modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
jonatasgrosman/exp_w2v2t_ja_vp-it_s73
|
jonatasgrosman
| 2022-07-08T18:24:40Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T18:24:16Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-it_s73
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_r-wav2vec2_s303
|
jonatasgrosman
| 2022-07-08T18:20:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T18:20:08Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_r-wav2vec2_s303
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_xls-r_s941
|
jonatasgrosman
| 2022-07-08T18:09:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T18:08:47Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_xls-r_s941
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 (ja)](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.
|
infinitejoy/ppo-SpaceInvadersNoFrameskip-v4
|
infinitejoy
| 2022-07-08T17:57:39Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-08T17:57:06Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 688.00 +/- 388.59
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 infinitejoy -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 infinitejoy
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
jonatasgrosman/exp_w2v2t_ja_unispeech-sat_s946
|
jonatasgrosman
| 2022-07-08T17:54:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:54:22Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_unispeech-sat_s946
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_unispeech-sat_s884
|
jonatasgrosman
| 2022-07-08T17:51:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:51:10Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_unispeech-sat_s884
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_vp-nl_s770
|
jonatasgrosman
| 2022-07-08T17:48:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:47:43Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-nl_s770
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_vp-nl_s287
|
jonatasgrosman
| 2022-07-08T17:44:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:43:51Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-nl_s287
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_vp-nl_s682
|
jonatasgrosman
| 2022-07-08T17:41:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:40:45Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-nl_s682
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_vp-es_s350
|
jonatasgrosman
| 2022-07-08T17:37:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:36:55Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-es_s350
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 (ja)](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.
|
thunninoi/wav2vec2-japanese-vtuber
|
thunninoi
| 2022-07-08T17:33:48Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-27T10:47:53Z |
---
tags:
- generated_from_trainer
model-index:
- name: checkpoints2
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. -->
# checkpoints2
This model is a fine-tuned version of [ttop324/wav2vec2-live-japanese](https://huggingface.co/ttop324/wav2vec2-live-japanese) on the extracted and cleaned transcript of [Holo No Graffiti](https://youtube.com/playlist?list=PLS51cvjOMUKwKtxe_IxhbBBvQ9XpiL1W_)
dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Evaluated from [common_voice | train | ja ](https://huggingface.co/datasets/common_voice/viewer/ja/test):
- WER: 32.940524
- CER: 15.251746
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 3
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_ja_vp-es_s381
|
jonatasgrosman
| 2022-07-08T17:29:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:29:31Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-es_s381
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_vp-fr_s543
|
jonatasgrosman
| 2022-07-08T17:26:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:26:03Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-fr_s543
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_vp-fr_s368
|
jonatasgrosman
| 2022-07-08T17:18:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:17:49Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-fr_s368
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s295
|
jonatasgrosman
| 2022-07-08T17:10:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:09:55Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_unispeech-ml_s295
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_wavlm_s664
|
jonatasgrosman
| 2022-07-08T17:03:01Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T17:02:32Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_wavlm_s664
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (ja)](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.
|
Guillaume63/Pong
|
Guillaume63
| 2022-07-08T17:01:31Z | 0 | 0 | null |
[
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-08T17:01:19Z |
---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pong
results:
- metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-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
|
jonatasgrosman/exp_w2v2t_ja_wavlm_s35
|
jonatasgrosman
| 2022-07-08T16:59:51Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:59:26Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_wavlm_s35
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_no-pretraining_s830
|
jonatasgrosman
| 2022-07-08T16:46:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:45:48Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_no-pretraining_s830
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_vp-sv_s570
|
jonatasgrosman
| 2022-07-08T16:43:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:42:50Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-sv_s570
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 (ja)](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.
|
Nitika/distilbert-base-uncased-finetuned-squad-d5716d28
|
Nitika
| 2022-07-08T16:36:38Z | 0 | 0 | null |
[
"pytorch",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"region:us"
] |
question-answering
| 2022-07-08T16:36:27Z |
---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
## Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
```python
from datasets import load_dataset
squad = load_dataset('squad')
```
## Training procedure
## Eval results
| | Exact Match | F1 |
|------------------|-------------|------|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the `squad` metric from `datasets`.
