---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner2
  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-ner2

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1749
- Precision: 0.5751
- Recall: 0.6210
- F1: 0.5972
- Accuracy: 0.9453

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 97   | 0.2560          | 0.4434    | 0.5933 | 0.5075 | 0.9187   |
| No log        | 2.0   | 194  | 0.1889          | 0.5398    | 0.5927 | 0.5650 | 0.9385   |
| No log        | 3.0   | 291  | 0.1749          | 0.5751    | 0.6210 | 0.5972 | 0.9453   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.2