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59.7M
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Davlan/distilbert-base-multilingual-cased-masakhaner | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
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"DistilBertForTokenClassification"
],
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} | 16 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_unispeech_s558
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/m2m100_418M-eng-yor-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"M2M100ForConditionalGeneration"
],
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} | 9 | null | ---
tags:
- conversational
---
# inywer/2-0OKUOHS Model |
Davlan/mT5_base_yoruba_adr | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 5 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 114.00 +/- 54.49
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 croumegous -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 croumegous
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Davlan/xlm-roberta-base-finetuned-chichewa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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}
} | 5 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_no-pretraining_s947
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-english | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
} | 5 | 2022-07-10T13:56:49Z | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_no-pretraining_s159
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-hausa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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} | 234 | 2022-07-10T13:57:07Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- metrics:
- type: mean_reward
value: 138.40 +/- 8.59
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Davlan/xlm-roberta-base-finetuned-igbo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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}
} | 68 | 2022-07-10T14:00:03Z | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_no-pretraining_s467
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-kinyarwanda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
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} | 61 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_wavlm_s722
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-lingala | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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} | 9 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_wavlm_s474
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-luganda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
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} | 11 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_wavlm_s21
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-luo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 5 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_unispeech-ml_s417
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-naija | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 1 | 2022-07-10T14:16:08Z | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_unispeech-ml_s156
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-shona | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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}
} | 5 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_unispeech-ml_s226
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-somali | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
} | 8 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_vp-fr_s3
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-xhosa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 12 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_vp-fr_s255
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-finetuned-yoruba | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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"translation_en_to_ro": {
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}
}
} | 29 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_vp-es_s211
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-base-masakhaner | [
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 3 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_vp-es_s692
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Davlan/xlm-roberta-large-masakhaner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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"translation_en_to_ro": {
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}
}
} | 1,449 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_vp-nl_s469
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Dawit/DialogGPT-small-ironman | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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},
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},
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}
}
} | 7 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_unispeech-sat_s222
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Dbluciferm3737/Idk | [] | null | {
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}
} | 0 | null | ---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- uk
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_uk_xls-r_s246
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/Breitbart_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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},
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"max_length": null,
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}
}
} | 7 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_wav2vec2_s211
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/ChicagoTribune_model_v7 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
} | 7 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_no-pretraining_s808
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/FoxNews_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 7 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_wavlm_s803
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/FoxNews_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
} | 7 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_unispeech-ml_s365
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/FoxNews_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
} | 3 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_vp-fr_s732
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/Independent__model | [] | null | {
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} | 0 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_vp-nl_s756
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/NPR_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_unispeech-sat_s325
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/NPR_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_unispeech-sat_s504
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Declan/NPR_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- ar
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ar_unispeech-sat_s75
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] | text-generation | {
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}
} | 340 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_hubert_s995
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeepESP/gpt2-spanish | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
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"model_type": "gpt2",
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} | 1,463 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_vp-sv_s187
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] | feature-extraction | {
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],
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}
} | 1,614 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
DeepPavlov/bert-base-multilingual-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | {
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} | 140 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_vp-sv_s571
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeepPavlov/distilrubert-base-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | {
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}
} | 6,324 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_no-pretraining_s20
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeepPavlov/roberta-large-winogrande | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:winogrande",
"arxiv:1907.11692",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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} | 348 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_wavlm_s515
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeepPavlov/rubert-base-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers",
"has_space"
] | feature-extraction | {
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} | 46,991 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_unispeech-ml_s463
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeltaHub/adapter_t5-3b_mrpc | [
"pytorch",
"transformers"
] | null | {
