own. I was happy with my life, my kids and the comforts. Then one day, my Mother came to visit me. She hadn’t seen me in years and she didn’t even meet her grandchildren. When she stood by the door, my children laughed at her, and I yelled at her for coming over uninvited. I screamed at her, “How dare you come to my house and scare my children! GET OUT OF HERE! NOW!! !” And to this, my mother quietly answered, “Oh, I’m so sorry. I may have gotten the wrong address,” and she disappeared out of sight. One day, a letter regarding a school reunion came to my house. So I lied to my wife that I was going on a business trip. After the reunion, I went to the old shack just out of curiosity. My neighbors said that she died. I did not shed a single tear. They handed me a letter that she had wanted me to have. My dearest son, I think of you all the time. I’m sorry that I came to your house and scared your children. I was so glad when I heard you were coming for the reunion. But I may not be able to even get out of bed to see you. I’m sorry that I was a constant embarrassment to you when you were growing up. You see……..when you were very little, you got into an accident, and lost your eye. As a mother, I couldn’t stand watching you having to grow up with one eye. So I gave you mine. I was so proud of my son who was seeing a whole new world for me, in my place, with that eye. With all my love to you, Your mother. In its earlier incarnation, the story identified by implication its location as Korea through statements made by both the mother and the son (the son’s “I left my mother and came to Seoul” and the mother’s “I won’t visit Seoul anymore”). It also supplied a reason for the son’s behavior when his mother arrived unexpectedly to visit him (“My little girl ran away, scared of my mom’s eye” and “I screamed at her, ‘How dare you come to my house and scare my daughter!'”). A further twist was provided in the original: rather than gaining the news of his mother’s death from neighbors (who hand him her letter), the son instead discovered the woman who bore him lying dead on the floor of what used to be his childhood home, her missive to him clutched in her lifeless hand: Give your parents roses while they are alive, not deadMY mom only had one eye. I hated her … she was such an embarrassment. My mom ran a small shop at a flea market. She collected little weeds and such to sell … anything for the money we needed she was such an embarrassment. There was this one day during elementary school … It was field day, and my mom came. I was so embarrassed. How could she do this to me? I threw her a hateful look and ran out. The next day at school … “your mom only has one eye?!? !” … And they taunted me. I wished that my mom would just disappear from this world so I said to my mom, “mom … Why don’t you have the other eye?! If you’re only going to make me a laughingstock, why don’t you just die?!! !” my mom did not respond … I guess I felt a little bad, but at the same time, it felt good to think that I had said what I’d wanted to say all this time… maybe it was because my mom hadn’t punished me, but I didn’t think that I had hurt her feelings very badly. That night… I woke up, and went to the kitchen to get a glass of water. My mom was crying there, so quietly, as if she was afraid that she might wake me. I took a look at her, and then turned away. Because of the thing I had said to her earlier, there was something pinching at me in the corner of my heart. Even so, I hated my mother who was crying out of her one eye. So I told myself that I would grow up and become successful. Because I hated my one-eyed mom and our desperate poverty… then I studied real hard. I left my mother and came to Seoul and studied, and got accepted in the Seoul University with all the confidence I had. Then, I got married. I bought a house of my own. Then I had kids, too… now I’m living happily as a successful man. I like it here because it’s a place that doesn’t remind me of my mom. This happiness was getting bigger and bigger, when… what?! Who’s this…it was my mother… still with her one eye. It felt as if the whole sky was falling apart on me. My little girl ran away, scared of my mom’s eye. And I asked her, “who are you? !” “I don’t know you!! !” as if trying to make that real. I screamed at her, “How dare you come to my house and scare my daughter!” “GET OUT OF HERE! NOW!! !” and to this, my mother quietly answered, “oh, I’m so sorry. I may have gotten the wrong address,” and she disappeared out of sight. Thank goodness… she doesn’t recognize me… I was quite relieved. I told myself that I wasn’t going to care, or think about this for the rest of my life. Then a wave of relief came upon me… One day, a letter regarding a school reunion came to my house. So, lying to my wife that I was going on a business trip, I went. After the reunion, I went down to the old shack, that I used to call a house… just out of curiosity there, I found my mother fallen on the cold ground. But I did not shed a single tear. She had a piece of paper in her hand…. it was a letter to me. My son… I think my life has been long enough now… And… I won’t visit Seoul anymore… but would it be too much to ask if I wanted you to come visit me once in a while? I miss you so much… and I was so glad when I heard you were coming for the reunion. But I decided not to go to the school. …for you… and I’m sorry that I only have one eye, and I was an embarrassment for you. You see, when you were very little, you got into an accident, and lost your eye. as a mom, I couldn’t stand watching you having to grow up with only one eye… so I gave you mine… I was so proud of my son that was seeing a whole new world for me, in my place, with that eye. I was never upset at you for anything you did… the couple times that you were angry with me, I thought to myself, ‘it’s because he loves me…’ my son. Oh, my son… I don’t want you to cry for me, because of my death. My son, I love you my son, I love you so much. With all modern medical technology, transplantation of the eyeball is still impossible. The optic nerve isn’t an ordinary nerve, but instead an inset running from the brain. Modern medicine isn’t able to “connect” an eyeball back to brain after an optic nerve has been severed, let alone transplant the eye from a different person. (The only exception is the cornea, the transparent part in front of the eye: corneas are transplanted to replace injured and opaque ones.) We won’t try to comment on whether any surgeon would accept an eye from a living donor for transplant into another — we’ll leave that to others who are far more knowledgeable about medical ethics and transplant procedures. But we will note that the plot device of a mother’s dramatic sacrifice for the sake of her child’s being revealed in a written communication delivered after her demise appears in another legend about maternal love: the 2008 tale about a woman who left a touching message on her cell phone even as life ebbed from her as she used her body to shield the tot during an earthquake. Giving up one’s own life for a loved one is central to a 2005 urban legend about a boy on a motorcycle who has his girlfriend hug him one last time and put on his helmet just before the crash that kills him and spares her. Returning to the “notes from the dead” theme is the 1995 story about a son who discovers only through a posthumous letter from his mother what their occasional dinner “dates” had meant to her. Another legend we’re familiar with features a meme used in the one-eyed mother story (the coming to light of the enduring love of the person who died for the completely unworthy person she’d lavished it on), but that one involves a terminally ill woman and her cheating husband. In it, an about-to-be-spurned wife begs the adulterous hoon she’d married to stick around for another 30 days and to carry her over the threshold of their home once every day of that month as her way of keeping him around long enough for her to kick the bucket and thus spare their son the knowledge that his parents were on the verge of divorce."
label = "false"
device = 0 if torch.cuda.is_available() else -1
pl = pipeline("text-classification", model="nbroad/bigbird-base-health-fact", device=device)
input_text = claim+pl.tokenizer.sep_token+text
print(len(pl.tokenizer(input_text).input_ids))
# 2303 (which is why bigbird is useful)
pl(input_text)
# [{'label': 'false', 'score': 0.3866822123527527}]
```
## 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: 8
- eval_batch_size: 32
- seed: 18
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Micro F1 | Macro F1 | False F1 | Mixture F1 | True F1 | Unproven F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:----------:|:-------:|:-----------:|
| 0.5563 | 1.0 | 1226 | 0.5020 | 0.7949 | 0.6062 | 0.7926 | 0.4591 | 0.8986 | 0.2745 |
| 0.5048 | 2.0 | 2452 | 0.4969 | 0.8180 | 0.6846 | 0.8202 | 0.4342 | 0.9126 | 0.5714 |
| 0.3454 | 3.0 | 3678 | 0.5864 | 0.8130 | 0.6874 | 0.8114 | 0.4557 | 0.9154 | 0.5672 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.1.dev0
- Tokenizers 0.12.1
|
JHart96/finetuning-sentiment-model-3000-samples
|
JHart96
| 2022-06-29T18:20:13Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-29T18:10:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.86
- name: F1
type: f1
value: 0.8627450980392156
---
<!-- 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 imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3300
- Accuracy: 0.86
- F1: 0.8627
## 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 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
kakashi210/autotrain-tweet-sentiment-classifier-1055036381
|
kakashi210
| 2022-06-29T17:54:00Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"unk",
"dataset:kakashi210/autotrain-data-tweet-sentiment-classifier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-29T17:45:44Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- kakashi210/autotrain-data-tweet-sentiment-classifier
co2_eq_emissions: 17.43982800509071
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1055036381
- CO2 Emissions (in grams): 17.43982800509071
## Validation Metrics
- Loss: 0.6177256107330322
- Accuracy: 0.7306006137658921
- Macro F1: 0.719534854339415
- Micro F1: 0.730600613765892
- Weighted F1: 0.7302204676842725
- Macro Precision: 0.714938066281146
- Micro Precision: 0.7306006137658921
- Weighted Precision: 0.7316651970219867
- Macro Recall: 0.7258484087500343
- Micro Recall: 0.7306006137658921
- Weighted Recall: 0.7306006137658921
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/kakashi210/autotrain-tweet-sentiment-classifier-1055036381
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("kakashi210/autotrain-tweet-sentiment-classifier-1055036381", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("kakashi210/autotrain-tweet-sentiment-classifier-1055036381", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
k3nneth/xlm-roberta-base-finetuned-panx-de-fr
|
k3nneth
| 2022-06-29T17:16:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-29T16:55:27Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1644
- F1: 0.8617
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 |
| 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 |
| 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
BeardedJohn/bert-finetuned-ner-ubb-endava-only-misc
|
BeardedJohn
| 2022-06-29T16:59:54Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-29T11:44:27Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: BeardedJohn/bert-finetuned-ner-ubb-endava-only-misc
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. -->
