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---
tags:
- generated_from_trainer
model-index:
- name: ner_model_ep3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ner_model_ep3
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3874
- allergy Name F1: 0.7968
- allergy Name Pres: 0.7706
- allergy Name Rec: 0.8249
- cancer F1: 0.7556
- cancer Pres: 0.7589
- cancer Rec: 0.7524
- chronic Disease F1: 0.7776
- chronic Disease Pres: 0.7562
- chronic Disease Rec: 0.8002
- treatment F1: 0.7804
- treatmen Prest: 0.7620
- treatment Rec: 0.7996
- Over All Precision: 0.7596
- Over All Recall: 0.7936
- Over All F1: 0.7762
- Over All Accuracy: 0.8806
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
| 0.3761 | 1.0 | 324 | 0.3480 | 0.7346 | 0.6720 | 0.8099 | 0.7108 | 0.7584 | 0.6688 | 0.7657 | 0.7619 | 0.7695 | 0.7700 | 0.7437 | 0.7983 | 0.7499 | 0.7687 | 0.7592 | 0.8738 |
| 0.29 | 2.0 | 648 | 0.3548 | 0.7593 | 0.7023 | 0.8263 | 0.7406 | 0.7683 | 0.7149 | 0.7710 | 0.7608 | 0.7816 | 0.7738 | 0.7435 | 0.8067 | 0.7519 | 0.7842 | 0.7677 | 0.8775 |
| 0.232 | 3.0 | 972 | 0.3579 | 0.8046 | 0.7787 | 0.8323 | 0.7446 | 0.7472 | 0.7421 | 0.7763 | 0.7568 | 0.7968 | 0.7798 | 0.7658 | 0.7944 | 0.7601 | 0.7887 | 0.7741 | 0.8809 |
| 0.1945 | 4.0 | 1296 | 0.3829 | 0.7942 | 0.7645 | 0.8263 | 0.7463 | 0.7678 | 0.7260 | 0.7749 | 0.7584 | 0.7920 | 0.7808 | 0.7683 | 0.7938 | 0.7643 | 0.7840 | 0.7741 | 0.8792 |
| 0.1734 | 5.0 | 1620 | 0.3874 | 0.7968 | 0.7706 | 0.8249 | 0.7556 | 0.7589 | 0.7524 | 0.7776 | 0.7562 | 0.8002 | 0.7804 | 0.7620 | 0.7996 | 0.7596 | 0.7936 | 0.7762 | 0.8806 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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