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} | 3 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_vp-fr_s354
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeltaHub/adapter_t5-3b_qnli | [
"pytorch",
"transformers"
] | null | {
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} | 3 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_vp-fr_s807
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeskDown/MarianMix_en-ja-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
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} | 1 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_xls-r_s471
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 5 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_r-wav2vec2_s72
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Devid/DialoGPT-small-Miku | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 10 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_r-wav2vec2_s996
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Devrim/prism-default | [
"license:mit"
] | null | {
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} | 0 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_vp-it_s157
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DevsIA/Devs_IA | [] | null | {
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} | 0 | null | ---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- pl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pl_vp-it_s474
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DevsIA/imagenes | [] | null | {
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} | 0 | null | ---
library_name: keras
tags:
- image-classification
- Architecture
---
# Tensorflow Keras implementation of : [Learning to tokenize in Vision Transformers](https://keras.io/examples/vision/token_learner/)
The full credit goes to: [Aritra Roy Gosthipaty](https://twitter.com/ariG23498), [Sayak Paul](https://twitter.com/RisingSayak)
## Short description:
ViT and other Transformer based architectures break down images into patches. As we increase the resolution of the images, the number of patches increases as well. To tackle this, Ryoo et al. introduced a new module called TokenLearner which can help reduce the number of patches used. The full paper can be found [here](https://openreview.net/forum?id=z-l1kpDXs88)
## Model and Dataset used
The Dataset used here is CIFAR-10. The model used here is a mini ViT model with the TokenLearner module.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | AdamW |
| learning_rate | 0.0010000000474974513 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| weight_decay | 9.999999747378752e-05 |
| exclude_from_weight_decay | None |
| training_precision | float32 |
## Training Metrics
After 20 Epocs, the test accuracy of the model is 55.9% and the Top 5 test accuracy is 95.06%
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
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} | 0 | null | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-100k_s103
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Digakive/Hsgshs | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-superb-demo-google-colab
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-base-superb-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3795
- eval_wer: 0.3148
- eval_runtime: 26.4914
- eval_samples_per_second: 10.23
- eval_steps_per_second: 1.283
- epoch: 2.47
- step: 1500
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Waynehillsdev/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
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} | 5 | null | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_wavlm_s753
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Doquey/DialoGPT-small-Luisbot1 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 7 | 2022-07-10T23:35:36Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech-ml_s527
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 30 | null | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-es_s803
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
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} | 25 | null | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech-sat_s364
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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} | 38,156 | 2022-07-11T01:09:13Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech-sat_s108
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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}
} | 4,785,283 | 2022-07-11T01:17:39Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech-sat_s211
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
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}
} | 26,792 | 2022-07-11T01:22:44Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_xls-r_s448
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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}
} | 341 | 2022-07-11T01:32:51Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_xls-r_s107
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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}
} | 2,973 | 2022-07-11T01:41:33Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_xls-r_s662
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
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}
} | 7,091 | 2022-07-11T01:48:59Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_r-wav2vec2_s468
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
} | 42,640 | 2022-07-11T01:50:50Z | ---
title: Cryptopunks Generator
emoji: 🧠➡️🙍♀️
colorFrom: red
colorTo: indigo
sdk: gradio
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
|
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 11,644 | 2022-07-11T02:00:33Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_r-wav2vec2_s957
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 8,621,271 | 2022-07-11T02:10:50Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_r-wav2vec2_s732
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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}
} | 1,814 | 2022-07-11T02:21:41Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-it_s511
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 68,305 | 2022-07-11T02:32:18Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-it_s992
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 4,749,504 | 2022-07-11T02:38:02Z | ---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-it_s222
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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}
}
} | 59,663,489 | 2022-07-11T02:46:29Z | ---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_wav2vec2_s379
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 388,769 | 2022-07-11T02:56:05Z | ---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_wav2vec2_s754
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bert-large-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 1,058,496 | 2022-07-11T03:18:34Z | ---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-100k_s772
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
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},
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}
} | 1,440,898 | 2022-07-11T03:22:58Z | ---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-100k_s408
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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}
}
} | 257,745 | 2022-07-11T03:27:38Z | ---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_xlsr-53_s799
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null | {
"architectures": null,
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},
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},
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}
} | 574,859 | 2022-07-11T03:31:17Z | ---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_xlsr-53_s972
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
xlnet-large-cased | [
"pytorch",
"tf",
"xlnet",
"text-generation",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1906.08237",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"XLNetLMHeadModel"
],
"model_type": "xlnet",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
} | 16,389 | 2022-07-11T06:19:49Z | ---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-es_s476
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AAli/t5-small-finetuned-xsum | [] | null | {
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}
} | 0 | 2022-07-11T09:16:37Z | ---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-nl_s131
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AI4Sec/cyner-xlm-roberta-large | [
"xlm-roberta",
"token-classification",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 4 | 2022-07-11T09:49:58Z | ---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_r-wav2vec2_s399
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AIDA-UPM/MSTSb_stsb-xlm-r-multilingual | [
"pytorch",
"xlm-roberta",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers"
] | sentence-similarity | {
"architectures": [
"XLMRobertaModel"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 30 | 2022-07-11T09:56:49Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 265.62 +/- 14.05
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
...
