---
base_model: bert-base-cased
model-index:
- name: bert-base-cased-ner-rfb
  results: []
license: apache-2.0
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: token-classification
---

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on a private dataset.

It achieves the following results on the evaluation set:
- eval_loss: 1.2720
- eval_FILL_precision: 0.7627
- eval_FILL_recall: 0.7759
- eval_FILL_f1: 0.7692
- eval_FILL_number: 58
- eval_ROLE_precision: 0.8125
- eval_ROLE_recall: 0.8125
- eval_ROLE_f1: 0.8125
- eval_ROLE_number: 48
- eval_overall_precision: 0.7850
- eval_overall_recall: 0.7925
- eval_overall_f1: 0.7887
- eval_overall_accuracy: 0.8289
- eval_runtime: 1.3592
- eval_samples_per_second: 44.144
- eval_steps_per_second: 5.886
- step: 0

It achieves the following results on the test set:
- test_FILL_f1: 0.8039
- test_FILL_number: 46,
- test_FILL_precision: 0.7321
- test_FILL_recall: 0.8913
- test_ROLE_f1: 0.8182
- test_ROLE_number: 42,
- test_ROLE_precision: 0.7826
- test_ROLE_recall: 0.8571
- test_loss: 0.9132
- test_overall_accuracy: 0.8791
- test_overall_f1: 0.8105
- test_overall_precision: 0.7549
- test_overall_recall: 0.875
- test_runtime: 0.9583
- test_samples_per_second: 63.652
- test_steps_per_second: 8.348


## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 600

### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1