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---
library_name: transformers
license: cc-by-4.0
base_model: NazaGara/NER-fine-tuned-BETO
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-fine-tuned-BETO-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: biobert_json
type: biobert_json
config: Biobert_json
split: validation
args: Biobert_json
metrics:
- name: Precision
type: precision
value: 0.9501046193465315
- name: Recall
type: recall
value: 0.9622364703325365
- name: F1
type: f1
value: 0.9561320627378993
- name: Accuracy
type: accuracy
value: 0.9770695187165775
---
<!-- 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. -->
# NER-fine-tuned-BETO-finetuned-ner
This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1398
- Precision: 0.9501
- Recall: 0.9622
- F1: 0.9561
- Accuracy: 0.9771
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1451 | 1.0 | 1224 | 0.1089 | 0.9356 | 0.9579 | 0.9466 | 0.9726 |
| 0.0963 | 2.0 | 2448 | 0.1152 | 0.9362 | 0.9701 | 0.9528 | 0.9751 |
| 0.0658 | 3.0 | 3672 | 0.1096 | 0.9485 | 0.9620 | 0.9552 | 0.9763 |
| 0.0481 | 4.0 | 4896 | 0.1050 | 0.9516 | 0.9683 | 0.9598 | 0.9785 |
| 0.0389 | 5.0 | 6120 | 0.1098 | 0.9535 | 0.9636 | 0.9585 | 0.9779 |
| 0.0348 | 6.0 | 7344 | 0.1205 | 0.9502 | 0.9631 | 0.9566 | 0.9772 |
| 0.0274 | 7.0 | 8568 | 0.1250 | 0.9512 | 0.9629 | 0.9570 | 0.9776 |
| 0.024 | 8.0 | 9792 | 0.1359 | 0.9499 | 0.9624 | 0.9561 | 0.9770 |
| 0.0188 | 9.0 | 11016 | 0.1350 | 0.9507 | 0.9626 | 0.9566 | 0.9772 |
| 0.0186 | 10.0 | 12240 | 0.1398 | 0.9501 | 0.9622 | 0.9561 | 0.9771 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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