# BeardedJohn/bert-finetuned-ner-ubb-endava-only-misc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0015
- Validation Loss: 0.0006
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 705, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1740 | 0.0013 | 0 |
| 0.0024 | 0.0007 | 1 |
| 0.0015 | 0.0006 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
freedomking/prompt-uie-medical-base
|
freedomking
| 2022-06-29T16:47:06Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T16:32:44Z |
## Introduction
Universal Information Extraction
More detail:
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/uie
|
elhamagk/distilbert-base-uncased-finetuned-imdb
|
elhamagk
| 2022-06-29T15:04:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-29T14:54:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Salvatore/bert-finetuned-mutation-recognition-3
|
Salvatore
| 2022-06-29T14:51:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-29T14:32:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-mutation-recognition-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-mutation-recognition-3
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0727
- Dnamutation F1: 0.6484
- Proteinmutation F1: 0.8571
- Snp F1: 1.0
- Precision: 0.7966
- Recall: 0.7625
- F1: 0.7792
- Accuracy: 0.9872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Dnamutation F1 | Proteinmutation F1 | Snp F1 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------------:|:------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 324 | 0.0323 | 0.5996 | 0.7886 | 1.0 | 0.6583 | 0.7982 | 0.7215 | 0.9901 |
| 0.0788 | 2.0 | 648 | 0.0314 | 0.6765 | 0.8783 | 1.0 | 0.7453 | 0.8571 | 0.7973 | 0.9907 |
| 0.0788 | 3.0 | 972 | 0.0306 | 0.6391 | 0.8679 | 1.0 | 0.7341 | 0.8232 | 0.7761 | 0.9903 |
| 0.0273 | 4.0 | 1296 | 0.0424 | 0.6360 | 0.8714 | 1.0 | 0.7792 | 0.775 | 0.7771 | 0.9885 |
| 0.0178 | 5.0 | 1620 | 0.0462 | 0.5885 | 0.8683 | 1.0 | 0.7576 | 0.7589 | 0.7583 | 0.9869 |
| 0.0178 | 6.0 | 1944 | 0.0531 | 0.6176 | 0.8701 | 1.0 | 0.7734 | 0.7679 | 0.7706 | 0.9873 |
| 0.0165 | 7.0 | 2268 | 0.0573 | 0.6597 | 0.8658 | 1.0 | 0.8022 | 0.775 | 0.7884 | 0.9881 |
| 0.0144 | 8.0 | 2592 | 0.0636 | 0.6596 | 0.8454 | 1.0 | 0.7919 | 0.7679 | 0.7797 | 0.9871 |
| 0.0144 | 9.0 | 2916 | 0.0710 | 0.6568 | 0.8748 | 1.0 | 0.8159 | 0.7679 | 0.7912 | 0.9872 |
| 0.0108 | 10.0 | 3240 | 0.0727 | 0.6484 | 0.8571 | 1.0 | 0.7966 | 0.7625 | 0.7792 | 0.9872 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2
- Datasets 2.0.0
- Tokenizers 0.12.1
|
jimypbr/cifar10_outputs
|
jimypbr
| 2022-06-29T14:48:46Z | 292 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"optimum_graphcore",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-29T14:30:22Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: cifar10_outputs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.991421568627451
---
<!-- 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. -->
# cifar10_outputs
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0806
- Accuracy: 0.9914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 17
- eval_batch_size: 17
- seed: 1337
- distributed_type: IPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 8704
- total_eval_batch_size: 272
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 100.0
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cpu
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
ydshieh/clip-vit-base-patch32
|
ydshieh
| 2022-06-29T14:47:32Z | 15 | 1 |
transformers
|
[
"transformers",
"tf",
"clip",
"zero-shot-image-classification",
"summarization",
"en",
"dataset:scientific_papers",
"arxiv:2007.14062",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
datasets:
- scientific_papers
tags:
- summarization
model-index:
- name: google/bigbird-pegasus-large-pubmed
results:
- task:
type: summarization
name: Summarization
dataset:
name: scientific_papers
type: scientific_papers
config: pubmed
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 40.8966
verified: true
- name: ROUGE-2
type: rouge
value: 18.1161
verified: true
- name: ROUGE-L
type: rouge
value: 26.1743
verified: true
- name: ROUGE-LSUM
type: rouge
value: 34.2773
verified: true
- name: loss
type: loss
value: 2.1707184314727783
verified: true
- name: meteor
type: meteor
value: 0.3513
verified: true
- name: gen_len
type: gen_len
value: 221.2531
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: scientific_papers
type: scientific_papers
config: arxiv
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 40.3815
verified: true
- name: ROUGE-2
type: rouge
value: 14.374
verified: true
- name: ROUGE-L
type: rouge
value: 23.4773
verified: true
- name: ROUGE-LSUM
type: rouge
value: 33.772
verified: true
- name: loss
type: loss
value: 3.235051393508911
verified: true
- name: gen_len
type: gen_len
value: 186.2003
verified: true
---
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was introduced in this [paper](https://arxiv.org/abs/2007.14062) and first released in this [repository](https://github.com/google-research/bigbird).
Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BigBird relies on **block sparse attention** instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts.
## How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-pubmed")
# by default encoder-attention is `block_sparse` with num_random_blocks=3, block_size=64
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-pubmed")
# decoder attention type can't be changed & will be "original_full"
# you can change `attention_type` (encoder only) to full attention like this:
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-pubmed", attention_type="original_full")
# you can change `block_size` & `num_random_blocks` like this:
model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-pubmed", block_size=16, num_random_blocks=2)
text = "Replace me by any text you'd like."
inputs = tokenizer(text, return_tensors='pt')
prediction = model.generate(**inputs)
prediction = tokenizer.batch_decode(prediction)
```
## Training Procedure
This checkpoint is obtained after fine-tuning `BigBirdPegasusForConditionalGeneration` for **summarization** on **pubmed dataset** from [scientific_papers](https://huggingface.co/datasets/scientific_papers).
## BibTeX entry and citation info
```tex
@misc{zaheer2021big,
title={Big Bird: Transformers for Longer Sequences},
author={Manzil Zaheer and Guru Guruganesh and Avinava Dubey and Joshua Ainslie and Chris Alberti and Santiago Ontanon and Philip Pham and Anirudh Ravula and Qifan Wang and Li Yang and Amr Ahmed},
year={2021},
eprint={2007.14062},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
FabianWillner/bert-base-uncased-finetuned-squad
|
FabianWillner
| 2022-06-29T14:46:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-29T09:16:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-finetuned-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. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0106
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0626 | 1.0 | 5533 | 1.0308 |
| 0.8157 | 2.0 | 11066 | 1.0106 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Salvatore/bert-finetuned-mutation-recognition-2
|
Salvatore
| 2022-06-29T14:29:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-29T10:10:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-mutation-recognition-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-mutation-recognition-2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0818
- Dnamutation F1: 0.6371
- Snp F1: 0.0952
- Proteinmutation F1: 0.8412
- Precision: 0.7646
- Recall: 0.6596
- F1: 0.7082
- Accuracy: 0.9877
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Dnamutation F1 | Snp F1 | Proteinmutation F1 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:------:|:------------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 403 | 0.0383 | 0.5871 | 0.0 | 0.7573 | 0.6195 | 0.6770 | 0.6470 | 0.9872 |
| 0.0863 | 2.0 | 806 | 0.0349 | 0.6202 | 0.0 | 0.8646 | 0.6815 | 0.7408 | 0.7099 | 0.9889 |
| 0.0295 | 3.0 | 1209 | 0.0415 | 0.5670 | 0.0 | 0.7689 | 0.6887 | 0.6035 | 0.6433 | 0.9866 |
| 0.019 | 4.0 | 1612 | 0.0430 | 0.5909 | 0.4742 | 0.7840 | 0.6667 | 0.6615 | 0.6641 | 0.9881 |
| 0.0127 | 5.0 | 2015 | 0.0507 | 0.6345 | 0.0 | 0.8455 | 0.7290 | 0.6867 | 0.7072 | 0.9885 |
| 0.0127 | 6.0 | 2418 | 0.0678 | 0.5946 | 0.05 | 0.8087 | 0.7471 | 0.6170 | 0.6758 | 0.9868 |
| 0.0067 | 7.0 | 2821 | 0.0544 | 0.6693 | 0.2727 | 0.8475 | 0.7208 | 0.7292 | 0.725 | 0.9884 |
| 0.0042 | 8.0 | 3224 | 0.0642 | 0.6694 | 0.2000 | 0.8401 | 0.7390 | 0.7118 | 0.7251 | 0.9885 |
| 0.0019 | 9.0 | 3627 | 0.0847 | 0.6271 | 0.0976 | 0.8416 | 0.7671 | 0.6499 | 0.7037 | 0.9877 |
| 0.0014 | 10.0 | 4030 | 0.0818 | 0.6371 | 0.0952 | 0.8412 | 0.7646 | 0.6596 | 0.7082 | 0.9877 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2
- Datasets 2.0.0
- Tokenizers 0.12.1
|
igpaub/q-Taxi-v3
|
igpaub
| 2022-06-29T14:18:47Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T14:07:59Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="igpaub/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
trtd56/q-Taxi-v3
|
trtd56
| 2022-06-29T13:22:25Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T13:22:18Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="trtd56/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
robingeibel/bigbird-base-finetuned-big_patent
|
robingeibel
| 2022-06-29T12:35:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"fill-mask",
"generated_from_trainer",
"dataset:big_patent",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-27T07:03:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- big_patent
model-index:
- name: bigbird-base-finetuned-big_patent
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. -->
# bigbird-base-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/bigbird-base-finetuned-big_patent](https://huggingface.co/robingeibel/bigbird-base-finetuned-big_patent) on the big_patent dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0686
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.1432 | 1.0 | 154482 | 1.0686 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
igpaub/q-FrozenLake-v1-4x4-noSlippery
|
igpaub
| 2022-06-29T12:17:50Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T12:17:41Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="igpaub/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
dunlp/GWW
|
dunlp
| 2022-06-29T09:36:26Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-30T12:06:11Z |
---
tags:
- generated_from_trainer
model-index:
- name: GWW
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. -->
# GWW
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on Dutch civiel works dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7097
## 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: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7179 | 1.0 | 78 | 3.1185 |
| 3.1134 | 2.0 | 156 | 2.8528 |
| 2.9327 | 3.0 | 234 | 2.7249 |
| 2.8377 | 4.0 | 312 | 2.7255 |
| 2.7888 | 5.0 | 390 | 2.6737 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
gguichard/q-FrozenLake-v1-4x4-noSlippery
|
gguichard
| 2022-06-29T09:16:16Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T09:13:59Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
squirro/distilroberta-base-squad_v2
|
squirro
| 2022-06-29T08:53:58Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"onnx",
"roberta",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-07T10:00:04Z |
---
license: apache-2.0
language: en
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilroberta-base-squad_v2
results:
- task:
name: Question Answering
type: question-answering
dataset:
type: squad_v2 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: The Stanford Question Answering Dataset
args: en
metrics:
- type: eval_exact
value: 65.2405
- type: eval_f1
value: 68.6265
- type: eval_HasAns_exact
value: 67.5776
- type: eval_HasAns_f1
value: 74.3594
- type: eval_NoAns_exact
value: 62.91
- type: eval_NoAns_f1
value: 62.91
---
# distilroberta-base-squad_v2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the squad_v2 dataset.
## Model description
This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/).
For convenience this model is prepared to be used with the frameworks `PyTorch`, `Tensorflow` and `ONNX`.
## Intended uses & limitations
This model can handle mismatched question-context pairs. Make sure to specify `handle_impossible_answer=True` when using `QuestionAnsweringPipeline`.
__Example usage:__
```python
>>> from transformers import AutoModelForQuestionAnswering, AutoTokenizer, QuestionAnsweringPipeline
>>> model = AutoModelForQuestionAnswering.from_pretrained("squirro/distilroberta-base-squad_v2")
>>> tokenizer = AutoTokenizer.from_pretrained("squirro/distilroberta-base-squad_v2")
>>> qa_model = QuestionAnsweringPipeline(model, tokenizer)
>>> qa_model(
>>> question="What's your name?",
>>> context="My name is Clara and I live in Berkeley.",
>>> handle_impossible_answer=True # important!
>>> )
{'score': 0.9498472809791565, 'start': 11, 'end': 16, 'answer': 'Clara'}
```
## Training and evaluation data
Training and evaluation was done on [SQuAD2.0](https://huggingface.co/datasets/squad_v2).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Metric | Value |
|:-------------------------|-------------:|
| epoch | 3 |
| eval_HasAns_exact | 67.5776 |
| eval_HasAns_f1 | 74.3594 |
| eval_HasAns_total | 5928 |
| eval_NoAns_exact | 62.91 |
| eval_NoAns_f1 | 62.91 |
| eval_NoAns_total | 5945 |
| eval_best_exact | 65.2489 |
| eval_best_exact_thresh | 0 |
| eval_best_f1 | 68.6349 |
| eval_best_f1_thresh | 0 |
| eval_exact | 65.2405 |
| eval_f1 | 68.6265 |
| eval_samples | 12165 |
| eval_total | 11873 |
| train_loss | 1.40336 |
| train_runtime | 1365.28 |
| train_samples | 131823 |
| train_samples_per_second | 289.662 |
| train_steps_per_second | 0.567 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
---
# About Us
<img src="https://squirro.com/wp-content/themes/squirro/img/squirro_logo.svg" alt="Squirro Logo" width="250"/>
Squirro marries data from any source with your intent, and your context to intelligently augment decision-making - right when you need it!
An Insight Engine at its core, Squirro works with global organizations, primarily in financial services, public sector, professional services, and manufacturing, among others. Customers include Bank of England, European Central Bank (ECB), Deutsche Bundesbank, Standard Chartered, Henkel, Armacell, Candriam, and many other world-leading firms.
Founded in 2012, Squirro is currently present in Zürich, London, New York, and Singapore. Further information about AI-driven business insights can be found at http://squirro.com.
## Social media profiles:
- Redefining AI Podcast (Spotify): https://open.spotify.com/show/6NPLcv9EyaD2DcNT8v89Kb
- Redefining AI Podcast (Apple Podcasts): https://podcasts.apple.com/us/podcast/redefining-ai/id1613934397
- Squirro LinkedIn: https://www.linkedin.com/company/squirroag
- Squirro Academy LinkedIn: https://www.linkedin.com/showcase/the-squirro-academy
- Twitter: https://twitter.com/Squirro
- Facebook: https://www.facebook.com/squirro
- Instagram: https://www.instagram.com/squirro/
|
ambekarsameer/distilbert-base-uncased-finetuned-cola
|
ambekarsameer
| 2022-06-29T08:26:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-29T08:16:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5337700382788287
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8051
- Matthews Correlation: 0.5338
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5233 | 1.0 | 535 | 0.5324 | 0.4151 |
| 0.3489 | 2.0 | 1070 | 0.5132 | 0.4836 |
| 0.2392 | 3.0 | 1605 | 0.5852 | 0.5177 |
| 0.1822 | 4.0 | 2140 | 0.7485 | 0.5256 |
| 0.1382 | 5.0 | 2675 | 0.8051 | 0.5338 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
cwkeam/m-ctc-t-large-lid
|
cwkeam
| 2022-06-29T08:11:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mctct",
"speech",
"en",
"dataset:librispeech_asr",
"dataset:common_voice",
"arxiv:2111.00161",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T08:08:36Z |
---
language: en
datasets:
- librispeech_asr
- common_voice
tags:
- speech
license: apache-2.0
---
# M-CTC-T
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal.
The original Flashlight code, model checkpoints, and Colab notebook can be found at https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl .
## Citation
[Paper](https://arxiv.org/abs/2111.00161)
Authors: Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert
```
@article{lugosch2021pseudo,
title={Pseudo-Labeling for Massively Multilingual Speech Recognition},
author={Lugosch, Loren and Likhomanenko, Tatiana and Synnaeve, Gabriel and Collobert, Ronan},
journal={ICASSP},
year={2022}
}
```
Additional thanks to [Chan Woo Kim](https://huggingface.co/cwkeam) and [Patrick von Platen](https://huggingface.co/patrickvonplaten) for porting the model from Flashlight to PyTorch.
# Training method
 TO-DO: replace with the training diagram from paper
For more information on how the model was trained, please take a look at the [official paper](https://arxiv.org/abs/2111.00161).
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import MCTCTForCTC, MCTCTProcessor
model = MCTCTForCTC.from_pretrained("speechbrain/mctct-large")
processor = MCTCTProcessor.from_pretrained("speechbrain/mctct-large")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_features = processor(ds[0]["audio"]["array"], return_tensors="pt").input_features
# retrieve logits
logits = model(input_features).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
Results for Common Voice, averaged over all languages:
*Character error rate (CER)*:
| Valid | Test |
|-------|------|
| 21.4 | 23.3 |
|
prithivida/bert-for-patents-64d
|
prithivida
| 2022-06-29T07:47:23Z | 41 | 8 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"masked-lm",
"en",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-31T06:40:35Z |
---
language:
- en
tags:
- masked-lm
- pytorch
pipeline-tag: "fill-mask"
mask-token: "[MASK]"
widget:
- text: "The present [MASK] provides a torque sensor that is small and highly rigid and for which high production efficiency is possible."
- text: "The present invention relates to [MASK] accessories and pertains particularly to a brake light unit for bicycles."
- text: "The present invention discloses a space-bound-free [MASK] and its coordinate determining circuit for determining a coordinate of a stylus pen."
- text: "The illuminated [MASK] includes a substantially translucent canopy supported by a plurality of ribs pivotally swingable towards and away from a shaft."
license: apache-2.0
metrics:
- perplexity
---
# Motivation
This model is based on anferico/bert-for-patents - a BERT<sub>LARGE</sub> model (See next section for details below). By default, the pre-trained model's output embeddings with size 768 (base-models) or with size 1024 (large-models). However, when you store Millions of embeddings, this can require quite a lot of memory/storage. So have reduced the embedding dimension to 64 i.e 1/16th of 1024 using Principle Component Analysis (PCA) and it still gives a comparable performance. Yes! PCA gives better performance than NMF. Note: This process neither improves the runtime, nor the memory requirement for running the model. It only reduces the needed space to store embeddings, for example, for semantic search using vector databases.
# BERT for Patents
BERT for Patents is a model trained by Google on 100M+ patents (not just US patents).
If you want to learn more about the model, check out the [blog post](https://cloud.google.com/blog/products/ai-machine-learning/how-ai-improves-patent-analysis), [white paper](https://services.google.com/fh/files/blogs/bert_for_patents_white_paper.pdf) and [GitHub page](https://github.com/google/patents-public-data/blob/master/models/BERT%20for%20Patents.md) containing the original TensorFlow checkpoint.