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
jonatasgrosman/exp_w2v2t_ja_hubert_s334
|
jonatasgrosman
| 2022-07-08T16:31:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:31:29Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_hubert_s334
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 (ja)](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.
|
Forkits/Reinforce-Pixelcopter
|
Forkits
| 2022-07-08T16:29:09Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-08T15:34:36Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter
results:
- metrics:
- type: mean_reward
value: 10.60 +/- 6.80
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
|
jonatasgrosman/exp_w2v2t_ja_hubert_s69
|
jonatasgrosman
| 2022-07-08T16:27:39Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:27:15Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_hubert_s69
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 (ja)](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.
|
Rocketknight1/europython-imdb-distilbert
|
Rocketknight1
| 2022-07-08T16:21:24Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-08T16:20:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: europython-imdb-distilbert
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# europython-imdb-distilbert
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3081
- Train Accuracy: 0.8663
- Validation Loss: 0.2459
- Validation Accuracy: 0.9006
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3081 | 0.8663 | 0.2459 | 0.9006 | 0 |
### Framework versions
- Transformers 4.21.0.dev0
- TensorFlow 2.9.1
- Datasets 2.3.3.dev0
- Tokenizers 0.11.0
|
jonatasgrosman/exp_w2v2t_ja_unispeech_s253
|
jonatasgrosman
| 2022-07-08T16:18:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:17:38Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_unispeech_s253
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_unispeech_s569
|
jonatasgrosman
| 2022-07-08T16:14:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:14:24Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_unispeech_s569
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_xlsr-53_s705
|
jonatasgrosman
| 2022-07-08T16:11:36Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:11:13Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_xlsr-53_s705
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_vp-100k_s255
|
jonatasgrosman
| 2022-07-08T16:02:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T16:02:13Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_vp-100k_s255
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 (ja)](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.
|
jonatasgrosman/exp_w2v2t_ja_wav2vec2_s727
|
jonatasgrosman
| 2022-07-08T15:53:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T15:52:34Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ja_wav2vec2_s727
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 (ja)](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.
|
Rocketknight1/bert-dummy-seq
|
Rocketknight1
| 2022-07-08T15:45:02Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-08T15:18:33Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-dummy-seq
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-dummy-seq
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.21.0.dev0
- TensorFlow 2.9.1
- Datasets 2.3.3.dev0
- Tokenizers 0.11.0
|
jonatasgrosman/exp_w2v2t_th_vp-it_s259
|
jonatasgrosman
| 2022-07-08T15:28:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T15:28:09Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_vp-it_s259
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 (th)](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.
|
jonatasgrosman/exp_w2v2t_th_r-wav2vec2_s930
|
jonatasgrosman
| 2022-07-08T15:22:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T15:21:42Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_r-wav2vec2_s930
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 (th)](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.
|
tfshaman/distilbert-base-uncased-distilled-clinc
|
tfshaman
| 2022-07-08T15:19:17Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-08T14:52:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.8264516129032258
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5565
- Accuracy: 0.8265
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2743 | 1.0 | 318 | 2.5809 | 0.7310 |
| 2.2148 | 2.0 | 636 | 1.7909 | 0.8071 |
| 1.7065 | 3.0 | 954 | 1.5565 | 0.8265 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_th_r-wav2vec2_s805
|
jonatasgrosman
| 2022-07-08T15:18:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T15:17:48Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_r-wav2vec2_s805
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 (th)](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.
|
jonatasgrosman/exp_w2v2t_th_xls-r_s625
|
jonatasgrosman
| 2022-07-08T15:07:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T15:07:26Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_xls-r_s625
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 (th)](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.
|
jonatasgrosman/exp_w2v2t_th_unispeech-sat_s772
|
jonatasgrosman
| 2022-07-08T15:04:41Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T15:03:49Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech-sat_s772
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 (th)](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.
|
jonatasgrosman/exp_w2v2t_th_unispeech-sat_s515
|
jonatasgrosman
| 2022-07-08T15:01:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T15:00:21Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech-sat_s515
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 (th)](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.
