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

# uner-roberta-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: 0.0930
- Precision: 0.8622
- Recall: 0.9010
- F1: 0.8812
- Accuracy: 0.9728

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 144  | 0.1285          | 0.8005    | 0.8241 | 0.8121 | 0.9589   |
| No log        | 2.0   | 288  | 0.1142          | 0.8142    | 0.8748 | 0.8434 | 0.9655   |
| No log        | 3.0   | 432  | 0.0962          | 0.8485    | 0.8985 | 0.8728 | 0.9702   |
| 0.1923        | 4.0   | 576  | 0.0916          | 0.8543    | 0.9018 | 0.8774 | 0.9719   |
| 0.1923        | 5.0   | 720  | 0.0930          | 0.8622    | 0.9010 | 0.8812 | 0.9728   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3