---
### Projects using this model (or variants of it):
- [Patents4IPPC](https://github.com/ec-jrc/Patents4IPPC) (carried out by [Pi School](https://picampus-school.com/) and commissioned by the [Joint Research Centre (JRC)](https://ec.europa.eu/jrc/en) of the European Commission)
|
iiShreya/frozenLake_8x8_Slippery
|
iiShreya
| 2022-06-29T05:50:25Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T05:50:17Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: frozenLake_8x8_Slippery
results:
- metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="iiShreya/frozenLake_8x8_Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
iiShreya/q-FrozenLake-v1-4x4-noSlippery
|
iiShreya
| 2022-06-29T05:28:15Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T05:28:08Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
RodrigoGuerra/bert-base-spanish-wwm-uncased-finetuned-clinical
|
RodrigoGuerra
| 2022-06-29T05:26:54Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-29T04:04:21Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-spanish-wwm-uncased-finetuned-clinical
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-uncased-finetuned-clinical
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7962
- F1: 0.1081
## 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: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 1.1202 | 1.0 | 2007 | 1.0018 | 0.0062 |
| 1.0153 | 2.0 | 4014 | 0.9376 | 0.0166 |
| 0.9779 | 3.0 | 6021 | 0.9026 | 0.0342 |
| 0.9598 | 4.0 | 8028 | 0.8879 | 0.0337 |
| 0.9454 | 5.0 | 10035 | 0.8699 | 0.0598 |
| 0.9334 | 6.0 | 12042 | 0.8546 | 0.0682 |
| 0.9263 | 7.0 | 14049 | 0.8533 | 0.0551 |
| 0.9279 | 8.0 | 16056 | 0.8538 | 0.0715 |
| 0.9184 | 9.0 | 18063 | 0.8512 | 0.0652 |
| 0.9151 | 10.0 | 20070 | 0.8313 | 0.0789 |
| 0.9092 | 11.0 | 22077 | 0.8299 | 0.0838 |
| 0.9083 | 12.0 | 24084 | 0.8331 | 0.0718 |
| 0.9057 | 13.0 | 26091 | 0.8319 | 0.0719 |
| 0.9018 | 14.0 | 28098 | 0.8133 | 0.0969 |
| 0.9068 | 15.0 | 30105 | 0.8234 | 0.0816 |
| 0.9034 | 16.0 | 32112 | 0.8151 | 0.0899 |
| 0.9008 | 17.0 | 34119 | 0.8145 | 0.0967 |
| 0.8977 | 18.0 | 36126 | 0.8168 | 0.0891 |
| 0.898 | 19.0 | 38133 | 0.8167 | 0.0818 |
| 0.8956 | 20.0 | 40140 | 0.8076 | 0.1030 |
| 0.8983 | 21.0 | 42147 | 0.8129 | 0.0867 |
| 0.896 | 22.0 | 44154 | 0.8118 | 0.0892 |
| 0.8962 | 23.0 | 46161 | 0.8066 | 0.1017 |
| 0.8917 | 24.0 | 48168 | 0.8154 | 0.0908 |
| 0.8923 | 25.0 | 50175 | 0.8154 | 0.0897 |
| 0.8976 | 26.0 | 52182 | 0.8089 | 0.0910 |
| 0.8926 | 27.0 | 54189 | 0.8069 | 0.0947 |
| 0.8911 | 28.0 | 56196 | 0.8170 | 0.0882 |
| 0.8901 | 29.0 | 58203 | 0.7991 | 0.1112 |
| 0.8934 | 30.0 | 60210 | 0.7996 | 0.1112 |
| 0.8903 | 31.0 | 62217 | 0.8049 | 0.0950 |
| 0.8924 | 32.0 | 64224 | 0.8116 | 0.0951 |
| 0.8887 | 33.0 | 66231 | 0.7982 | 0.1075 |
| 0.8922 | 34.0 | 68238 | 0.8013 | 0.1025 |
| 0.8871 | 35.0 | 70245 | 0.8064 | 0.0979 |
| 0.8913 | 36.0 | 72252 | 0.8108 | 0.0909 |
| 0.8924 | 37.0 | 74259 | 0.8081 | 0.0889 |
| 0.8848 | 38.0 | 76266 | 0.7923 | 0.1228 |
| 0.8892 | 39.0 | 78273 | 0.8025 | 0.0959 |
| 0.8886 | 40.0 | 80280 | 0.7954 | 0.1148 |
| 0.8938 | 41.0 | 82287 | 0.8017 | 0.1058 |
| 0.8897 | 42.0 | 84294 | 0.7946 | 0.1146 |
| 0.8906 | 43.0 | 86301 | 0.7983 | 0.1102 |
| 0.889 | 44.0 | 88308 | 0.8068 | 0.0950 |
| 0.8872 | 45.0 | 90315 | 0.7999 | 0.1089 |
| 0.8902 | 46.0 | 92322 | 0.7992 | 0.0999 |
| 0.8912 | 47.0 | 94329 | 0.7981 | 0.1048 |
| 0.886 | 48.0 | 96336 | 0.8024 | 0.0991 |
| 0.8848 | 49.0 | 98343 | 0.8026 | 0.0984 |
| 0.8866 | 50.0 | 100350 | 0.7965 | 0.1135 |
| 0.8848 | 51.0 | 102357 | 0.8054 | 0.0926 |
| 0.8863 | 52.0 | 104364 | 0.8068 | 0.0917 |
| 0.8866 | 53.0 | 106371 | 0.7993 | 0.0964 |
| 0.8823 | 54.0 | 108378 | 0.7929 | 0.1126 |
| 0.8911 | 55.0 | 110385 | 0.7938 | 0.1132 |
| 0.8911 | 56.0 | 112392 | 0.7932 | 0.1144 |
| 0.8866 | 57.0 | 114399 | 0.8018 | 0.0957 |
| 0.8841 | 58.0 | 116406 | 0.7976 | 0.1015 |
| 0.8874 | 59.0 | 118413 | 0.8035 | 0.0966 |
| 0.887 | 60.0 | 120420 | 0.7954 | 0.1112 |
| 0.888 | 61.0 | 122427 | 0.7927 | 0.1164 |
| 0.8845 | 62.0 | 124434 | 0.7982 | 0.1012 |
| 0.8848 | 63.0 | 126441 | 0.7978 | 0.1034 |
| 0.8857 | 64.0 | 128448 | 0.8036 | 0.0969 |
| 0.8827 | 65.0 | 130455 | 0.7958 | 0.1036 |
| 0.8878 | 66.0 | 132462 | 0.7983 | 0.1030 |
| 0.885 | 67.0 | 134469 | 0.7956 | 0.1055 |
| 0.8859 | 68.0 | 136476 | 0.7964 | 0.1058 |
| 0.8872 | 69.0 | 138483 | 0.7989 | 0.1005 |
| 0.8841 | 70.0 | 140490 | 0.7949 | 0.1138 |
| 0.8846 | 71.0 | 142497 | 0.7960 | 0.1062 |
| 0.8867 | 72.0 | 144504 | 0.7965 | 0.1058 |
| 0.8856 | 73.0 | 146511 | 0.7980 | 0.1007 |
| 0.8852 | 74.0 | 148518 | 0.7971 | 0.1012 |
| 0.8841 | 75.0 | 150525 | 0.7975 | 0.1049 |
| 0.8865 | 76.0 | 152532 | 0.7981 | 0.1010 |
| 0.8887 | 77.0 | 154539 | 0.7945 | 0.1095 |
| 0.8853 | 78.0 | 156546 | 0.7965 | 0.1053 |
| 0.8843 | 79.0 | 158553 | 0.7966 | 0.1062 |
| 0.8858 | 80.0 | 160560 | 0.7962 | 0.1081 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.9.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
domenicrosati/deberta-mlm-test
|
domenicrosati
| 2022-06-29T05:17:09Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-28T23:53:45Z |
---
license: mit
tags:
- fill-mask
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-mlm-test
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. -->
# deberta-mlm-test
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2792
- Accuracy: 0.4766
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.4466 | 1.0 | 2067 | 4.1217 | 0.3847 |
| 3.9191 | 2.0 | 4134 | 3.6562 | 0.4298 |
| 3.6397 | 3.0 | 6201 | 3.4417 | 0.4550 |
| 3.522 | 4.0 | 8268 | 3.3239 | 0.4692 |
| 3.4504 | 5.0 | 10335 | 3.2792 | 0.4766 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0a0+17540c5
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Abhinandan/Atari
|
Abhinandan
| 2022-06-29T04:59:38Z | 5 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-29T04:38:24Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 14.50 +/- 12.34
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Abhinandan -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Abhinandan
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Shikenrua/distilbert-base-uncased-finetuned-emotion
|
Shikenrua
| 2022-06-29T04:46:53Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-17T05:16:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
huggingtweets/reallifemera
|
huggingtweets
| 2022-06-29T04:14:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-27T22:32:37Z |
---
language: en
thumbnail: http://www.huggingtweets.com/reallifemera/1656476064337/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/1525581631020576771/qgSl4j4O_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mera Brown</div>
<div style="text-align: center; font-size: 14px;">@reallifemera</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 Mera Brown.
| Data | Mera Brown |
| --- | --- |
| Tweets downloaded | 944 |
| Retweets | 22 |
| Short tweets | 98 |
| Tweets kept | 824 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wqhoe3wp/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 @reallifemera's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/nuhzlovs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/nuhzlovs/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/reallifemera')
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)
|
Rami/qa-adhd
|
Rami
| 2022-06-29T01:41:36Z | 0 | 0 | null |
[
"pytorch",
"license:mit",
"region:us"
] | null | 2022-06-26T22:49:40Z |
---
license: mit
widget:
- text: "Jens Peter Hansen kommer fra Danmark"
---
|
gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v3
|
gary109
| 2022-06-29T01:22:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-28T14:58:21Z |
---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v3
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. -->
# ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v3
This model is a fine-tuned version of [gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v2](https://huggingface.co/gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v2) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5265
- Wer: 0.2256
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2546 | 1.0 | 280 | 0.6004 | 0.2796 |
| 0.2325 | 2.0 | 560 | 0.6337 | 0.2729 |
| 0.2185 | 3.0 | 840 | 0.5546 | 0.2299 |
| 0.1988 | 4.0 | 1120 | 0.5265 | 0.2256 |
| 0.1755 | 5.0 | 1400 | 0.5577 | 0.2212 |
| 0.1474 | 6.0 | 1680 | 0.6353 | 0.2241 |
| 0.1498 | 7.0 | 1960 | 0.5758 | 0.2086 |
| 0.1252 | 8.0 | 2240 | 0.5738 | 0.2052 |
| 0.1174 | 9.0 | 2520 | 0.5994 | 0.2048 |
| 0.1035 | 10.0 | 2800 | 0.5988 | 0.2038 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
jdang/dummy-model
|
jdang
| 2022-06-29T00:30:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-29T00:15:47Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# DistilBERT base model (dummy test)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is uncased: it does
not make a difference between english and English.
