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---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: ner_model_ep_all
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_ep_all
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3739
- allergy Name F1: 0.7755
- allergy Name Pres: 0.76
- allergy Name Rec: 0.7917
- cancer F1: 0.7389
- cancer Pres: 0.7283
- cancer Rec: 0.7497
- chronic Disease F1: 0.7778
- chronic Disease Pres: 0.7676
- chronic Disease Rec: 0.7882
- treatment F1: 0.7918
- treatmen Prest: 0.7837
- treatment Rec: 0.7999
- Over All Precision: 0.7698
- Over All Recall: 0.7887
- Over All F1: 0.7792
- Over All Accuracy: 0.8803
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
| 0.5174 | 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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