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---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-85-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/drugtemist-it-85-ner
type: Rodrigo1771/drugtemist-it-85-ner
config: DrugTEMIST Italian NER
split: validation
args: DrugTEMIST Italian NER
metrics:
- name: Precision
type: precision
value: 0.9193083573487032
- name: Recall
type: recall
value: 0.9264278799612778
- name: F1
type: f1
value: 0.922854387656702
- name: Accuracy
type: accuracy
value: 0.9985847831732018
---
<!-- 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-85-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0061
- Precision: 0.9193
- Recall: 0.9264
- F1: 0.9229
- 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 | 1.0 | 479 | 0.0052 | 0.8498 | 0.9313 | 0.8887 | 0.9983 |
| 0.0127 | 2.0 | 958 | 0.0056 | 0.9063 | 0.9080 | 0.9072 | 0.9984 |
| 0.0035 | 3.0 | 1437 | 0.0047 | 0.9211 | 0.9158 | 0.9184 | 0.9985 |
| 0.002 | 4.0 | 1916 | 0.0065 | 0.9028 | 0.9080 | 0.9054 | 0.9984 |
| 0.0014 | 5.0 | 2395 | 0.0061 | 0.9193 | 0.9264 | 0.9229 | 0.9986 |
| 0.0007 | 6.0 | 2874 | 0.0069 | 0.9246 | 0.8906 | 0.9073 | 0.9984 |
| 0.0004 | 7.0 | 3353 | 0.0071 | 0.8990 | 0.9216 | 0.9101 | 0.9985 |
| 0.0003 | 8.0 | 3832 | 0.0076 | 0.9135 | 0.9303 | 0.9218 | 0.9986 |
| 0.0001 | 9.0 | 4311 | 0.0080 | 0.9130 | 0.9245 | 0.9187 | 0.9986 |
| 0.0001 | 10.0 | 4790 | 0.0080 | 0.9107 | 0.9284 | 0.9195 | 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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