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. 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 using the BERT base model. More precisely, it was pretrained
with three objectives:
- Distillation loss: the model was trained to return the same probabilities as the BERT base model.
- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
tokens. It allows the model to learn a bidirectional representation of the sentence.
- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
model.
This way, the model learns the same inner representation of the English language than its teacher model, while being
faster for inference or downstream tasks.
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.05292855575680733,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.03968575969338417,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a business model. [SEP]",
'score': 0.034743521362543106,
'token': 2449,
'token_str': 'business'},
{'sequence': "[CLS] hello i'm a model model. [SEP]",
'score': 0.03462274372577667,
'token': 2944,
'token_str': 'model'},
{'sequence': "[CLS] hello i'm a modeling model. [SEP]",
'score': 0.018145186826586723,
'token': 11643,
'token_str': 'modeling'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import DistilBertTokenizer, DistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertModel.from_pretrained("distilbert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import DistilBertTokenizer, TFDistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
>>> unmasker("The White man worked as a [MASK].")
[{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]',
'score': 0.1235365942120552,
'token': 20987,
'token_str': 'blacksmith'},
{'sequence': '[CLS] the white man worked as a carpenter. [SEP]',
'score': 0.10142576694488525,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the white man worked as a farmer. [SEP]',
'score': 0.04985016956925392,
'token': 7500,
'token_str': 'farmer'},
{'sequence': '[CLS] the white man worked as a miner. [SEP]',
'score': 0.03932540491223335,
'token': 18594,
'token_str': 'miner'},
{'sequence': '[CLS] the white man worked as a butcher. [SEP]',
'score': 0.03351764753460884,
'token': 14998,
'token_str': 'butcher'}]
>>> unmasker("The Black woman worked as a [MASK].")
[{'sequence': '[CLS] the black woman worked as a waitress. [SEP]',
'score': 0.13283951580524445,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
'score': 0.12586183845996857,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the black woman worked as a maid. [SEP]',
'score': 0.11708822101354599,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the black woman worked as a prostitute. [SEP]',
'score': 0.11499975621700287,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]',
'score': 0.04722772538661957,
'token': 22583,
'token_str': 'housekeeper'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 8 16 GB V100 for 90 hours. See the
[training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters
details.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
| | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 |
### BibTeX entry and citation info
```bibtex
@article{Sanh2019DistilBERTAD,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
journal={ArXiv},
year={2019},
volume={abs/1910.01108}
}
```
<a href="https://huggingface.co/exbert/?model=distilbert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
workRL/q-FrozenLake-v1-4x4-noSlippery
|
workRL
| 2022-06-28T23:47:39Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T23:47:32Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
egumasa/bert-base-uncased-finetuned-academic
|
egumasa
| 2022-06-28T22:55:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:elsevier-oa-cc-by",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-28T20:26:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- elsevier-oa-cc-by
model-index:
- name: bert-base-uncased-finetuned-academic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-academic
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the elsevier-oa-cc-by dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5893
## 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: 40
- eval_batch_size: 40
- seed: 42
- optimizer: Adam with betas=(0.9,0.97) and epsilon=0.0001
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9591 | 0.25 | 820 | 2.6567 |
| 2.7993 | 0.5 | 1640 | 2.6006 |
| 2.7519 | 0.75 | 2460 | 2.5707 |
| 2.7319 | 1.0 | 3280 | 2.5763 |
| 2.7359 | 1.25 | 4100 | 2.5866 |
| 2.7451 | 1.5 | 4920 | 2.5855 |
| 2.7421 | 1.75 | 5740 | 2.5770 |
| 2.7319 | 2.0 | 6560 | 2.5762 |
| 2.7356 | 2.25 | 7380 | 2.5807 |
| 2.7376 | 2.5 | 8200 | 2.5813 |
| 2.7386 | 2.75 | 9020 | 2.5841 |
| 2.7378 | 3.0 | 9840 | 2.5737 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Aalaa/opt-125m-wikitext2
|
Aalaa
| 2022-06-28T22:39:40Z | 53 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-28T21:52:26Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: opt-125m-wikitext2
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. -->
# opt-125m-wikitext2
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3409
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4123 | 1.0 | 2370 | 3.3621 |
| 3.2096 | 2.0 | 4740 | 3.3452 |
| 3.0822 | 3.0 | 7110 | 3.3409 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Aalaa/distilgpt2-finetuned-wikitext2
|
Aalaa
| 2022-06-28T21:26:23Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-28T01:45:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
PrimeQA/tydiqa-boolean-question-classifier
|
PrimeQA
| 2022-06-28T20:19:31Z | 5,988 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:1810.04805",
"arxiv:2206.08441",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-09T14:54:52Z |
---
license: apache-2.0
---
## Model description
A question type classification model based on multilingual BERT.
The question type classifier takes as input the question, and returns a label that distinguishes between boolean and short answer extractive questions.
The model was initialized with [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) and fine-tuned on the answerable subset of [TyDiQA](https://huggingface.co/datasets/tydiqa) train questions.
## Intended uses & limitations
You can use the raw model for question classification. Biases associated with the pre-existing language model, bert-base-multilingual-cased, may be present in our fine-tuned model, tydiqa-boolean-question-classifier.
## Usage
You can use this model directly in the the [PrimeQA](https://github.com/primeqa/primeqa) framework for supporting boolean question in reading comprehension as in this [example](https://github.com/primeqa/primeqa/tree/main/examples/boolqa).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
```bibtex
@misc{https://doi.org/10.48550/arxiv.2206.08441,
author = {McCarley, Scott and
Bornea, Mihaela and
Rosenthal, Sara and
Ferritto, Anthony and
Sultan, Md Arafat and
Sil, Avirup and
Florian, Radu},
title = {GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions},
journal = {CoRR},
publisher = {arXiv},
year = {2022},
url = {https://arxiv.org/abs/2206.08441},
}
```
|
czearing/article-title-generator
|
czearing
| 2022-06-28T20:08:16Z | 1,175 | 21 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-28T19:44:19Z |
---
license: mit
---
## Article Title Generator
The model is based on the T5 language model and trained using a large collection of Medium articles.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("czearing/article-title-generator")
model = AutoModel.from_pretrained("czearing/article-title-generator")
```
## License
MIT
|
YushiUeda/callhome_adapt_real
|
YushiUeda
| 2022-06-28T19:34:58Z | 5 | 0 |
espnet
|
[
"espnet",
"audio",
"diarization",
"dataset:callhome",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-28T19:34:35Z |
---
tags:
- espnet
- audio
- diarization
language: noinfo
datasets:
- callhome
license: cc-by-4.0
---
## ESPnet2 DIAR model
### `YushiUeda/callhome_adapt_real`
This model was trained by YushiUeda using callhome recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 0cabe65afd362122e77b04e2e967986a91de0fd8
pip install -e .