|
dminiotas05/distilbert-base-uncased-finetuned-ft650_10class
|
dminiotas05
| 2022-07-08T14:58:07Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-08T14:33:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft650_10class
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft650_10class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9674
- Accuracy: 0.2207
- F1: 0.2002
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.1088 | 1.0 | 188 | 2.0460 | 0.1807 | 0.1324 |
| 1.9628 | 2.0 | 376 | 1.9867 | 0.2173 | 0.1821 |
| 1.8966 | 3.0 | 564 | 1.9693 | 0.2193 | 0.1936 |
| 1.8399 | 4.0 | 752 | 1.9674 | 0.2207 | 0.2002 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_th_unispeech-sat_s658
|
jonatasgrosman
| 2022-07-08T14:57:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T14:56:29Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech-sat_s658
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 (th)](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.
|
jonatasgrosman/exp_w2v2t_th_vp-nl_s253
|
jonatasgrosman
| 2022-07-08T14:49:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T14:49:10Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_vp-nl_s253
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 (th)](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.
|
hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100
|
hsohn3
| 2022-07-08T14:49:05Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-07T18:58:20Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8536
- Epoch: 99
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 3.9508 | 0 |
| 3.4063 | 1 |
| 3.3682 | 2 |
| 3.3468 | 3 |
| 3.3330 | 4 |
| 3.3308 | 5 |
| 3.3225 | 6 |
| 3.3106 | 7 |
| 3.2518 | 8 |
| 3.1859 | 9 |
| 3.1373 | 10 |
| 3.0923 | 11 |
| 3.0390 | 12 |
| 2.9560 | 13 |
| 2.8605 | 14 |
| 2.7564 | 15 |
| 2.4969 | 16 |
| 2.2044 | 17 |
| 1.9566 | 18 |
| 1.7686 | 19 |
| 1.5995 | 20 |
| 1.4932 | 21 |
| 1.4100 | 22 |
| 1.3538 | 23 |
| 1.2973 | 24 |
| 1.2610 | 25 |
| 1.2160 | 26 |
| 1.1916 | 27 |
| 1.1607 | 28 |
| 1.1468 | 29 |
| 1.1262 | 30 |
| 1.1123 | 31 |
| 1.0942 | 32 |
| 1.0816 | 33 |
| 1.0717 | 34 |
| 1.0575 | 35 |
| 1.0503 | 36 |
| 1.0411 | 37 |
| 1.0293 | 38 |
| 1.0229 | 39 |
| 1.0139 | 40 |
| 1.0081 | 41 |
| 1.0028 | 42 |
| 0.9967 | 43 |
| 0.9906 | 44 |
| 0.9834 | 45 |
| 0.9782 | 46 |
| 0.9766 | 47 |
| 0.9676 | 48 |
| 0.9618 | 49 |
| 0.9611 | 50 |
| 0.9553 | 51 |
| 0.9504 | 52 |
| 0.9483 | 53 |
| 0.9404 | 54 |
| 0.9423 | 55 |
| 0.9361 | 56 |
| 0.9327 | 57 |
| 0.9327 | 58 |
| 0.9263 | 59 |
| 0.9275 | 60 |
| 0.9218 | 61 |
| 0.9202 | 62 |
| 0.9158 | 63 |
| 0.9152 | 64 |
| 0.9091 | 65 |
| 0.9104 | 66 |
| 0.9094 | 67 |
| 0.9087 | 68 |
| 0.9034 | 69 |
| 0.9063 | 70 |
| 0.8984 | 71 |
| 0.8966 | 72 |
| 0.8953 | 73 |
| 0.8910 | 74 |
| 0.8913 | 75 |
| 0.8887 | 76 |
| 0.8868 | 77 |
| 0.8868 | 78 |
| 0.8815 | 79 |
| 0.8821 | 80 |
| 0.8791 | 81 |
| 0.8752 | 82 |
| 0.8731 | 83 |
| 0.8779 | 84 |
| 0.8727 | 85 |
| 0.8702 | 86 |
| 0.8712 | 87 |
| 0.8689 | 88 |
| 0.8646 | 89 |
| 0.8644 | 90 |
| 0.8608 | 91 |
| 0.8643 | 92 |
| 0.8602 | 93 |
| 0.8605 | 94 |
| 0.8568 | 95 |
| 0.8567 | 96 |
| 0.8557 | 97 |
| 0.8543 | 98 |
| 0.8536 | 99 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Rocketknight1/europython-imdb
|
Rocketknight1
| 2022-07-08T14:42:10Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"deberta-v2",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-07T16:56:10Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: europython-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# europython-imdb
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1279
- Train Accuracy: 0.9548
- Validation Loss: 0.1595
- Validation Accuracy: 0.9418
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2073 | 0.9203 | 0.1486 | 0.9435 | 0 |
| 0.1279 | 0.9548 | 0.1595 | 0.9418 | 1 |
### Framework versions
- Transformers 4.21.0.dev0
- TensorFlow 2.9.1
- Datasets 2.3.3.dev0
- Tokenizers 0.11.0
|
Guillaume63/Reinforce-helicopter
|
Guillaume63
| 2022-07-08T14:41:44Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-08T14:41:33Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-helicopter
results:
- metrics:
- type: mean_reward
value: 15.60 +/- 16.11
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
|
jonatasgrosman/exp_w2v2t_th_vp-es_s552
|
jonatasgrosman
| 2022-07-08T14:35:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T14:35:02Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_vp-es_s552
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 (th)](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.
