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

# roberta-lg-cased-ms-ner-v3-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: 0.1071
- Precision: 0.8912
- Recall: 0.9039
- F1: 0.8975
- 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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1478        | 1.0   | 3615  | 0.1187          | 0.8247    | 0.8225 | 0.8236 | 0.9687   |
| 0.0909        | 2.0   | 7230  | 0.1025          | 0.8617    | 0.8702 | 0.8659 | 0.9753   |
| 0.0552        | 3.0   | 10845 | 0.1016          | 0.8789    | 0.8886 | 0.8837 | 0.9790   |
| 0.0325        | 4.0   | 14460 | 0.0966          | 0.8958    | 0.8956 | 0.8957 | 0.9815   |
| 0.0185        | 5.0   | 18075 | 0.1071          | 0.8912    | 0.9039 | 0.8975 | 0.9813   |


### Framework versions

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