cd egs2/callhome/diar1
./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/callhome_adapt_real
```
<!-- Generated by scripts/utils/show_diar_result.sh -->
# RESULTS
## Environments
- date: `Mon Jun 20 10:30:23 EDT 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 202205`
- pytorch version: `pytorch 1.9.1+cu102`
- Git hash: `fc62b1ce3e50c5ef8a2ac8cedb0d92ac41df54ca`
- Commit date: `Thu Jun 9 16:29:52 2022 +0900`
## diar_train_diar_eda_adapt_real_lr0001
### DER
diarized_callhome2_spkall
|threshold_median_collar|DER|
|---|---|
|result_th0.3_med11_collar0.25|22.29|
|result_th0.3_med1_collar0.25|23.27|
|result_th0.4_med11_collar0.25|19.85|
|result_th0.4_med1_collar0.25|20.80|
|result_th0.5_med11_collar0.25|19.26|
|result_th0.5_med1_collar0.25|20.18|
|result_th0.6_med11_collar0.25|20.24|
|result_th0.6_med1_collar0.25|21.08|
|result_th0.7_med11_collar0.25|22.38|
|result_th0.7_med1_collar0.25|23.17|
## DIAR config
<details><summary>expand</summary>
```
config: conf/tuning/train_diar_eda_adapt.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/diar_train_diar_eda_adapt_real_lr0001
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
- - train
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 16
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- exp/diar_train_diar_eda_adapt_simu/latest.pth
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 1
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/diar_stats_8k/train/speech_shape
- exp/diar_stats_8k/train/spk_labels_shape
valid_shape_file:
- exp/diar_stats_8k/valid/speech_shape
- exp/diar_stats_8k/valid/spk_labels_shape
batch_type: sorted
valid_batch_type: null
fold_length:
- 80000
- 800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/callhome1_spkall/wav.scp
- speech
- sound
- - dump/raw/callhome1_spkall/espnet_rttm
- spk_labels
- rttm
valid_data_path_and_name_and_type:
- - dump/raw/callhome2_spkall/wav.scp
- speech
- sound
- - dump/raw/callhome2_spkall/espnet_rttm
- spk_labels
- rttm
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
scheduler: null
scheduler_conf: {}
num_spk: 7
init: null
input_size: null
model_conf:
attractor_weight: 1.0
use_preprocessor: true
frontend: default
frontend_conf:
fs: 8k
hop_length: 128
specaug: specaug
specaug_conf:
apply_time_warp: false
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/diar_stats_8k/train/feats_stats.npz
encoder: transformer
encoder_conf:
input_layer: conv2d
num_blocks: 4
linear_units: 512
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.1
decoder: linear
decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf:
win_length: 1024
hop_length: 512
attractor: rnn
attractor_conf:
unit: 256
layer: 1
dropout: 0.0
attractor_grad: false
required:
- output_dir
version: '202204'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
uvd174/baseline-ppo-LunarLander-v2
|
uvd174
| 2022-06-28T19:28:55Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T13:53:24Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 291.42 +/- 16.47
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
Shaier/distilbert-base-uncased-continued_training-medqa
|
Shaier
| 2022-06-28T19:04:13Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-22T04:20:40Z |
---
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-continued_training-medqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-continued_training-medqa
This model is a fine-tuned version of [Shaier/distilbert-base-uncased-continued_training-medqa](https://huggingface.co/Shaier/distilbert-base-uncased-continued_training-medqa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5389
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 220
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log | 1.0 | 333 | 0.4516 |
| No log | 2.0 | 666 | 0.4277 |
| No log | 3.0 | 999 | 0.3734 |
| No log | 4.0 | 1332 | 0.4083 |
| No log | 5.0 | 1665 | 0.4134 |
| No log | 6.0 | 1998 | 0.5093 |
| No log | 7.0 | 2331 | 0.4639 |
| 0.4564 | 8.0 | 2664 | 0.5132 |
| 0.4564 | 9.0 | 2997 | 0.3483 |
| 0.4564 | 10.0 | 3330 | 0.4174 |
| 0.4564 | 11.0 | 3663 | 0.4975 |
| 0.4564 | 12.0 | 3996 | 0.4030 |
| 0.4564 | 13.0 | 4329 | 0.4476 |
| 0.4564 | 14.0 | 4662 | 0.3692 |
| 0.4564 | 15.0 | 4995 | 0.4474 |
| 0.4533 | 16.0 | 5328 | 0.3289 |
| 0.4533 | 17.0 | 5661 | 0.4647 |
| 0.4533 | 18.0 | 5994 | 0.4873 |
| 0.4533 | 19.0 | 6327 | 0.5323 |
| 0.4533 | 20.0 | 6660 | 0.4273 |
| 0.4533 | 21.0 | 6993 | 0.3426 |
| 0.4533 | 22.0 | 7326 | 0.3892 |
| 0.4533 | 23.0 | 7659 | 0.4297 |
| 0.4493 | 24.0 | 7992 | 0.4162 |
| 0.4493 | 25.0 | 8325 | 0.4424 |
| 0.4493 | 26.0 | 8658 | 0.4575 |
| 0.4493 | 27.0 | 8991 | 0.4192 |
| 0.4493 | 28.0 | 9324 | 0.4151 |
| 0.4493 | 29.0 | 9657 | 0.4321 |
| 0.4493 | 30.0 | 9990 | 0.4129 |
| 0.4493 | 31.0 | 10323 | 0.4869 |
| 0.4456 | 32.0 | 10656 | 0.4510 |
| 0.4456 | 33.0 | 10989 | 0.5263 |
| 0.4456 | 34.0 | 11322 | 0.3908 |
| 0.4456 | 35.0 | 11655 | 0.5016 |
| 0.4456 | 36.0 | 11988 | 0.4454 |
| 0.4456 | 37.0 | 12321 | 0.4011 |
| 0.4456 | 38.0 | 12654 | 0.4714 |
| 0.4456 | 39.0 | 12987 | 0.4972 |
| 0.443 | 40.0 | 13320 | 0.4200 |
| 0.443 | 41.0 | 13653 | 0.4659 |
| 0.443 | 42.0 | 13986 | 0.4758 |
| 0.443 | 43.0 | 14319 | 0.4509 |
| 0.443 | 44.0 | 14652 | 0.4211 |
| 0.443 | 45.0 | 14985 | 0.4007 |
| 0.443 | 46.0 | 15318 | 0.3205 |
| 0.443 | 47.0 | 15651 | 0.4479 |
| 0.4402 | 48.0 | 15984 | 0.4723 |
| 0.4402 | 49.0 | 16317 | 0.4956 |
| 0.4402 | 50.0 | 16650 | 0.4103 |
| 0.4402 | 51.0 | 16983 | 0.4234 |
| 0.4402 | 52.0 | 17316 | 0.4052 |
| 0.4402 | 53.0 | 17649 | 0.4033 |
| 0.4402 | 54.0 | 17982 | 0.4139 |
| 0.4402 | 55.0 | 18315 | 0.3618 |
| 0.4372 | 56.0 | 18648 | 0.5102 |
| 0.4372 | 57.0 | 18981 | 0.4166 |
| 0.4372 | 58.0 | 19314 | 0.4475 |
| 0.4372 | 59.0 | 19647 | 0.4259 |
| 0.4372 | 60.0 | 19980 | 0.4018 |
| 0.4372 | 61.0 | 20313 | 0.5005 |
| 0.4372 | 62.0 | 20646 | 0.4445 |
| 0.4372 | 63.0 | 20979 | 0.4280 |
| 0.434 | 64.0 | 21312 | 0.4533 |
| 0.434 | 65.0 | 21645 | 0.3672 |
| 0.434 | 66.0 | 21978 | 0.4726 |
| 0.434 | 67.0 | 22311 | 0.4084 |
| 0.434 | 68.0 | 22644 | 0.4508 |
| 0.434 | 69.0 | 22977 | 0.3746 |
| 0.434 | 70.0 | 23310 | 0.4703 |
| 0.434 | 71.0 | 23643 | 0.4789 |
| 0.4314 | 72.0 | 23976 | 0.3963 |
| 0.4314 | 73.0 | 24309 | 0.3800 |
| 0.4314 | 74.0 | 24642 | 0.5051 |
| 0.4314 | 75.0 | 24975 | 0.4245 |
| 0.4314 | 76.0 | 25308 | 0.4745 |
| 0.4314 | 77.0 | 25641 | 0.4351 |
| 0.4314 | 78.0 | 25974 | 0.4367 |
| 0.4314 | 79.0 | 26307 | 0.4200 |
| 0.4291 | 80.0 | 26640 | 0.4985 |
| 0.4291 | 81.0 | 26973 | 0.5058 |
| 0.4291 | 82.0 | 27306 | 0.4154 |
| 0.4291 | 83.0 | 27639 | 0.4837 |
| 0.4291 | 84.0 | 27972 | 0.3865 |
| 0.4291 | 85.0 | 28305 | 0.4357 |
| 0.4291 | 86.0 | 28638 | 0.3978 |
| 0.4291 | 87.0 | 28971 | 0.4413 |
| 0.4263 | 88.0 | 29304 | 0.4223 |
| 0.4263 | 89.0 | 29637 | 0.4241 |
| 0.4263 | 90.0 | 29970 | 0.4525 |
| 0.4263 | 91.0 | 30303 | 0.3895 |
| 0.4263 | 92.0 | 30636 | 0.4207 |
| 0.4263 | 93.0 | 30969 | 0.3217 |
| 0.4263 | 94.0 | 31302 | 0.3725 |
| 0.4263 | 95.0 | 31635 | 0.4354 |
| 0.4239 | 96.0 | 31968 | 0.4169 |
| 0.4239 | 97.0 | 32301 | 0.4873 |
| 0.4239 | 98.0 | 32634 | 0.4219 |
| 0.4239 | 99.0 | 32967 | 0.4984 |
| 0.4239 | 100.0 | 33300 | 0.4078 |
| 0.4239 | 101.0 | 33633 | 0.4463 |
| 0.4239 | 102.0 | 33966 | 0.3371 |
