---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT-full-finetuned-ner-pablo
  results: []
---

<!-- 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. -->

# BERT-full-finetuned-ner-pablo

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1008
- Precision: 0.7986
- Recall: 0.7968
- F1: 0.7977
- Accuracy: 0.9750

## 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: 512
- eval_batch_size: 512
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 2048
- 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        | 0.8696 | 5    | 0.0968          | 0.8048    | 0.7943 | 0.7995 | 0.9757   |
| No log        | 1.9130 | 11   | 0.0984          | 0.8030    | 0.7966 | 0.7998 | 0.9754   |
| No log        | 2.9565 | 17   | 0.1003          | 0.8008    | 0.7965 | 0.7987 | 0.9751   |
| No log        | 4.0    | 23   | 0.1008          | 0.7986    | 0.7968 | 0.7977 | 0.9750   |
| No log        | 4.3478 | 25   | 0.1008          | 0.7986    | 0.7968 | 0.7977 | 0.9750   |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1