license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioLinkBERT-base-finetuned-ner
results: []
BioLinkBERT-base-finetuned-ner
This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1226
- Precision: 0.8760
- Recall: 0.9185
- F1: 0.8968
- Accuracy: 0.9647
Model description
This model is designed to perform NER function for specific text using BioLink BERT
Intended uses & limitations
The goal was to have a drug tag printed immediately for a particular sentence, but it has the disadvantage of being marked as LABEL
LABEL0 : irrelevant text LABEL1,2 : Drug LABEL3,4 : condition
Training and evaluation data
More information needed
Training procedure
Reference Code: SciBERT Fine-Tuning on Drug/ADE Corpus (https://github.com/jsylee/personal-projects/blob/master/Hugging%20Face%20ADR%20Fine-Tuning/SciBERT%20ADR%20Fine-Tuning.ipynb)
How to use
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner")
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1099 | 1.0 | 201 | 0.1489 | 0.8415 | 0.9032 | 0.8713 | 0.9566 |
| 0.1716 | 2.0 | 402 | 0.1318 | 0.8456 | 0.9135 | 0.8782 | 0.9597 |
| 0.1068 | 3.0 | 603 | 0.1197 | 0.8682 | 0.9110 | 0.8891 | 0.9641 |
| 0.0161 | 4.0 | 804 | 0.1219 | 0.8694 | 0.9157 | 0.8919 | 0.9639 |
| 0.1499 | 5.0 | 1005 | 0.1226 | 0.8760 | 0.9185 | 0.8968 | 0.9647 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1