|
jonatasgrosman/exp_w2v2t_th_vp-es_s51
|
jonatasgrosman
| 2022-07-08T14:10:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T14:10:08Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_vp-es_s51
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 (th)](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.
|
malteos/gpt2-xl-german-covid-19
|
malteos
| 2022-07-08T13:48:32Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-08T13:14:23Z |
---
license: mit
language: de
widget:
- text: "Noch Wochen nach einer Erkrankung an COVID-19 können "
---
# German Covid-19 GPT2-XL (1.5B)
- Covid-19 specific version of [`malteos/gpt2-xl-wechsel-german`](https://huggingface.co/malteos/gpt2-xl-wechsel-german)
- Fine-tuned on 2 GB text from OSCAR filtered for covid related terms.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='malteos/gpt2-xl-german-covid-19')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
```
## License
MIT
|
akraut/dummy_bin_image_clf
|
akraut
| 2022-07-08T13:39:56Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-07-08T13:39:46Z |
---
library_name: keras
---
## 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:
| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision |
|----|-------------|-----|------|------|-------|-------|------------------|
|Adam|0.0010000000474974513|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
domenicrosati/deberta-v3-xsmall-with-biblio-context-frozenlm-finetuned-review_classifier
|
domenicrosati
| 2022-07-08T13:26:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-08T12:06:35Z |
---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: deberta-v3-xsmall-with-biblio-context-frozenlm-finetuned-review_classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-xsmall-with-biblio-context-frozenlm-finetuned-review_classifier
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3109
- Accuracy: 0.9066
- F1: 0.0090
- Recall: 0.0045
- Precision: 0.8293
## 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: 4.5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.2938 | 1.0 | 6667 | 0.3103 | 0.9070 | 0.0221 | 0.0112 | 0.7636 |
| 0.2851 | 2.0 | 13334 | 0.3109 | 0.9066 | 0.0090 | 0.0045 | 0.8293 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100
|
hsohn3
| 2022-07-08T13:10:14Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-07T18:42:58Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8009
- Epoch: 99
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 3.8699 | 0 |
| 3.1667 | 1 |
| 3.1286 | 2 |
| 3.1169 | 3 |
| 3.1077 | 4 |
| 3.0989 | 5 |
| 3.0911 | 6 |
| 3.0896 | 7 |
| 3.0820 | 8 |
| 3.0856 | 9 |
| 3.0827 | 10 |
| 3.0800 | 11 |
| 3.0647 | 12 |
| 3.0396 | 13 |
| 3.0052 | 14 |
| 2.9879 | 15 |
| 2.9633 | 16 |
| 2.9449 | 17 |
| 2.9217 | 18 |
| 2.8921 | 19 |
| 2.8625 | 20 |
| 2.8153 | 21 |
| 2.7495 | 22 |
| 2.6202 | 23 |
| 2.3762 | 24 |
| 2.1064 | 25 |
| 1.8489 | 26 |
| 1.6556 | 27 |
| 1.5005 | 28 |
| 1.4110 | 29 |
| 1.3472 | 30 |
| 1.2896 | 31 |
| 1.2391 | 32 |
| 1.2001 | 33 |
| 1.1663 | 34 |
| 1.1418 | 35 |
| 1.1159 | 36 |
| 1.0987 | 37 |
| 1.0753 | 38 |
| 1.0608 | 39 |
| 1.0456 | 40 |
| 1.0381 | 41 |
| 1.0248 | 42 |
| 1.0127 | 43 |
| 0.9970 | 44 |
| 0.9958 | 45 |
| 0.9847 | 46 |
| 0.9789 | 47 |
| 0.9617 | 48 |
| 0.9575 | 49 |
| 0.9517 | 50 |
| 0.9442 | 51 |
| 0.9379 | 52 |
| 0.9350 | 53 |
| 0.9325 | 54 |
| 0.9235 | 55 |
| 0.9182 | 56 |
| 0.9139 | 57 |
| 0.9074 | 58 |
| 0.8984 | 59 |
| 0.8988 | 60 |
| 0.8958 | 61 |
| 0.8937 | 62 |
| 0.8853 | 63 |
| 0.8812 | 64 |
| 0.8758 | 65 |
| 0.8729 | 66 |
| 0.8732 | 67 |
| 0.8647 | 68 |
| 0.8634 | 69 |
| 0.8604 | 70 |
| 0.8577 | 71 |
| 0.8597 | 72 |
| 0.8508 | 73 |
| 0.8510 | 74 |
| 0.8450 | 75 |
| 0.8451 | 76 |
| 0.8398 | 77 |
| 0.8392 | 78 |
| 0.8345 | 79 |
| 0.8350 | 80 |
| 0.8329 | 81 |
| 0.8299 | 82 |
| 0.8257 | 83 |
| 0.8217 | 84 |
| 0.8192 | 85 |
| 0.8211 | 86 |
| 0.8208 | 87 |
| 0.8171 | 88 |
| 0.8166 | 89 |
| 0.8134 | 90 |
| 0.8124 | 91 |
| 0.8102 | 92 |
| 0.8133 | 93 |
| 0.8066 | 94 |
| 0.8023 | 95 |
| 0.8049 | 96 |
| 0.8024 | 97 |
| 0.7980 | 98 |
| 0.8009 | 99 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Guillaume63/Reinforce-cartpole
|
Guillaume63
