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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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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: nlpcw_bert-base-uncased-abbr |
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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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# nlpcw_bert-base-uncased-abbr |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2675 |
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- Precision: 0.9390 |
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- Recall: 0.9349 |
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- F1: 0.9369 |
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- Accuracy: 0.9317 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.6325 | 1.0 | 67 | 0.2629 | 0.9036 | 0.9090 | 0.9063 | 0.9043 | |
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| 0.3169 | 2.0 | 134 | 0.2297 | 0.9309 | 0.9137 | 0.9223 | 0.9182 | |
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| 0.1994 | 3.0 | 201 | 0.2282 | 0.9310 | 0.9193 | 0.9251 | 0.9223 | |
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| 0.17 | 4.0 | 268 | 0.2193 | 0.9366 | 0.9286 | 0.9326 | 0.9278 | |
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| 0.1457 | 5.0 | 335 | 0.2350 | 0.9395 | 0.9373 | 0.9384 | 0.9331 | |
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| 0.1086 | 6.0 | 402 | 0.2435 | 0.9418 | 0.9340 | 0.9379 | 0.9331 | |
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| 0.0908 | 7.0 | 469 | 0.2537 | 0.9357 | 0.9283 | 0.9319 | 0.9270 | |
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| 0.0791 | 8.0 | 536 | 0.2675 | 0.9390 | 0.9349 | 0.9369 | 0.9317 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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