```
|
AK/ak_nlp | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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}
}
} | 6 | 2022-07-11T10:22:18Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-100k_s957
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AKulk/wav2vec2-base-timit-demo-colab | [] | null | {
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}
} | 0 | 2022-07-11T10:28:54Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-100k_s732
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AccurateIsaiah/DialoGPT-small-mozarkv2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 12 | 2022-07-11T16:08:25Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech-sat_s42
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-quail | [
"bert",
"en",
"dataset:quail",
"arxiv:2104.08247",
"adapter-transformers"
] | null | {
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}
} | 2 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech_s952
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-quartz | [
"bert",
"en",
"dataset:quartz",
"arxiv:2104.08247",
"adapter-transformers"
] | null | {
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} | 2 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s807
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-quoref | [
"bert",
"en",
"dataset:quoref",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] | question-answering | {
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}
} | 6 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s301
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-race | [
"bert",
"en",
"dataset:race",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:rc/race"
] | null | {
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} | 2 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s486
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-record | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:rc/record"
] | text-classification | {
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} | 0 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s612
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-rotten_tomatoes | [
"bert",
"en",
"dataset:rotten_tomatoes",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/rotten_tomatoes"
] | text-classification | {
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}
} | 2 | null | ---
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: 12.70 +/- 11.50
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
|
AdapterHub/bert-base-uncased-pf-rte | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/rte"
] | text-classification | {
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}
} | 4 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s563
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-scitail | [
"bert",
"en",
"dataset:scitail",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/scitail"
] | text-classification | {
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}
} | 2 | 2022-07-11T17:54:09Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s894
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-snli | [
"bert",
"en",
"dataset:snli",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] | text-classification | {
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} | 8 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_no-pretraining_s84
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-squad | [
"bert",
"en",
"dataset:squad",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/squad1"
] | question-answering | {
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} | 9 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_no-pretraining_s541
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-squad_v2 | [
"bert",
"en",
"dataset:squad_v2",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/squad2"
] | question-answering | {
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}
} | 10 | null | ---
language:
- pt
license: cc-by-4.0
datasets:
- wiki_lingua
thumbnail: null
tags:
- named-entity-recognition
- Transformer
- pytorch
- bert
metrics:
- f1
- precision
- recall
model-index:
- name: rpunct-ptbr
results:
- task:
type: named-entity-recognition
dataset:
type: wiki_lingua
name: wiki_lingua
metrics:
- type: f1
value: 55.70
name: F1 Score
- type: precision
value: 57.72
name: Precision
- type: recall
value: 53.83
name: Recall
widget:
- text: "henrique foi no lago pescar com o pedro mais tarde foram para a casa do pedro fritar os peixes"
- text: "cinco trabalhadores da construção civil em capacetes e coletes amarelos estão ocupados no trabalho"
- text: "na quinta feira em visita a belo horizonte pedro sobrevoa a cidade atingida pelas chuvas"
- text: "coube ao representante de classe contar que na avaliação de língua portuguesa alguns alunos se mantiveram concentrados e outros dispersos"
---
# 🤗 bert-restore-punctuation-ptbr
* 🪄 [W&B Dashboard](https://wandb.ai/dominguesm/RestorePunctuationPTBR)
* ⛭ [GitHub](https://github.com/DominguesM/respunct)
This is a [bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for punctuation restoration on [WikiLingua](https://github.com/esdurmus/Wikilingua).
This model is intended for direct use as a punctuation restoration model for the general Portuguese language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks.
Model restores the following punctuations -- **[! ? . , - : ; ' ]**
The model also restores the upper-casing of words.