| 0.4239 | 103.0 | 34299 | 0.3896 |
| 0.422 | 104.0 | 34632 | 0.4743 |
| 0.422 | 105.0 | 34965 | 0.4931 |
| 0.422 | 106.0 | 35298 | 0.3574 |
| 0.422 | 107.0 | 35631 | 0.4127 |
| 0.422 | 108.0 | 35964 | 0.3892 |
| 0.422 | 109.0 | 36297 | 0.3881 |
| 0.422 | 110.0 | 36630 | 0.4221 |
| 0.422 | 111.0 | 36963 | 0.3924 |
| 0.4204 | 112.0 | 37296 | 0.4067 |
| 0.4204 | 113.0 | 37629 | 0.4357 |
| 0.4204 | 114.0 | 37962 | 0.4175 |
| 0.4204 | 115.0 | 38295 | 0.4424 |
| 0.4204 | 116.0 | 38628 | 0.3925 |
| 0.4204 | 117.0 | 38961 | 0.4693 |
| 0.4204 | 118.0 | 39294 | 0.3503 |
| 0.4204 | 119.0 | 39627 | 0.4761 |
| 0.4183 | 120.0 | 39960 | 0.3816 |
| 0.4183 | 121.0 | 40293 | 0.3903 |
| 0.4183 | 122.0 | 40626 | 0.3535 |
| 0.4183 | 123.0 | 40959 | 0.4388 |
| 0.4183 | 124.0 | 41292 | 0.4519 |
| 0.4183 | 125.0 | 41625 | 0.4241 |
| 0.4183 | 126.0 | 41958 | 0.4085 |
| 0.4183 | 127.0 | 42291 | 0.4836 |
| 0.4168 | 128.0 | 42624 | 0.4101 |
| 0.4168 | 129.0 | 42957 | 0.4749 |
| 0.4168 | 130.0 | 43290 | 0.4022 |
| 0.4168 | 131.0 | 43623 | 0.4861 |
| 0.4168 | 132.0 | 43956 | 0.4376 |
| 0.4168 | 133.0 | 44289 | 0.4597 |
| 0.4168 | 134.0 | 44622 | 0.4154 |
| 0.4168 | 135.0 | 44955 | 0.4431 |
| 0.415 | 136.0 | 45288 | 0.4887 |
| 0.415 | 137.0 | 45621 | 0.4229 |
| 0.415 | 138.0 | 45954 | 0.3997 |
| 0.415 | 139.0 | 46287 | 0.4185 |
| 0.415 | 140.0 | 46620 | 0.4633 |
| 0.415 | 141.0 | 46953 | 0.4061 |
| 0.415 | 142.0 | 47286 | 0.4604 |
| 0.415 | 143.0 | 47619 | 0.4047 |
| 0.4139 | 144.0 | 47952 | 0.4272 |
| 0.4139 | 145.0 | 48285 | 0.4783 |
| 0.4139 | 146.0 | 48618 | 0.3954 |
| 0.4139 | 147.0 | 48951 | 0.4501 |
| 0.4139 | 148.0 | 49284 | 0.4941 |
| 0.4139 | 149.0 | 49617 | 0.4112 |
| 0.4139 | 150.0 | 49950 | 0.4582 |
| 0.4139 | 151.0 | 50283 | 0.4361 |
| 0.4126 | 152.0 | 50616 | 0.3535 |
| 0.4126 | 153.0 | 50949 | 0.3797 |
| 0.4126 | 154.0 | 51282 | 0.4080 |
| 0.4126 | 155.0 | 51615 | 0.4049 |
| 0.4126 | 156.0 | 51948 | 0.4255 |
| 0.4126 | 157.0 | 52281 | 0.4303 |
| 0.4126 | 158.0 | 52614 | 0.4950 |
| 0.4126 | 159.0 | 52947 | 0.3721 |
| 0.4114 | 160.0 | 53280 | 0.2861 |
| 0.4114 | 161.0 | 53613 | 0.3775 |
| 0.4114 | 162.0 | 53946 | 0.4274 |
| 0.4114 | 163.0 | 54279 | 0.3904 |
| 0.4114 | 164.0 | 54612 | 0.4687 |
| 0.4114 | 165.0 | 54945 | 0.4013 |
| 0.4114 | 166.0 | 55278 | 0.4760 |
| 0.4114 | 167.0 | 55611 | 0.3554 |
| 0.4104 | 168.0 | 55944 | 0.5193 |
| 0.4104 | 169.0 | 56277 | 0.4476 |
| 0.4104 | 170.0 | 56610 | 0.5011 |
| 0.4104 | 171.0 | 56943 | 0.4441 |
| 0.4104 | 172.0 | 57276 | 0.4457 |
| 0.4104 | 173.0 | 57609 | 0.3792 |
| 0.4104 | 174.0 | 57942 | 0.5116 |
| 0.4104 | 175.0 | 58275 | 0.4249 |
| 0.4097 | 176.0 | 58608 | 0.3804 |
| 0.4097 | 177.0 | 58941 | 0.3886 |
| 0.4097 | 178.0 | 59274 | 0.4420 |
| 0.4097 | 179.0 | 59607 | 0.3573 |
| 0.4097 | 180.0 | 59940 | 0.3635 |
| 0.4097 | 181.0 | 60273 | 0.4596 |
| 0.4097 | 182.0 | 60606 | 0.3674 |
| 0.4097 | 183.0 | 60939 | 0.3869 |
| 0.409 | 184.0 | 61272 | 0.3909 |
| 0.409 | 185.0 | 61605 | 0.4339 |
| 0.409 | 186.0 | 61938 | 0.4475 |
| 0.409 | 187.0 | 62271 | 0.3218 |
| 0.409 | 188.0 | 62604 | 0.3771 |
| 0.409 | 189.0 | 62937 | 0.4007 |
| 0.409 | 190.0 | 63270 | 0.4520 |
| 0.409 | 191.0 | 63603 | 0.3980 |
| 0.4077 | 192.0 | 63936 | 0.4572 |
| 0.4077 | 193.0 | 64269 | 0.3952 |
| 0.4077 | 194.0 | 64602 | 0.4384 |
| 0.4077 | 195.0 | 64935 | 0.4795 |
| 0.4077 | 196.0 | 65268 | 0.3743 |
| 0.4077 | 197.0 | 65601 | 0.4445 |
| 0.4077 | 198.0 | 65934 | 0.3925 |
| 0.4077 | 199.0 | 66267 | 0.4564 |
| 0.4075 | 200.0 | 66600 | 0.4580 |
| 0.4075 | 201.0 | 66933 | 0.4446 |
| 0.4075 | 202.0 | 67266 | 0.4289 |
| 0.4075 | 203.0 | 67599 | 0.3722 |
| 0.4075 | 204.0 | 67932 | 0.4810 |
| 0.4075 | 205.0 | 68265 | 0.4004 |
| 0.4075 | 206.0 | 68598 | 0.4219 |
| 0.4075 | 207.0 | 68931 | 0.3926 |
| 0.407 | 208.0 | 69264 | 0.6043 |
| 0.407 | 209.0 | 69597 | 0.3835 |
| 0.407 | 210.0 | 69930 | 0.3791 |
| 0.407 | 211.0 | 70263 | 0.4152 |
| 0.407 | 212.0 | 70596 | 0.3654 |
| 0.407 | 213.0 | 70929 | 0.4434 |
| 0.407 | 214.0 | 71262 | 0.3613 |
| 0.407 | 215.0 | 71595 | 0.5103 |
| 0.4069 | 216.0 | 71928 | 0.3733 |
| 0.4069 | 217.0 | 72261 | 0.4881 |
| 0.4069 | 218.0 | 72594 | 0.3375 |
| 0.4069 | 219.0 | 72927 | 0.4766 |
| 0.4069 | 220.0 | 73260 | 0.4604 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.11.0
|
zunicd/finetuning-sentiment-model-3000-samples
|
zunicd
| 2022-06-28T18:12:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-28T17:48:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.8741721854304636
---
<!-- 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 imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3349
- Accuracy: 0.8733
- F1: 0.8742
## 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 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
facebook/regnet-x-002
|
facebook
| 2022-06-28T17:54:23Z | 142 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-15T19:34:23Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
pitronalldak/distilbert-base-uncased-finetuned-ner
|
pitronalldak
| 2022-06-28T17:30:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-24T17:24:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0709
- Precision: 0.8442
- Recall: 0.8364
- F1: 0.8403
- Accuracy: 0.9794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0442 | 1.0 | 1875 | 0.0772 | 0.7945 | 0.7627 | 0.7783 | 0.9739 |
| 0.0272 | 2.0 | 3750 | 0.0679 | 0.8465 | 0.8551 | 0.8507 | 0.9791 |
| 0.0175 | 3.0 | 5625 | 0.0709 | 0.8442 | 0.8364 | 0.8403 | 0.9794 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ranesh/qw
|
ranesh
| 2022-06-28T17:17:47Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-06-28T17:17:47Z |
---
license: bigscience-bloom-rail-1.0
---
|
DeepPavlov/distilrubert-tiny-cased-conversational
|
DeepPavlov
| 2022-06-28T17:10:33Z | 1,401 | 3 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"arxiv:1910.01108",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
language:
- ru
---
WARNING: This is `distilrubert-small-cased-conversational` model uploaded with wrong name. This one is the same as [distilrubert-small-cased-conversational](https://huggingface.co/DeepPavlov/distilrubert-small-cased-conversational). `distilrubert-tiny-cased-conversational` could be found in [distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1).
# distilrubert-small-cased-conversational
Conversational DistilRuBERT-small \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)). It can be considered as small copy of [Conversational DistilRuBERT-base](https://huggingface.co/DeepPavlov/distilrubert-base-cased-conversational).
Our DistilRuBERT-small was highly inspired by \[3\], \[4\]. Namely, we used
* KL loss (between teacher and student output logits)
* MLM loss (between tokens labels and student output logits)
* Cosine embedding loss (between averaged six consecutive hidden states from teacher's encoder and one hidden state of the student)
* MSE loss (between averaged six consecutive attention maps from teacher's encoder and one attention map of the student)
The model was trained for about 80 hrs. on 8 nVIDIA Tesla P100-SXM2.0 16Gb.
To evaluate improvements in the inference speed, we ran teacher and student models on random sequences with seq_len=512, batch_size = 16 (for throughput) and batch_size=1 (for latency).
