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---
library_name: transformers
base_model: IVN-RIN/bioBIT
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-it-fasttext-9-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/drugtemist-it-fasttext-9-ner
type: Rodrigo1771/drugtemist-it-fasttext-9-ner
config: DrugTEMIST Italian NER
split: validation
args: DrugTEMIST Italian NER
metrics:
- name: Precision
type: precision
value: 0.9168241965973535
- name: Recall
type: recall
value: 0.9390125847047435
- name: F1
type: f1
value: 0.9277857484457198
- name: Accuracy
type: accuracy
value: 0.9986691768371851
---
<!-- 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-9-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0051
- Precision: 0.9168
- Recall: 0.9390
- F1: 0.9278
- 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 | 0.9988 | 434 | 0.0046 | 0.8889 | 0.8829 | 0.8859 | 0.9982 |
| 0.011 | 2.0 | 869 | 0.0039 | 0.9147 | 0.9138 | 0.9143 | 0.9985 |
| 0.0034 | 2.9988 | 1303 | 0.0045 | 0.9317 | 0.8848 | 0.9076 | 0.9985 |
| 0.0019 | 4.0 | 1738 | 0.0056 | 0.9309 | 0.9129 | 0.9218 | 0.9986 |
| 0.0013 | 4.9988 | 2172 | 0.0051 | 0.9168 | 0.9390 | 0.9278 | 0.9987 |
| 0.0008 | 6.0 | 2607 | 0.0071 | 0.9325 | 0.9100 | 0.9211 | 0.9986 |
| 0.0005 | 6.9988 | 3041 | 0.0068 | 0.9291 | 0.9264 | 0.9278 | 0.9986 |
| 0.0005 | 8.0 | 3476 | 0.0075 | 0.9226 | 0.9226 | 0.9226 | 0.9986 |
| 0.0003 | 8.9988 | 3910 | 0.0080 | 0.9187 | 0.9293 | 0.9240 | 0.9986 |
| 0.0002 | 9.9885 | 4340 | 0.0083 | 0.9282 | 0.9264 | 0.9273 | 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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