-----------------------------------------------
## 🤷 Usage
🇧🇷 easy-to-use package to restore punctuation of portuguese texts.
**Below is a quick way to use the template.**
1. First, install the package.
```
pip install respunct
```
2. Sample python code.
``` python
from respunct import RestorePuncts
model = RestorePuncts()
model.restore_puncts("""
henrique foi no lago pescar com o pedro mais tarde foram para a casa do pedro fritar os peixes""")
# output:
# Henrique foi no lago pescar com o Pedro. Mais tarde, foram para a casa do Pedro fritar os peixes.
```
-----------------------------------------------
## 🎯 Accuracy
| label | precision | recall | f1-score | support|
| ------------------------- | -------------|-------- | ----------|--------|
| **Upper - OU** | 0.89 | 0.91 | 0.90 | 69376
| **None - OO** | 0.99 | 0.98 | 0.98 | 857659
| **Full stop/period - .O** | 0.86 | 0.93 | 0.89 | 60410
| **Comma - ,O** | 0.85 | 0.83 | 0.84 | 48608
| **Upper + Comma - ,U** | 0.73 | 0.76 | 0.75 | 3521
| **Question - ?O** | 0.68 | 0.78 | 0.73 | 1168
| **Upper + period - .U** | 0.66 | 0.72 | 0.69 | 1884
| **Upper + colon - :U** | 0.59 | 0.63 | 0.61 | 352
| **Colon - :O** | 0.70 | 0.53 | 0.60 | 2420
| **Question Mark - ?U** | 0.50 | 0.56 | 0.53 | 36
| **Upper + Exclam. - !U** | 0.38 | 0.32 | 0.34 | 38
| **Exclamation Mark - !O** | 0.30 | 0.05 | 0.08 | 783
| **Semicolon - ;O** | 0.35 | 0.04 | 0.08 | 1557
| **Apostrophe - 'O** | 0.00 | 0.00 | 0.00 | 3
| **Hyphen - -O** | 0.00 | 0.00 | 0.00 | 3
| | | | |
| **accuracy** | | | 0.96 | 1047818
| **macro avg** | 0.57 | 0.54 | 0.54 | 1047818
| **weighted avg** | 0.96 | 0.96 | 0.96 | 1047818
-----------------------------------------------
## 🤙 Contact
[Maicon Domingues]([email protected]) for questions, feedback and/or requests for similar models.
|
AdapterHub/bert-base-uncased-pf-sst2 | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/sst-2"
] | text-classification | {
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} | 7 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_no-pretraining_s34
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-stsb | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sts/sts-b"
] | text-classification | {
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} | 3 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wavlm_s51
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-swag | [
"bert",
"en",
"dataset:swag",
"arxiv:2104.08247",
"adapter-transformers"
] | null | {
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} | 0 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wavlm_s691
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-ud_deprel | [
"bert",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:deprel/ud_ewt"
] | token-classification | {
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}
} | 61 | null | ---
tags:
- conversational
---
# Chatbot Stacey
Made for **LGBTQ+ Spacey**'s Bot on [Discord](https://discord.com/invite/jt4PWme44X).
