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base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/drugtemist-it-ner
type: Rodrigo1771/drugtemist-it-ner
config: DrugTEMIST Italian NER
split: validation
args: DrugTEMIST Italian NER
metrics:
- name: Precision
type: precision
value: 0.9328214971209213
- name: Recall
type: recall
value: 0.9409486931268151
- name: F1
type: f1
value: 0.936867469879518
- name: Accuracy
type: accuracy
value: 0.9988184887042326
---
<!-- 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 [IVN-RIN/bioBIT](https://huggingface.co/IVN-RIN/bioBIT) on the Rodrigo1771/drugtemist-it-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Precision: 0.9328
- Recall: 0.9409
- F1: 0.9369
- 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 | 1.0 | 425 | 0.0056 | 0.8672 | 0.9226 | 0.8940 | 0.9981 |
| 0.0104 | 2.0 | 850 | 0.0042 | 0.9151 | 0.9284 | 0.9217 | 0.9986 |
| 0.0034 | 3.0 | 1275 | 0.0043 | 0.9182 | 0.9129 | 0.9155 | 0.9985 |
| 0.0022 | 4.0 | 1700 | 0.0044 | 0.9365 | 0.9138 | 0.9250 | 0.9986 |
| 0.0012 | 5.0 | 2125 | 0.0061 | 0.9107 | 0.9284 | 0.9195 | 0.9985 |
| 0.0009 | 6.0 | 2550 | 0.0060 | 0.9104 | 0.9342 | 0.9221 | 0.9987 |
| 0.0009 | 7.0 | 2975 | 0.0065 | 0.9230 | 0.9400 | 0.9314 | 0.9987 |
| 0.0005 | 8.0 | 3400 | 0.0059 | 0.9258 | 0.9303 | 0.9281 | 0.9987 |
| 0.0004 | 9.0 | 3825 | 0.0066 | 0.9255 | 0.9380 | 0.9317 | 0.9987 |
| 0.0001 | 10.0 | 4250 | 0.0067 | 0.9328 | 0.9409 | 0.9369 | 0.9988 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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