---
library_name: transformers
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: PubMedBERT-full-finetuned-ner-pablo
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# PubMedBERT-full-finetuned-ner-pablo

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0905
- Precision: 0.8142
- Recall: 0.8048
- F1: 0.8095
- 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.9970 | 252  | 0.0892          | 0.7631    | 0.7751 | 0.7690 | 0.9751   |
| 0.1853        | 1.9980 | 505  | 0.0802          | 0.8139    | 0.7876 | 0.8005 | 0.9780   |
| 0.1853        | 2.9990 | 758  | 0.0792          | 0.7994    | 0.7984 | 0.7989 | 0.9767   |
| 0.0461        | 4.0    | 1011 | 0.0788          | 0.8134    | 0.8045 | 0.8089 | 0.9780   |
| 0.0461        | 4.9852 | 1260 | 0.0905          | 0.8142    | 0.8048 | 0.8095 | 0.9771   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1