All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb.
| Model | Size, Mb. | CPU latency, sec.| GPU latency, sec. | CPU throughput, samples/sec. | GPU throughput, samples/sec. |
|-------------------------------------------------|------------|------------------|-------------------|------------------------------|------------------------------|
| Teacher (RuBERT-base-cased-conversational) | 679 | 0.655 | 0.031 | 0.3754 | 36.4902 |
| Student (DistilRuBERT-small-cased-conversational)| 409 | 0.1656 | 0.015 | 0.9692 | 71.3553 |
To evaluate model quality, we fine-tuned DistilRuBERT-small on classification, NER and question answering tasks. Scores and archives with fine-tuned models can be found in [DeepPavlov docs](http://docs.deeppavlov.ai/en/master/features/overview.html#models).
# Citation
If you found the model useful for your research, we are kindly ask to cite [this](https://arxiv.org/abs/2205.02340) paper:
```
@misc{https://doi.org/10.48550/arxiv.2205.02340,
doi = {10.48550/ARXIV.2205.02340},
url = {https://arxiv.org/abs/2205.02340},
author = {Kolesnikova, Alina and Kuratov, Yuri and Konovalov, Vasily and Burtsev, Mikhail},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Knowledge Distillation of Russian Language Models with Reduction of Vocabulary},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
\[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
\[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.
\[3\]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. \(2019\). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
\[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>
|
crumb/gpt2-regular-large
|
crumb
| 2022-06-28T16:35:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-13T14:08:55Z |
---
tags:
- generated_from_trainer
model-index:
- name: gpt-regular-test
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. -->
# gpt-regular-test
i was stupid and all the newline tokens are replaced with [/n] so be wary if you're using the demo on this page that that just means new line
```python
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("crumb/gpt2-regular-large")
tokenizer = AutoTokenizer.from_pretrained("gpt2-large", use_fast=True)
prompt = """(Episode begins with Mordecai and Rigby watching TV)
Mordecai: Dude, what are you doing? I think I'm gonna lose my mind.
Rigby:"""
prompt=prompt.replace("\n","[/n]")
tokenz = tokenizer(prompt,return_tensors='pt')['input_ids']
output = model.generate(
tokenz,
max_length=length,
num_return_sequences=1,
top_p=.92,
temperature=.65,
do_sample=True,
top_k=125,
early_stopping=True,
pad_token_id=tokenizer.eos_token_id
)
output = tokenizer.decode(output[0]).replace("[/n]","\n")
print(output)
```
This model is a fine-tuned version of gpt2-large on the entirety of Regular Show. It achieves the following results on the evaluation set (The Power, Death Punchies, Do Me a Solid):
- Loss: 1.6383
## Intended uses & limitations
Same as gpt2-large
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1844 | 1.0 | 7633 | 1.6383 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
facebook/regnet-x-006
|
facebook
| 2022-06-28T15:41:44Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-15T19:35:27Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
facebook/regnet-x-120
|
facebook
| 2022-06-28T15:40:50Z | 68 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-18T15:26:36Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
Shanny/dbgbert-finetuned-squad
|
Shanny
| 2022-06-28T15:28:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-27T09:04:37Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: dbgbert-finetuned-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. -->
# dbgbert-finetuned-squad
This model was trained from scratch 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Salvatore/bert-finetuned-ner
|
Salvatore
| 2022-06-28T15:24:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-16T09:09:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0997
- Proteinmutation F1: 0.1309
- Snp F1: 0.1953
- Dnamutation F1: 0.3778
- Precision: 0.2380
- Recall: 0.2416
- F1: 0.2398
- Accuracy: 0.9703
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Proteinmutation F1 | Snp F1 | Dnamutation F1 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:------:|:--------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 324 | 0.0533 | 0.0396 | 0.2830 | 0.4667 | 0.2334 | 0.3221 | 0.2707 | 0.9788 |
| 0.1072 | 2.0 | 648 | 0.0437 | 0.6065 | 0.4906 | 0.5009 | 0.4802 | 0.6348 | 0.5468 | 0.9868 |
| 0.1072 | 3.0 | 972 | 0.0592 | 0.1379 | 0.2485 | 0.2005 | 0.1639 | 0.2228 | 0.1889 | 0.9731 |
| 0.0573 | 4.0 | 1296 | 0.0722 | 0.0749 | 0.2530 | 0.4692 | 0.2705 | 0.2959 | 0.2826 | 0.9749 |
| 0.0431 | 5.0 | 1620 | 0.0766 | 0.1574 | 0.1847 | 0.2540 | 0.1766 | 0.2285 | 0.1992 | 0.9723 |
| 0.0431 | 6.0 | 1944 | 0.0805 | 0.1099 | 0.2202 | 0.2383 | 0.1657 | 0.2097 | 0.1851 | 0.9715 |
| 0.0396 | 7.0 | 2268 | 0.0886 | 0.1337 | 0.2138 | 0.4318 | 0.2683 | 0.2678 | 0.2680 | 0.9724 |
| 0.0354 | 8.0 | 2592 | 0.0927 | 0.1535 | 0.2113 | 0.3769 | 0.2505 | 0.2528 | 0.2516 | 0.9714 |
| 0.0354 | 9.0 | 2916 | 0.0978 | 0.1011 | 0.2540 | 0.3812 | 0.2495 | 0.2528 | 0.2512 | 0.9705 |
| 0.0312 | 10.0 | 3240 | 0.0997 | 0.1309 | 0.1953 | 0.3778 | 0.2380 | 0.2416 | 0.2398 | 0.9703 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2
- Datasets 2.0.0
- Tokenizers 0.12.1
|
huggingtweets/g__j
|
huggingtweets
| 2022-06-28T13:36:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-28T13:36:09Z |
---
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/959389610978742273/jfOMGQ1B_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Greg Jackson</div>
<div style="text-align: center; font-size: 14px;">@g__j</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 Greg Jackson.
| Data | Greg Jackson |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 187 |
| Short tweets | 179 |
| Tweets kept | 2884 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2sl53oes/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 @g__j's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/stwh74do) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/stwh74do/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/g__j')
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)
|
choonlee/TEST2ppo-LunarLander-v2
|
choonlee
| 2022-06-28T13:12:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-28T13:11:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 179.80 +/- 62.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
huggingtweets/gregorian000-levelsio
|
huggingtweets
| 2022-06-28T13:11:29Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-28T13:11:21Z |
---
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/1501241215433510919/4GctQi3o_400x400.jpg')">
</div>
<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/1441044961957343232/Sl1U4tSw_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>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">David ⚡ & @levelsio</div>
<div style="text-align: center; font-size: 14px;">@gregorian000-levelsio</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 David ⚡ & @levelsio.
| Data | David ⚡ | @levelsio |
| --- | --- | --- |
| Tweets downloaded | 95 | 3250 |
| Retweets | 22 | 176 |
| Short tweets | 9 | 556 |
| Tweets kept | 64 | 2518 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ozvo6hl5/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 @gregorian000-levelsio's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1emg780i) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1emg780i/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/gregorian000-levelsio')
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)
|
alleniver/my_test_cat
|
alleniver
| 2022-06-28T12:12:49Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2022-06-28T12:12:21Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v5
|
gary109
| 2022-06-28T11:49:44Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-27T14:51:07Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v5
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. -->
# ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v5
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v4](https://huggingface.co/gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v4) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0163
- Wer: 0.6622
## 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: 4
- eval_batch_size: 4
- 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_steps: 100
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8867 | 1.0 | 376 | 1.0382 | 0.6821 |
| 0.8861 | 2.0 | 752 | 1.0260 | 0.6686 |
| 0.8682 | 3.0 | 1128 | 1.0358 | 0.6604 |
| 0.8662 | 4.0 | 1504 | 1.0234 | 0.6665 |
| 0.8463 | 5.0 | 1880 | 1.0333 | 0.6666 |
| 0.8573 | 6.0 | 2256 | 1.0163 | 0.6622 |
| 0.8628 | 7.0 | 2632 | 1.0209 | 0.6551 |
| 0.8493 | 8.0 | 3008 | 1.0525 | 0.6582 |
| 0.8371 | 9.0 | 3384 | 1.0409 | 0.6515 |
| 0.8229 | 10.0 | 3760 | 1.0597 | 0.6523 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
facebook/regnet-y-032
|
facebook
| 2022-06-28T11:39:30Z | 68 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-18T15:35:16Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
facebook/regnet-y-160
|
facebook
| 2022-06-28T11:39:06Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-18T15:40:45Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
facebook/regnet-y-016
|
facebook
| 2022-06-28T11:38:42Z | 64 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-18T15:34:34Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
aspis/swin-finetuned-food101
|
aspis
| 2022-06-28T11:02:36Z | 105 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-09T10:48:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-finetuned-food101
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9210297029702971
- task:
type: image-classification
name: Image Classification
dataset:
name: food101
type: food101
config: default
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.9135841584158416
verified: true
- name: Precision Macro
type: precision
value: 0.9151645786633058
verified: true
- name: Precision Micro
type: precision
value: 0.9135841584158416
verified: true
- name: Precision Weighted
type: precision
value: 0.915164578663306
verified: true
- name: Recall Macro
type: recall
value: 0.9135841584158414
verified: true
- name: Recall Micro
type: recall
value: 0.9135841584158416
verified: true
- name: Recall Weighted
type: recall
value: 0.9135841584158416
verified: true
- name: F1 Macro
type: f1
value: 0.9138785016966742
verified: true
- name: F1 Micro
type: f1
value: 0.9135841584158415
verified: true
- name: F1 Weighted
type: f1
value: 0.9138785016966743
verified: true
- name: loss
type: loss
value: 0.30761435627937317
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-finetuned-food101
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2772
- Accuracy: 0.9210
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5077 | 1.0 | 1183 | 0.3851 | 0.8893 |
| 0.3523 | 2.0 | 2366 | 0.3124 | 0.9088 |
| 0.1158 | 3.0 | 3549 | 0.2772 | 0.9210 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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