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

# XLM-AgloBERTa-eu-hu-ner

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2582
- Precision: 0.9039
- Recall: 0.9209
- F1: 0.9123
- Accuracy: 0.9661

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2501        | 1.0   | 1250  | 0.1919          | 0.8343    | 0.8634 | 0.8486 | 0.9456   |
| 0.1718        | 2.0   | 2500  | 0.1631          | 0.8662    | 0.8793 | 0.8727 | 0.9540   |
| 0.1269        | 3.0   | 3750  | 0.1743          | 0.8748    | 0.8913 | 0.8830 | 0.9571   |
| 0.0899        | 4.0   | 5000  | 0.1642          | 0.8734    | 0.9083 | 0.8905 | 0.9587   |
| 0.0578        | 5.0   | 6250  | 0.1958          | 0.8867    | 0.9000 | 0.8933 | 0.9599   |
| 0.0474        | 6.0   | 7500  | 0.1823          | 0.9062    | 0.9069 | 0.9065 | 0.9647   |
| 0.031         | 7.0   | 8750  | 0.1928          | 0.9007    | 0.9137 | 0.9071 | 0.9643   |
| 0.0168        | 8.0   | 10000 | 0.2168          | 0.9042    | 0.9113 | 0.9077 | 0.9644   |
| 0.0109        | 9.0   | 11250 | 0.2423          | 0.9028    | 0.9191 | 0.9108 | 0.9658   |
| 0.0057        | 10.0  | 12500 | 0.2582          | 0.9039    | 0.9209 | 0.9123 | 0.9661   |


### Framework versions

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