---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test-ner
  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. -->

# test-ner

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2327
- Precision: 0.9133
- Recall: 0.9225
- F1: 0.9179
- Accuracy: 0.9687

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3488        | 1.0   | 625  | 0.1874          | 0.8414    | 0.8560 | 0.8487 | 0.9486   |
| 0.1914        | 2.0   | 1250 | 0.1857          | 0.8674    | 0.8794 | 0.8734 | 0.9552   |
| 0.1418        | 3.0   | 1875 | 0.1618          | 0.8752    | 0.8906 | 0.8828 | 0.9596   |
| 0.0883        | 4.0   | 2500 | 0.1701          | 0.8952    | 0.9011 | 0.8982 | 0.9631   |
| 0.0582        | 5.0   | 3125 | 0.1873          | 0.8774    | 0.9149 | 0.8958 | 0.9620   |
| 0.0453        | 6.0   | 3750 | 0.1902          | 0.9008    | 0.9131 | 0.9069 | 0.9641   |
| 0.0353        | 7.0   | 4375 | 0.2059          | 0.8992    | 0.9067 | 0.9029 | 0.9654   |
| 0.015         | 8.0   | 5000 | 0.2231          | 0.9031    | 0.9183 | 0.9106 | 0.9659   |
| 0.0114        | 9.0   | 5625 | 0.2234          | 0.9120    | 0.9198 | 0.9159 | 0.9677   |
| 0.0066        | 10.0  | 6250 | 0.2327          | 0.9133    | 0.9225 | 0.9179 | 0.9687   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0