punct_restore_fr / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model_index:
  - name: punct_restore_fr
    results:
      - task:
          name: Token Classification
          type: token-classification
        metric:
          name: Accuracy
          type: accuracy
          value: 0.991500810518732

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