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---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- generated_from_trainer
datasets:
- drugtemist-en-75-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: drugtemist-en-75-ner
type: drugtemist-en-75-ner
config: DrugTEMIST English NER
split: validation
args: DrugTEMIST English NER
metrics:
- name: Precision
type: precision
value: 0.921028466483012
- name: Recall
type: recall
value: 0.934762348555452
- name: F1
type: f1
value: 0.9278445883441258
- name: Accuracy
type: accuracy
value: 0.9986883598917199
---
<!-- 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. -->
# output
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the drugtemist-en-75-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0083
- Precision: 0.9210
- Recall: 0.9348
- F1: 0.9278
- Accuracy: 0.9987
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0189 | 1.0 | 504 | 0.0052 | 0.8712 | 0.9394 | 0.9040 | 0.9984 |
| 0.0047 | 2.0 | 1008 | 0.0048 | 0.9253 | 0.9236 | 0.9244 | 0.9987 |
| 0.0027 | 3.0 | 1512 | 0.0059 | 0.9252 | 0.9226 | 0.9239 | 0.9986 |
| 0.0015 | 4.0 | 2016 | 0.0065 | 0.9342 | 0.9264 | 0.9303 | 0.9987 |
| 0.0011 | 5.0 | 2520 | 0.0073 | 0.9073 | 0.9394 | 0.9231 | 0.9986 |
| 0.0005 | 6.0 | 3024 | 0.0090 | 0.9191 | 0.9217 | 0.9204 | 0.9984 |
| 0.0007 | 7.0 | 3528 | 0.0084 | 0.9074 | 0.9310 | 0.9190 | 0.9986 |
| 0.0004 | 8.0 | 4032 | 0.0085 | 0.9093 | 0.9338 | 0.9214 | 0.9986 |
| 0.0003 | 9.0 | 4536 | 0.0080 | 0.9186 | 0.9357 | 0.9271 | 0.9987 |
| 0.0002 | 10.0 | 5040 | 0.0083 | 0.9210 | 0.9348 | 0.9278 | 0.9987 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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