| 2022-07-08T13:06:18Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-06T12:59:13Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- metrics:
- type: mean_reward
value: 500.00 +/- 0.00
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
|
swtx/simcse-chinese-roberta-www-ext
|
swtx
| 2022-07-08T12:12:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08821",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-07-08T09:13:31Z |
## swtx SIMCSE RoBERTa WWM Ext Chinese
This model provides simplified Chinese sentence embeddings encoding based on [Simple Contrastive Learning](https://arxiv.org/abs/2104.08821).
The pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.
## How to use
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("swtx/simcse-chinese-roberta-wwm-ext")
model = AutoModel.from_pretrained("swtx/simcse-chinese-roberta-wwm-ext")
```
|
dminiotas05/distilbert-base-uncased-finetuned-ft650_6class
|
dminiotas05
| 2022-07-08T12:11:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-08T11:46:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft650_6class
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft650_6class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4555
- Accuracy: 0.3707
- F1: 0.3625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5838 | 1.0 | 188 | 1.5235 | 0.3253 | 0.2947 |
| 1.4521 | 2.0 | 376 | 1.4744 | 0.3467 | 0.3234 |
| 1.3838 | 3.0 | 564 | 1.4565 | 0.358 | 0.3483 |
| 1.323 | 4.0 | 752 | 1.4555 | 0.3707 | 0.3625 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_th_vp-fr_s77
|
jonatasgrosman
| 2022-07-08T11:40:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T11:40:05Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_vp-fr_s77
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_unispeech-ml_s351
|
jonatasgrosman
| 2022-07-08T11:34:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T11:33:47Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech-ml_s351
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_wavlm_s847
|
jonatasgrosman
| 2022-07-08T11:21:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T11:20:15Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_wavlm_s847
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_wavlm_s108
|
jonatasgrosman
| 2022-07-08T11:17:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T11:16:18Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_wavlm_s108
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_no-pretraining_s414
|
jonatasgrosman
| 2022-07-08T11:10:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T11:10:08Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_no-pretraining_s414
Fine-tuned randomly initialized wav2vec2 model for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_no-pretraining_s950
|
jonatasgrosman
| 2022-07-08T11:07:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T11:06:50Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_no-pretraining_s950
Fine-tuned randomly initialized wav2vec2 model for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_vp-sv_s884
|
jonatasgrosman
| 2022-07-08T11:04:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T11:03:49Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_vp-sv_s884
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_vp-sv_s635
|
jonatasgrosman
| 2022-07-08T11:01:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T11:00:49Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_vp-sv_s635
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_unispeech_s131
|
jonatasgrosman
| 2022-07-08T10:45:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T10:45:06Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech_s131
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_unispeech_s624
|
jonatasgrosman
| 2022-07-08T10:42:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T10:41:56Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech_s624
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_xlsr-53_s218
|
jonatasgrosman
| 2022-07-08T10:35:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T10:34:50Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_xlsr-53_s218
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_vp-100k_s630
|
jonatasgrosman
