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--- |
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base_model: monologg/biobert_v1.1_pubmed |
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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: NLP-HIBA_DisTEMIST_fine_tuned_biobert |
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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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# NLP-HIBA_DisTEMIST_fine_tuned_biobert |
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This model is a fine-tuned version of [monologg/biobert_v1.1_pubmed](https://huggingface.co/monologg/biobert_v1.1_pubmed) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2325 |
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- Precision: 0.5394 |
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- Recall: 0.5010 |
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- F1: 0.5195 |
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- Accuracy: 0.9434 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | 71 | 0.2200 | 0.2689 | 0.2813 | 0.2750 | 0.9147 | |
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| No log | 2.0 | 142 | 0.1895 | 0.4126 | 0.3834 | 0.3975 | 0.9308 | |
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| No log | 3.0 | 213 | 0.1813 | 0.5319 | 0.3667 | 0.4341 | 0.9389 | |
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| No log | 4.0 | 284 | 0.1746 | 0.5085 | 0.4395 | 0.4715 | 0.9410 | |
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| No log | 5.0 | 355 | 0.1942 | 0.4868 | 0.4697 | 0.4781 | 0.9382 | |
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| No log | 6.0 | 426 | 0.1975 | 0.5246 | 0.4860 | 0.5046 | 0.9413 | |
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| No log | 7.0 | 497 | 0.2066 | 0.5207 | 0.4897 | 0.5047 | 0.9415 | |
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| 0.122 | 8.0 | 568 | 0.2201 | 0.5232 | 0.4956 | 0.5090 | 0.9420 | |
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| 0.122 | 9.0 | 639 | 0.2358 | 0.5178 | 0.5048 | 0.5112 | 0.9418 | |
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| 0.122 | 10.0 | 710 | 0.2325 | 0.5394 | 0.5010 | 0.5195 | 0.9434 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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