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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-fasttext-9-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/drugtemist-en-fasttext-9-ner
type: Rodrigo1771/drugtemist-en-fasttext-9-ner
config: DrugTEMIST English NER
split: validation
args: DrugTEMIST English NER
metrics:
- name: Precision
type: precision
value: 0.9311627906976744
- name: Recall
type: recall
value: 0.9328984156570364
- name: F1
type: f1
value: 0.9320297951582869
- name: Accuracy
type: accuracy
value: 0.998772081600759
---
<!-- 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-fasttext-9-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0071
- Precision: 0.9312
- Recall: 0.9329
- F1: 0.9320
- Accuracy: 0.9988
## 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 | 0.9989 | 435 | 0.0060 | 0.8714 | 0.9217 | 0.8958 | 0.9981 |
| 0.0156 | 2.0 | 871 | 0.0044 | 0.9183 | 0.9217 | 0.92 | 0.9987 |
| 0.0038 | 2.9989 | 1306 | 0.0040 | 0.8969 | 0.9404 | 0.9181 | 0.9987 |
| 0.0025 | 4.0 | 1742 | 0.0045 | 0.9078 | 0.9357 | 0.9215 | 0.9986 |
| 0.0016 | 4.9989 | 2177 | 0.0054 | 0.9182 | 0.9096 | 0.9139 | 0.9986 |
| 0.0011 | 6.0 | 2613 | 0.0053 | 0.9152 | 0.9254 | 0.9203 | 0.9986 |
| 0.0009 | 6.9989 | 3048 | 0.0060 | 0.9263 | 0.9366 | 0.9314 | 0.9987 |
| 0.0009 | 8.0 | 3484 | 0.0059 | 0.9181 | 0.9404 | 0.9291 | 0.9988 |
| 0.0005 | 8.9989 | 3919 | 0.0067 | 0.9258 | 0.9301 | 0.9279 | 0.9988 |
| 0.0003 | 9.9885 | 4350 | 0.0071 | 0.9312 | 0.9329 | 0.9320 | 0.9988 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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