readme: ---
  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
  ---
  
  <!-- 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. -->
  
  # punct_restore_fr
  
  This model is a fine-tuned version of [camembert-base](https://huggingface.co/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
  