---
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: x5-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. -->

# x5-ner

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4700
- Precision: 0.9465
- Recall: 0.9597
- F1: 0.9531
- Accuracy: 0.9525

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1882        | 4.0   | 12264 | 0.2794          | 0.9282    | 0.9477 | 0.9379 | 0.9443   |
| 0.1232        | 5.0   | 15330 | 0.2867          | 0.9391    | 0.9534 | 0.9462 | 0.9504   |
| 0.0967        | 6.0   | 18396 | 0.3523          | 0.9400    | 0.9543 | 0.9471 | 0.9508   |
| 0.0529        | 7.0   | 21462 | 0.3790          | 0.9397    | 0.9585 | 0.9490 | 0.9516   |
| 0.0372        | 8.0   | 24528 | 0.4232          | 0.9454    | 0.9556 | 0.9505 | 0.9518   |
| 0.0238        | 9.0   | 27594 | 0.4425          | 0.9472    | 0.9616 | 0.9544 | 0.9544   |
| 0.0126        | 10.0  | 30660 | 0.4700          | 0.9465    | 0.9597 | 0.9531 | 0.9525   |


### Framework versions

- Transformers 4.56.2
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.22.0