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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: recitation-segmenter-v2 |
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results: [] |
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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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# recitation-segmenter-v2 |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9958 |
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- F1: 0.9964 |
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- Loss: 0.0132 |
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- Precision: 0.9976 |
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- Recall: 0.9951 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 50 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: constant |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:------:|:----:|:--------:|:------:|:---------------:|:---------:|:------:| |
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| 0.0701 | 0.2507 | 275 | 0.9953 | 0.9959 | 0.0249 | 0.9947 | 0.9971 | |
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| 0.0234 | 0.5014 | 550 | 0.9953 | 0.9959 | 0.0185 | 0.9940 | 0.9977 | |
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| 0.0186 | 0.7521 | 825 | 0.9958 | 0.9964 | 0.0132 | 0.9976 | 0.9951 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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