[](https://github.com/ashtrindade/spacey-website-articles-api/blob/main/LICENSE.md)
---
## License
MIT License
Copyright (c) 2022 Ash Trindade
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
AdapterHub/bert-base-uncased-pf-ud_pos | [
"bert",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:pos/ud_ewt"
] | token-classification | {
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}
} | 1 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-ml_s324
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-wic | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:wordsence/wic"
] | text-classification | {
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}
} | 0 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-ml_s808
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-wikihop | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/wikihop"
] | question-answering | {
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}
} | 4 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 596.50 +/- 113.18
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 quanxi -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 quanxi
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AdapterHub/bert-base-uncased-pf-wnut_17 | [
"bert",
"en",
"dataset:wnut_17",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification"
] | token-classification | {
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}
} | 6 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-ml_s610
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bert-base-uncased-pf-yelp_polarity | [
"bert",
"en",
"dataset:yelp_polarity",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] | text-classification | {
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}
} | 2 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-fr_s675
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/bioASQyesno | [
"bart",
"adapter-transformers",
"adapterhub:qa/bioasq"
] | null | {
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"model_type": "bart",
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} | 10 | null | ---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/twitter-roberta-base-dec2020-tweetner7-random
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweetner7
type: tner/tweetner7
args: tner/tweetner7
metrics:
- name: F1 (test_2021)
type: f1
value: 0.647180429539451
- name: Precision (test_2021)
type: precision
value: 0.6428245493953912
- name: Recall (test_2021)
type: recall
value: 0.651595744680851
- name: Macro F1 (test_2021)
type: f1_macro
value: 0.599724784991918
- name: Macro Precision (test_2021)
type: precision_macro
value: 0.5927116702269455
- name: Macro Recall (test_2021)
type: recall_macro
value: 0.6075992592680901
- name: Entity Span F1 (test_2021)
type: f1_entity_span
value: 0.7828633779360248
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7775496235455168
- name: Entity Span Recall (test_2021)
type: recall_entity_span
value: 0.7882502602058518
- name: F1 (test_2020)
type: f1
value: 0.6469310157523085
- name: Precision (test_2020)
type: precision
value: 0.6786324786324787
- name: Recall (test_2020)
type: recall
value: 0.6180591593149974
- name: Macro F1 (test_2020)
type: f1_macro
value: 0.6053228739595288
- name: Macro Precision (test_2020)
type: precision_macro
value: 0.6353958642029116
- name: Macro Recall (test_2020)
type: recall_macro
value: 0.5799081543030431
- name: Entity Span F1 (test_2020)
type: f1_entity_span
value: 0.7593699076588811
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7965811965811965
- name: Entity Span Recall (test_2020)
type: recall_entity_span
value: 0.7254800207576544
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/twitter-roberta-base-dec2020-tweetner7-random
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_random` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.647180429539451
- Precision (micro): 0.6428245493953912
- Recall (micro): 0.651595744680851
- F1 (macro): 0.599724784991918
- Precision (macro): 0.5927116702269455
- Recall (macro): 0.6075992592680901
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.49081803005008345
- creative_work: 0.4642392717815344
- event: 0.4564920273348519
- group: 0.6168039538714991
- location: 0.6750496360026472
- person: 0.8331479421579534
- product: 0.661522633744856
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.638863290259823, 0.6571232166113093]
- 95%: [0.6369059711235887, 0.6583232503306811]
- F1 (macro):
- 90%: [0.638863290259823, 0.6571232166113093]
- 95%: [0.6369059711235887, 0.6583232503306811]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-random/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-random/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip.
```shell
pip install tner
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
```python
import re
from urlextract import URLExtract
from tner import TransformersNER
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/twitter-roberta-base-dec2020-tweetner7-random")
model.predict([text_format])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/tweetner7']
- dataset_split: train_random
- dataset_name: None
- local_dataset: None
- model: cardiffnlp/twitter-roberta-base-dec2020
- crf: True
- max_length: 128
- epoch: 30
- batch_size: 32
- lr: 1e-05
- random_seed: 0
- gradient_accumulation_steps: 1
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.15
- max_grad_norm: 1
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-random/raw/main/trainer_config.json).
### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
- TweetNER7
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
AdapterHub/narrativeqa | [
"bart",
"dataset:narrativeqa",
"adapter-transformers",
"adapterhub:qa/narrativeqa"
] | null | {
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"model_type": "bart",
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},
"translation_en_to_ro": {
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}
}
} | 23 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-fr_s485
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/roberta-base-pf-anli_r3 | [
"roberta",
"en",
"dataset:anli",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] | text-classification | {
"architectures": null,
"model_type": "roberta",
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-fr_s752
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/roberta-base-pf-art | [
"roberta",
"en",
"dataset:art",
"arxiv:2104.08247",
"adapter-transformers"
] | null | {
"architectures": null,
"model_type": "roberta",
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},
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"translation_en_to_de": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-es_s454
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdapterHub/roberta-base-pf-commonsense_qa | [
"roberta",
"en",
"dataset:commonsense_qa",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/csqa"
] | null | {
"architectures": null,
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 20 | null | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-es_s291
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
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