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---
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
model-index:
- name: JNLPBA_ClinicalBERT_NER
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. -->
# JNLPBA_ClinicalBERT_NER
This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1723
- Seqeval classification report: precision recall f1-score support
DNA 0.72 0.81 0.77 1351
RNA 0.71 0.86 0.78 723
cell_line 0.84 0.74 0.78 582
cell_type 0.72 0.75 0.73 5623
protein 0.85 0.85 0.85 3501
micro avg 0.76 0.79 0.78 11780
macro avg 0.77 0.80 0.78 11780
weighted avg 0.76 0.79 0.78 11780
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.336 | 1.0 | 582 | 0.1930 | precision recall f1-score support
DNA 0.72 0.77 0.75 1351
RNA 0.70 0.84 0.77 723
cell_line 0.85 0.70 0.77 582
cell_type 0.71 0.68 0.69 5623
protein 0.85 0.80 0.83 3501
micro avg 0.76 0.74 0.75 11780
macro avg 0.77 0.76 0.76 11780
weighted avg 0.76 0.74 0.75 11780
|
| 0.1841 | 2.0 | 1164 | 0.1762 | precision recall f1-score support
DNA 0.73 0.78 0.76 1351
RNA 0.70 0.87 0.78 723
cell_line 0.86 0.71 0.78 582
cell_type 0.71 0.73 0.72 5623
protein 0.86 0.83 0.84 3501
micro avg 0.76 0.77 0.77 11780
macro avg 0.77 0.78 0.78 11780
weighted avg 0.77 0.77 0.77 11780
|
| 0.1582 | 3.0 | 1746 | 0.1723 | precision recall f1-score support
DNA 0.72 0.81 0.77 1351
RNA 0.71 0.86 0.78 723
cell_line 0.84 0.74 0.78 582
cell_type 0.72 0.75 0.73 5623
protein 0.85 0.85 0.85 3501
micro avg 0.76 0.79 0.78 11780
macro avg 0.77 0.80 0.78 11780
weighted avg 0.76 0.79 0.78 11780
|
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|