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---
library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-en-8-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/drugtemist-en-8-ner
type: Rodrigo1771/drugtemist-en-8-ner
config: DrugTEMIST English NER
split: validation
args: DrugTEMIST English NER
metrics:
- name: Precision
type: precision
value: 0.9318394024276377
- name: Recall
type: recall
value: 0.9301025163094129
- name: F1
type: f1
value: 0.9309701492537313
- name: Accuracy
type: accuracy
value: 0.9986953367008066
---
<!-- 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 Rodrigo1771/drugtemist-en-8-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0057
- Precision: 0.9318
- Recall: 0.9301
- F1: 0.9310
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 493 | 0.0050 | 0.9288 | 0.9245 | 0.9267 | 0.9987 |
| 0.018 | 2.0 | 986 | 0.0057 | 0.9104 | 0.9189 | 0.9147 | 0.9984 |
| 0.0044 | 3.0 | 1479 | 0.0079 | 0.9362 | 0.9161 | 0.9260 | 0.9985 |
| 0.0023 | 4.0 | 1972 | 0.0057 | 0.9318 | 0.9301 | 0.9310 | 0.9987 |
| 0.0014 | 5.0 | 2465 | 0.0070 | 0.9201 | 0.9226 | 0.9214 | 0.9986 |
| 0.0008 | 6.0 | 2958 | 0.0082 | 0.9118 | 0.9254 | 0.9186 | 0.9985 |
| 0.0006 | 7.0 | 3451 | 0.0074 | 0.9172 | 0.9394 | 0.9282 | 0.9986 |
| 0.0003 | 8.0 | 3944 | 0.0085 | 0.9219 | 0.9245 | 0.9232 | 0.9985 |
| 0.0003 | 9.0 | 4437 | 0.0086 | 0.9149 | 0.9320 | 0.9234 | 0.9985 |
| 0.0002 | 10.0 | 4930 | 0.0089 | 0.9172 | 0.9292 | 0.9231 | 0.9985 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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