---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLM-AgloBERTa-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-hu-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: 0.2705
- Precision: 0.9070
- Recall: 0.9256
- F1: 0.9162
- Accuracy: 0.9671

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0861        | 1.0   | 1250  | 0.2330          | 0.8811    | 0.9005 | 0.8907 | 0.9574   |
| 0.0666        | 2.0   | 2500  | 0.1955          | 0.8842    | 0.9113 | 0.8975 | 0.9597   |
| 0.0613        | 3.0   | 3750  | 0.2059          | 0.8941    | 0.9035 | 0.8988 | 0.9603   |
| 0.0408        | 4.0   | 5000  | 0.2386          | 0.9021    | 0.9023 | 0.9022 | 0.9616   |
| 0.0327        | 5.0   | 6250  | 0.2314          | 0.8892    | 0.9188 | 0.9038 | 0.9621   |
| 0.0222        | 6.0   | 7500  | 0.2574          | 0.9015    | 0.9108 | 0.9061 | 0.9631   |
| 0.0143        | 7.0   | 8750  | 0.2482          | 0.9070    | 0.9192 | 0.9131 | 0.9657   |
| 0.0093        | 8.0   | 10000 | 0.2570          | 0.9092    | 0.9206 | 0.9149 | 0.9664   |
| 0.0044        | 9.0   | 11250 | 0.2697          | 0.9055    | 0.9199 | 0.9126 | 0.9660   |
| 0.0026        | 10.0  | 12500 | 0.2705          | 0.9070    | 0.9256 | 0.9162 | 0.9671   |


### Framework versions

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