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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model_index: |
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- name: punct_restore_fr |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.991500810518732 |
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base_model: camembert-base |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# punct_restore_fr |
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This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on a raw, French opensubtitles dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0301 |
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- Precision: 0.9601 |
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- Recall: 0.9527 |
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- F1: 0.9564 |
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- Accuracy: 0.9915 |
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## Model description |
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Classifies tokens based on beginning of French sentences (B-SENT) and everything else (O). |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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1 million Open Subtitles (French) sentences. 80%/10%/10% training/validation/test split. |
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The sentences: |
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- were lower-cased |
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- had end punctuation (.?!) removed |
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- were of length between 7 and 70 words |
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- had beginning word of sentence tagged with B-SENT. |
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- All other words marked with O. |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 1 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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### Framework versions |
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- Transformers 4.8.1 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.8.0 |
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- Tokenizers 0.10.3 |
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