| 2022-07-08T10:24:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T10:23:54Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_vp-100k_s630
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_th_wav2vec2_s35
|
jonatasgrosman
| 2022-07-08T10:13:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T10:13:35Z |
---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_wav2vec2_s35
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on Thai using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-it_s250
|
jonatasgrosman
| 2022-07-08T10:03:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T10:02:46Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-it_s250
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
prashanth0205/vit_spectrogram
|
prashanth0205
| 2022-07-08T09:58:35Z | 34 | 4 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-06T13:17:32Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vit_spectrogram
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vit_spectrogram
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on a dataset containing
images of Mel spectrogram belonging to the classes 'Male' and 'Female'. This model is still being fine tuned and tested.
It achieves the following results on the evaluation set:
- Train Loss: 0.2893
- Train Accuracy: 0.8757
- Train Top-3-accuracy: 1.0000
- Validation Loss: 0.8757
- Validation Accuracy: 0.9366
- Validation Top-3-accuracy: 1.0
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3032, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.4.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
jonatasgrosman/exp_w2v2t_en_vp-it_s859
|
jonatasgrosman
| 2022-07-08T09:52:16Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T09:51:29Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-it_s859
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_r-wav2vec2_s44
|
jonatasgrosman
| 2022-07-08T09:36:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T09:35:33Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_r-wav2vec2_s44
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_r-wav2vec2_s93
|
jonatasgrosman
| 2022-07-08T09:28:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T09:28:09Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_r-wav2vec2_s93
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_xls-r_s732
|
jonatasgrosman
| 2022-07-08T09:02:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T09:02:05Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_xls-r_s732
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-nl_s980
|
jonatasgrosman
| 2022-07-08T08:17:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T08:16:42Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-nl_s980
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-nl_s281
|
jonatasgrosman
| 2022-07-08T08:09:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T08:08:43Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-nl_s281
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-es_s474
|
jonatasgrosman
| 2022-07-08T07:45:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:44:40Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-es_s474
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-es_s952
|
jonatasgrosman
| 2022-07-08T07:36:55Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:36:29Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-es_s952
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
ClassCat/roberta-base-french
|
ClassCat
| 2022-07-08T07:34:58Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"fr",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-04T17:58:21Z |
---
language: fr
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
widget:
- text: "Je vais à la <mask>."
- text: "J'aime le <mask>."
- text: "J'ai ouvert la <mask>."
- text: "Je m'appelle <mask>."
- text: "J'ai beaucoup d'<mask>."
---
## RoBERTa French base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* [wiki40b/fr](https://www.tensorflow.org/datasets/catalog/wiki40b#wiki40bfr) (French Wikipedia)
* Subset of [CC-100/fr](https://data.statmt.org/cc-100/) : Monolingual Datasets from Web Crawl Data
### Usage
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='ClassCat/roberta-base-french')
unmasker("Je vais à la <mask>.")
