---
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  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-finetuned-ner

This model is a fine-tuned version of [albert/albert-base-v1](https://huggingface.co/albert/albert-base-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0994
- Precision: 0.9628
- Recall: 0.9740
- F1: 0.9683
- Accuracy: 0.9813

## 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0688        | 1.0   | 5285  | 0.1227          | 0.9555    | 0.9670 | 0.9612 | 0.9769   |
| 0.0583        | 2.0   | 10570 | 0.1051          | 0.9581    | 0.9723 | 0.9652 | 0.9803   |
| 0.0798        | 3.0   | 15855 | 0.0994          | 0.9628    | 0.9740 | 0.9683 | 0.9813   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1