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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: BERT_BIOMAT_NER_1000 |
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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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# BERT_BIOMAT_NER_1000 |
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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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3920 |
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- Precision: 0.9495 |
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- Recall: 0.9444 |
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- F1: 0.9470 |
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- Accuracy: 0.9380 |
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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: 32 |
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- eval_batch_size: 32 |
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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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| No log | 1.0 | 211 | 0.2669 | 0.9407 | 0.9352 | 0.9380 | 0.9275 | |
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| No log | 2.0 | 422 | 0.2809 | 0.9457 | 0.9432 | 0.9444 | 0.9343 | |
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| 0.2064 | 3.0 | 633 | 0.3114 | 0.9472 | 0.9454 | 0.9463 | 0.9353 | |
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| 0.2064 | 4.0 | 844 | 0.3323 | 0.9491 | 0.9422 | 0.9456 | 0.9358 | |
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| 0.0481 | 5.0 | 1055 | 0.3478 | 0.9493 | 0.9441 | 0.9467 | 0.9382 | |
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| 0.0481 | 6.0 | 1266 | 0.3731 | 0.9486 | 0.9438 | 0.9462 | 0.9374 | |
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| 0.0481 | 7.0 | 1477 | 0.3723 | 0.9491 | 0.9445 | 0.9468 | 0.9379 | |
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| 0.0201 | 8.0 | 1688 | 0.3830 | 0.9489 | 0.9443 | 0.9466 | 0.9369 | |
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| 0.0201 | 9.0 | 1899 | 0.3873 | 0.9503 | 0.9448 | 0.9475 | 0.9378 | |
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| 0.0106 | 10.0 | 2110 | 0.3920 | 0.9495 | 0.9444 | 0.9470 | 0.9380 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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