README.md

---
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-lg-cased-ms-ner-v3-test
  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-lg-cased-ms-ner-v3-test

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1288
- Precision: 0.8909
- Recall: 0.9094
- F1: 0.9001
- Accuracy: 0.9804

## 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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1394        | 1.0   | 3615  | 0.1234          | 0.8374    | 0.8269 | 0.8321 | 0.9695   |
| 0.0736        | 2.0   | 7230  | 0.1110          | 0.8618    | 0.8742 | 0.8679 | 0.9756   |
| 0.0385        | 3.0   | 10845 | 0.1019          | 0.8844    | 0.8968 | 0.8906 | 0.9787   |
| 0.019         | 4.0   | 14460 | 0.1193          | 0.8859    | 0.9048 | 0.8953 | 0.9798   |
| 0.0094        | 5.0   | 18075 | 0.1288          | 0.8909    | 0.9094 | 0.9001 | 0.9804   |


### Framework versions

- Transformers 4.39.3
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.15.2