---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLM-AgloBERTa-fi-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-fi-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: 0.2917
- Precision: 0.8818
- Recall: 0.8970
- F1: 0.8893
- Accuracy: 0.9578

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2709        | 1.0   | 1250  | 0.2630          | 0.8052    | 0.8239 | 0.8145 | 0.9296   |
| 0.1861        | 2.0   | 2500  | 0.2313          | 0.8182    | 0.8543 | 0.8358 | 0.9357   |
| 0.1341        | 3.0   | 3750  | 0.1858          | 0.8599    | 0.8644 | 0.8621 | 0.9492   |
| 0.1035        | 4.0   | 5000  | 0.2108          | 0.8705    | 0.8738 | 0.8721 | 0.9520   |
| 0.0784        | 5.0   | 6250  | 0.2073          | 0.8736    | 0.8779 | 0.8757 | 0.9538   |
| 0.0572        | 6.0   | 7500  | 0.2226          | 0.8758    | 0.8828 | 0.8793 | 0.9549   |
| 0.0358        | 7.0   | 8750  | 0.2514          | 0.8763    | 0.8885 | 0.8824 | 0.9566   |
| 0.0226        | 8.0   | 10000 | 0.2522          | 0.8792    | 0.8922 | 0.8857 | 0.9563   |
| 0.0151        | 9.0   | 11250 | 0.2836          | 0.8795    | 0.8949 | 0.8871 | 0.9576   |
| 0.0079        | 10.0  | 12500 | 0.2917          | 0.8818    | 0.8970 | 0.8893 | 0.9578   |


### Framework versions

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