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---
license: mit
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: bc2gm_corpus
type: bc2gm_corpus
args: bc2gm_corpus
metrics:
- name: Precision
type: precision
value: 0.7853881278538812
- name: Recall
type: recall
value: 0.8158102766798419
- name: F1
type: f1
value: 0.8003101977510663
- name: Accuracy
type: accuracy
value: 0.9758965601366187
---
<!-- 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. -->
# bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc2gm_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1505
- Precision: 0.7854
- Recall: 0.8158
- F1: 0.8003
- Accuracy: 0.9759
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0981 | 1.0 | 782 | 0.0712 | 0.7228 | 0.7948 | 0.7571 | 0.9724 |
| 0.0509 | 2.0 | 1564 | 0.0687 | 0.7472 | 0.8199 | 0.7818 | 0.9746 |
| 0.0121 | 3.0 | 2346 | 0.0740 | 0.7725 | 0.8011 | 0.7866 | 0.9747 |
| 0.0001 | 4.0 | 3128 | 0.1009 | 0.7618 | 0.8251 | 0.7922 | 0.9741 |
| 0.0042 | 5.0 | 3910 | 0.1106 | 0.7757 | 0.8185 | 0.7965 | 0.9754 |
| 0.0015 | 6.0 | 4692 | 0.1182 | 0.7812 | 0.8111 | 0.7958 | 0.9758 |
| 0.0001 | 7.0 | 5474 | 0.1283 | 0.7693 | 0.8275 | 0.7973 | 0.9753 |
| 0.0072 | 8.0 | 6256 | 0.1376 | 0.7863 | 0.8158 | 0.8008 | 0.9762 |
| 0.0045 | 9.0 | 7038 | 0.1468 | 0.7856 | 0.8180 | 0.8015 | 0.9761 |
| 0.0 | 10.0 | 7820 | 0.1505 | 0.7854 | 0.8158 | 0.8003 | 0.9759 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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