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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: ner_model_ep_all |
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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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# ner_model_ep_all |
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3739 |
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- allergy Name F1: 0.7755 |
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- allergy Name Pres: 0.76 |
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- allergy Name Rec: 0.7917 |
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- cancer F1: 0.7389 |
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- cancer Pres: 0.7283 |
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- cancer Rec: 0.7497 |
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- chronic Disease F1: 0.7778 |
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- chronic Disease Pres: 0.7676 |
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- chronic Disease Rec: 0.7882 |
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- treatment F1: 0.7918 |
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- treatmen Prest: 0.7837 |
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- treatment Rec: 0.7999 |
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- Over All Precision: 0.7698 |
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- Over All Recall: 0.7887 |
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- Over All F1: 0.7792 |
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- Over All Accuracy: 0.8803 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | allergy Name F1 | allergy Name Pres | allergy Name Rec | cancer F1 | cancer Pres | cancer Rec | chronic Disease F1 | chronic Disease Pres | chronic Disease Rec | treatment F1 | treatmen Prest | treatment Rec | Over All Precision | Over All Recall | Over All F1 | Over All Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:| |
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| 0.5174 | 1.0 | 1005 | 0.3949 | 0.7230 | 0.7710 | 0.6806 | 0.6254 | 0.6354 | 0.6158 | 0.6958 | 0.6914 | 0.7003 | 0.7376 | 0.7683 | 0.7093 | 0.7218 | 0.6925 | 0.7068 | 0.8570 | |
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| 0.3297 | 2.0 | 2010 | 0.3664 | 0.7746 | 0.7857 | 0.7639 | 0.7133 | 0.7171 | 0.7095 | 0.7509 | 0.7746 | 0.7287 | 0.7738 | 0.7834 | 0.7643 | 0.7711 | 0.7444 | 0.7576 | 0.8732 | |
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| 0.2691 | 3.0 | 3015 | 0.3585 | 0.7589 | 0.8364 | 0.6944 | 0.7415 | 0.7417 | 0.7412 | 0.7674 | 0.7754 | 0.7596 | 0.7819 | 0.7652 | 0.7994 | 0.7670 | 0.7748 | 0.7709 | 0.8780 | |
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| 0.2278 | 4.0 | 4020 | 0.3686 | 0.7717 | 0.7878 | 0.7562 | 0.7400 | 0.7170 | 0.7645 | 0.7762 | 0.7717 | 0.7807 | 0.7885 | 0.7604 | 0.8188 | 0.7588 | 0.7965 | 0.7772 | 0.8795 | |
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| 0.2038 | 5.0 | 5025 | 0.3739 | 0.7755 | 0.76 | 0.7917 | 0.7389 | 0.7283 | 0.7497 | 0.7778 | 0.7676 | 0.7882 | 0.7918 | 0.7837 | 0.7999 | 0.7698 | 0.7887 | 0.7792 | 0.8803 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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