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---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
model-index:
- name: JNLPBA_bioBERT_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_bioBERT_NER
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1433
- Seqeval classification report: precision recall f1-score support
DNA 0.81 0.83 0.82 1593
RNA 0.79 0.83 0.81 1400
cell_line 0.77 0.82 0.79 1016
cell_type 0.98 0.96 0.97 37439
protein 0.85 0.86 0.85 2992
micro avg 0.95 0.94 0.95 44440
macro avg 0.84 0.86 0.85 44440
weighted avg 0.95 0.94 0.95 44440
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.2673 | 1.0 | 582 | 0.1492 | precision recall f1-score support
DNA 0.79 0.81 0.80 1593
RNA 0.78 0.79 0.79 1400
cell_line 0.74 0.84 0.79 1016
cell_type 0.98 0.96 0.97 37439
protein 0.84 0.86 0.85 2992
micro avg 0.95 0.94 0.94 44440
macro avg 0.83 0.85 0.84 44440
weighted avg 0.95 0.94 0.94 44440
|
| 0.1408 | 2.0 | 1164 | 0.1469 | precision recall f1-score support
DNA 0.83 0.80 0.81 1593
RNA 0.78 0.83 0.80 1400
cell_line 0.75 0.83 0.79 1016
cell_type 0.98 0.96 0.97 37439
protein 0.84 0.87 0.85 2992
micro avg 0.95 0.94 0.95 44440
macro avg 0.84 0.86 0.85 44440
weighted avg 0.95 0.94 0.95 44440
|
| 0.1237 | 3.0 | 1746 | 0.1433 | precision recall f1-score support
DNA 0.81 0.83 0.82 1593
RNA 0.79 0.83 0.81 1400
cell_line 0.77 0.82 0.79 1016
cell_type 0.98 0.96 0.97 37439
protein 0.85 0.86 0.85 2992
micro avg 0.95 0.94 0.95 44440
macro avg 0.84 0.86 0.85 44440
weighted avg 0.95 0.94 0.95 44440
|
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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