```
|
jonatasgrosman/exp_w2v2t_en_vp-fr_s51
|
jonatasgrosman
| 2022-07-08T07:29:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:28:38Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-fr_s51
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-fr_s118
|
jonatasgrosman
| 2022-07-08T07:12:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:12:00Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-fr_s118
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_unispeech-ml_s756
|
jonatasgrosman
| 2022-07-08T07:05:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:04:52Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech-ml_s756
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_wavlm_s461
|
jonatasgrosman
| 2022-07-08T06:40:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:39:25Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wavlm_s461
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_wavlm_s767
|
jonatasgrosman
| 2022-07-08T06:33:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:32:43Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wavlm_s767
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_no-pretraining_s289
|
jonatasgrosman
| 2022-07-08T06:21:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:21:09Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_no-pretraining_s289
Fine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-sv_s320
|
jonatasgrosman
| 2022-07-08T06:07:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:06:37Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-sv_s320
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-sv_s179
|
jonatasgrosman
| 2022-07-08T06:02:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:01:42Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-sv_s179
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
igpaub/Reinforce-Cartpole-v1
|
igpaub
| 2022-07-08T05:54:39Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-08T05:53:59Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- metrics:
- type: mean_reward
value: 500.00 +/- 0.00
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
|
jonatasgrosman/exp_w2v2t_en_hubert_s596
|
jonatasgrosman
| 2022-07-08T05:50:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:49:43Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_hubert_s596
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_hubert_s875
|
jonatasgrosman
| 2022-07-08T05:46:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:45:44Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_hubert_s875
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_unispeech_s227
|
jonatasgrosman
| 2022-07-08T05:36:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:35:18Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech_s227
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_unispeech_s870
|
jonatasgrosman
| 2022-07-08T05:31:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:30:42Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech_s870
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
phyous/q-Taxi-v3
|
phyous
| 2022-07-08T05:12:47Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-08T05:12:07Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.44 +/- 2.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="phyous/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
jonatasgrosman/exp_w2v2t_en_xlsr-53_s870
|
jonatasgrosman
| 2022-07-08T05:07:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:06:55Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_xlsr-53_s870
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_vp-100k_s807
|
jonatasgrosman
| 2022-07-08T04:33:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T04:32:40Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-100k_s807
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
jonatasgrosman/exp_w2v2t_en_wav2vec2_s878
|
jonatasgrosman
| 2022-07-08T03:56:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T03:23:38Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wav2vec2_s878
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on English using the train split of [Common Voice 7.0](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.
|
ccarvajal-reyes/beto-emoji
|
ccarvajal-reyes
| 2022-07-08T03:35:39Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-03T07:26:55Z |
---
language:
- es
---
# beto-emoji
Fine-tunning [BETO](https://github.com/dccuchile/beto) for emoji-prediction.
## Repository
Details with training and a use example are shown in [github.com/camilocarvajalreyes/beto-emoji](https://github.com/camilocarvajalreyes/beto-emoji). A deeper analysis of this and other models on the full dataset can be found in [github.com/furrutiav/data-mining-2022](https://github.com/furrutiav/data-mining-2022). We have used this model for a project for [CC5205 Data Mining](https://github.com/dccuchile/CC5205) course.
## Example
Inspired by model card from [cardiffnlp/twitter-roberta-base-emoji](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji).
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
MODEL = f"ccarvajal/beto-emoji"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# download label mapping
labels=[]
mapping_link = f"https://raw.githubusercontent.com/camilocarvajalreyes/beto-emoji/main/es_mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "que viva españa"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output
```python
1) 🇪🇸 0.2508
2) 😍 0.238
3) 👌 0.2225
4) 😂 0.0806
5) ❤ 0.0489
6) 😁 0.0415
7) 😜 0.0232
8) 😎 0.0229
9) 😊 0.0156
10) 😉 0.0119
11) 💜 0.0079
12) 💕 0.0077
13) 💪 0.0066
14) 💘 0.0054
15) 💙 0.0052
16) 💞 0.005
17) 😘 0.0034
18) 🎶 0.0022
19) ✨ 0.0007
```
## Results in test set
precision recall f1-score support
❤ 0.39 0.43 0.41 2141
😍 0.29 0.39 0.33 1408
😂 0.51 0.51 0.51 1499
💕 0.09 0.05 0.06 352
😊 0.12 0.23 0.16 514
😘 0.24 0.23 0.24 397
💪 0.37 0.43 0.40 307
😉 0.15 0.17 0.16 453
👌 0.09 0.16 0.11 180
🇪🇸 0.46 0.46 0.46 424
😎 0.12 0.11 0.11 339
💙 0.36 0.02 0.04 413
💜 0.00 0.00 0.00 235
😜 0.04 0.02 0.02 274
💞 0.00 0.00 0.00 93
✨ 0.26 0.12 0.17 416
🎶 0.25 0.24 0.24 212
💘 0.00 0.00 0.00 134
😁 0.05 0.03 0.04 209
accuracy 0.30 10000
macro_avg 0.20 0.19 0.18 10000
weighted avg 0.29 0.30 0.29 10000
[Another example](https://github.com/camilocarvajalreyes/beto-emoji/blob/main/attention_visualisation.ipynb) with a visualisation of the attention modules within this model is carried out using [bertviz](https://github.com/jessevig/bertviz).
