---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-lg-cased-ms-ner-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. -->

# roberta-lg-cased-ms-ner-test

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1631
- Precision: 0.8047
- Recall: 0.8306
- F1: 0.8174
- Accuracy: 0.9660

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2027        | 1.0   | 2712  | 0.1739          | 0.7335    | 0.7283 | 0.7309 | 0.9518   |
| 0.1304        | 2.0   | 5424  | 0.1446          | 0.7860    | 0.7674 | 0.7766 | 0.9605   |
| 0.0842        | 3.0   | 8136  | 0.1393          | 0.7892    | 0.8118 | 0.8003 | 0.9629   |
| 0.0556        | 4.0   | 10848 | 0.1498          | 0.8001    | 0.8288 | 0.8142 | 0.9648   |
| 0.0363        | 5.0   | 13560 | 0.1631          | 0.8047    | 0.8306 | 0.8174 | 0.9660   |


### Framework versions

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