punct_restore_fr
This model is a fine-tuned version of camembert-base on a raw, French opensubtitles dataset. It achieves the following results on the evaluation set:
- Loss: 0.0301
- Precision: 0.9601
- Recall: 0.9527
- F1: 0.9564
- Accuracy: 0.9915
Model description
Classifies tokens based on beginning of French sentences (B-SENT) and everything else (O).
Intended uses & limitations
This model aims to help punctuation restoration on French YouTube auto-generated subtitles. In doing so, one can measure more in a corpus such as words per sentence, grammar structures per sentence, etc.
Training and evaluation data
1 million Open Subtitles (French) sentences. 80%/10%/10% training/validation/test split.
The sentences:
- were lower-cased
- had end punctuation (.?!) removed
- were of length between 7 and 70 words
- had beginning word of sentence tagged with B-SENT.
- All other words marked with O.
Token/tag pairs batched together in groups of 64. This helps show variety of positions for B-SENT and O tags. This also keeps training examples from just being one sentence. Otherwise, this leads to having the first word and only the first word in a sequence being labeled B-SENT.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.8.1
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3