## Reproducibility
The Multilingual Emoji Prediction dataset (Barbieri et al. 2010) consists of tweets in English and Spanish that originally had a single emoji, which is later used as a tag. Test and trial sets can be downloaded [here](https://github.com/fvancesco/Semeval2018-Task2-Emoji-Detection/blob/master/dataset/Semeval2018-Task2-EmojiPrediction.zip?raw=true), but the train set needs to be downloaded using a [twitter crawler](https://github.com/fra82/twitter-crawler/blob/master/semeval2018task2TwitterCrawlerHOWTO.md). The goal is to predict that single emoji that was originally in the tweet using the text in it (out of a fixed set of possible emojis, 20 for English and 19 for Spanish).
Training parameters:
```python
training_args = TrainingArguments(
output_dir="./results",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.01
)
```
|
eplatas/distilroberta-base-finetuned-wikitext2
|
eplatas
| 2022-07-08T01:58:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-08T01:52:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8359
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 87 | 1.9893 |
| No log | 2.0 | 174 | 1.9055 |
| No log | 3.0 | 261 | 1.8187 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sam34738/bert-hinglish
|
sam34738
| 2022-07-08T00:00:58Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-07T23:37:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-hinglish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-hinglish
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3557 | 1.0 | 460 | 0.7714 |
| 0.6349 | 2.0 | 920 | 0.5475 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
FelipeAD/mt5-small-finetuned-amazon-en-es
|
FelipeAD
| 2022-07-07T22:38:39Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-07T21:08:36Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: FelipeAD/mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# FelipeAD/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.0682
- Validation Loss: 3.3902
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.0232 | 4.5431 | 0 |
| 6.0233 | 3.9118 | 1 |
| 5.2216 | 3.6621 | 2 |
| 4.7560 | 3.5532 | 3 |
| 4.4685 | 3.4825 | 4 |
| 4.2748 | 3.4303 | 5 |
| 4.1432 | 3.4013 | 6 |
| 4.0682 | 3.3902 | 7 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
tfshaman/distilbert-base-uncased-finetuned-clinc
|
tfshaman
| 2022-07-07T22:15:13Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-07T21:36:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9158064516129032
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7786
- Accuracy: 0.9158
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2838 | 1.0 | 318 | 3.2787 | 0.7455 |
| 2.622 | 2.0 | 636 | 1.8706 | 0.8332 |
| 1.5466 | 3.0 | 954 | 1.1623 | 0.8939 |
| 1.0135 | 4.0 | 1272 | 0.8619 | 0.91 |
| 0.7985 | 5.0 | 1590 | 0.7786 | 0.9158 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/fairytale_bot23
|
huggingtweets
| 2022-07-07T21:44:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-07T21:43:08Z |
---
language: en
thumbnail: http://www.huggingtweets.com/fairytale_bot23/1657230245911/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1486954631464771591/cwgDTNXD_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Fairytale Generator</div>
<div style="text-align: center; font-size: 14px;">@fairytale_bot23</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Fairytale Generator.
| Data | Fairytale Generator |
| --- | --- |
| Tweets downloaded | 315 |
| Retweets | 0 |
| Short tweets | 0 |
| Tweets kept | 315 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lznwr8t9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @fairytale_bot23's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hjhfq1n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hjhfq1n/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/fairytale_bot23')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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