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---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-fasttext-85-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/drugtemist-it-fasttext-85-ner
type: Rodrigo1771/drugtemist-it-fasttext-85-ner
config: DrugTEMIST Italian NER
split: validation
args: DrugTEMIST Italian NER
metrics:
- name: Precision
type: precision
value: 0.9211538461538461
- name: Recall
type: recall
value: 0.9273959341723137
- name: F1
type: f1
value: 0.9242643511818619
- name: Accuracy
type: accuracy
value: 0.9986302259153467
---
<!-- 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-fasttext-85-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0080
- Precision: 0.9212
- Recall: 0.9274
- F1: 0.9243
- Accuracy: 0.9986
## 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 | 451 | 0.0051 | 0.9326 | 0.8703 | 0.9004 | 0.9984 |
| 0.0116 | 2.0 | 903 | 0.0049 | 0.9066 | 0.9206 | 0.9135 | 0.9985 |
| 0.0034 | 2.9989 | 1354 | 0.0056 | 0.8990 | 0.9216 | 0.9101 | 0.9984 |
| 0.0018 | 4.0 | 1806 | 0.0066 | 0.9094 | 0.9235 | 0.9164 | 0.9985 |
| 0.0011 | 4.9989 | 2257 | 0.0056 | 0.9082 | 0.9293 | 0.9187 | 0.9986 |
| 0.0007 | 6.0 | 2709 | 0.0068 | 0.9145 | 0.9109 | 0.9127 | 0.9985 |
| 0.0005 | 6.9989 | 3160 | 0.0076 | 0.8880 | 0.9284 | 0.9077 | 0.9984 |
| 0.0003 | 8.0 | 3612 | 0.0080 | 0.9094 | 0.9235 | 0.9164 | 0.9986 |
| 0.0002 | 8.9989 | 4063 | 0.0078 | 0.9162 | 0.9206 | 0.9184 | 0.9986 |
| 0.0001 | 9.9889 | 4510 | 0.0080 | 0.9212 | 0.9274 | 0.9243 | 0.9986 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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