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---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: ner_model_ep1
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_ep1
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: 0.3469
- allergy Name F1: 0.7059
- allergy Name Pres: 0.7326
- allergy Name Rec: 0.6811
- cancer F1: 0.6499
- cancer Pres: 0.6837
- cancer Rec: 0.6192
- chronic Disease F1: 0.7431
- chronic Disease Pres: 0.7462
- chronic Disease Rec: 0.7400
- treatment F1: 0.7572
- treatmen Prest: 0.7680
- treatment Rec: 0.7468
- Over All Precision: 0.7475
- Over All Recall: 0.7237
- Over All F1: 0.7354
- Over All Accuracy: 0.8824
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
| 0.5799 | 1.0 | 368 | 0.4111 | 0.2933 | 0.825 | 0.1784 | 0.5345 | 0.5010 | 0.5728 | 0.6044 | 0.6269 | 0.5834 | 0.6718 | 0.6294 | 0.7204 | 0.6084 | 0.6379 | 0.6228 | 0.8467 |
| 0.3846 | 2.0 | 736 | 0.3624 | 0.6618 | 0.6054 | 0.7297 | 0.6057 | 0.6025 | 0.6088 | 0.6553 | 0.6925 | 0.6219 | 0.7153 | 0.7450 | 0.6879 | 0.7 | 0.6537 | 0.6761 | 0.8642 |
| 0.3069 | 3.0 | 1104 | 0.3516 | 0.6801 | 0.7284 | 0.6378 | 0.6316 | 0.6489 | 0.6152 | 0.6994 | 0.7227 | 0.6775 | 0.7317 | 0.7368 | 0.7267 | 0.7187 | 0.6906 | 0.7044 | 0.8733 |
| 0.2571 | 4.0 | 1472 | 0.3492 | 0.6807 | 0.7687 | 0.6108 | 0.6472 | 0.6867 | 0.612 | 0.7239 | 0.7276 | 0.7201 | 0.7456 | 0.7548 | 0.7366 | 0.7358 | 0.7092 | 0.7222 | 0.8779 |
| 0.2276 | 5.0 | 1840 | 0.3469 | 0.7059 | 0.7326 | 0.6811 | 0.6499 | 0.6837 | 0.6192 | 0.7431 | 0.7462 | 0.7400 | 0.7572 | 0.7680 | 0.7468 | 0.7475 | 0.7237 | 0.7354 | 0.8824 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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