---
base_model: bert-base-uncased
library_name: transformers
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: NER_training_base_uncased_with_randomization
  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. -->

# NER_training_base_uncased_with_randomization

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0472
- Precision: 0.9550
- Recall: 0.9576
- F1: 0.9563
- Accuracy: 0.9849

## 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: 16
- eval_batch_size: 32
- seed: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0508        | 1.0   | 9836  | 0.0472          | 0.9550    | 0.9576 | 0.9563 | 0.9849   |
| 0.035         | 2.0   | 19672 | 0.0473          | 0.9590    | 0.9644 | 0.9617 | 0.9870   |
| 0.021         | 3.0   | 29508 | 0.0537          | 0.9592    | 0.9636 | 0.9614 | 0.9870   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0