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

# RE_NegREF_NSD_Nubes_Training_Development_dataset_xlm_RoBERTa_base_fine_tuned

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co./FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2774
- Negref Precision: 0.5671
- Negref Recall: 0.5899
- Negref F1: 0.5782
- Neg Precision: 0.9511
- Neg Recall: 0.9771
- Neg F1: 0.9639
- Nsco Precision: 0.8734
- Nsco Recall: 0.9181
- Nsco F1: 0.8952
- Precision: 0.8399
- Recall: 0.8727
- F1: 0.8560
- Accuracy: 0.9615

## 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: 12

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Negref Precision | Negref Recall | Negref F1 | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.1739        | 1.0   | 1729  | 0.1433          | 0.4553           | 0.4419        | 0.4485    | 0.9216        | 0.9629     | 0.9418 | 0.7944         | 0.8717      | 0.8313  | 0.7799    | 0.8180 | 0.7985 | 0.9555   |
| 0.1189        | 2.0   | 3458  | 0.1388          | 0.4859           | 0.5476        | 0.5149    | 0.9230        | 0.9683     | 0.9451 | 0.7951         | 0.8895      | 0.8397  | 0.7779    | 0.8494 | 0.8121 | 0.9569   |
| 0.0944        | 3.0   | 5187  | 0.1374          | 0.5771           | 0.5539        | 0.5653    | 0.9495        | 0.9640     | 0.9567 | 0.8535         | 0.8860      | 0.8695  | 0.8375    | 0.8476 | 0.8425 | 0.9615   |
| 0.0703        | 4.0   | 6916  | 0.1524          | 0.5415           | 0.6068        | 0.5723    | 0.9410        | 0.9749     | 0.9576 | 0.8476         | 0.8848      | 0.8658  | 0.8164    | 0.8628 | 0.8390 | 0.9602   |
| 0.0472        | 5.0   | 8645  | 0.1812          | 0.5306           | 0.5687        | 0.5490    | 0.9368        | 0.9716     | 0.9539 | 0.8440         | 0.8931      | 0.8679  | 0.8139    | 0.8566 | 0.8347 | 0.9585   |
| 0.0371        | 6.0   | 10374 | 0.2231          | 0.5371           | 0.5814        | 0.5584    | 0.9286        | 0.9803     | 0.9538 | 0.8448         | 0.9181      | 0.8799  | 0.8129    | 0.8723 | 0.8415 | 0.9588   |
| 0.031         | 7.0   | 12103 | 0.2036          | 0.5362           | 0.5793        | 0.5569    | 0.9509        | 0.9716     | 0.9611 | 0.8830         | 0.9145      | 0.8985  | 0.8340    | 0.8669 | 0.8501 | 0.9617   |
| 0.0226        | 8.0   | 13832 | 0.2390          | 0.5392           | 0.5666        | 0.5526    | 0.9507        | 0.9694     | 0.9600 | 0.8751         | 0.9157      | 0.8950  | 0.8335    | 0.8637 | 0.8483 | 0.9589   |
| 0.0174        | 9.0   | 15561 | 0.2462          | 0.5355           | 0.5899        | 0.5614    | 0.9443        | 0.9803     | 0.9620 | 0.8694         | 0.9169      | 0.8925  | 0.8258    | 0.8736 | 0.8491 | 0.9599   |
| 0.0098        | 10.0  | 17290 | 0.2751          | 0.5796           | 0.5772        | 0.5784    | 0.9490        | 0.9749     | 0.9618 | 0.8740         | 0.9145      | 0.8938  | 0.8443    | 0.8678 | 0.8559 | 0.9621   |
| 0.008         | 11.0  | 19019 | 0.2760          | 0.5611           | 0.5729        | 0.5669    | 0.9461        | 0.9771     | 0.9613 | 0.8695         | 0.9181      | 0.8931  | 0.8365    | 0.8691 | 0.8525 | 0.9606   |
| 0.0041        | 12.0  | 20748 | 0.2774          | 0.5671           | 0.5899        | 0.5782    | 0.9511        | 0.9771     | 0.9639 | 0.8734         | 0.9181      | 0.8952  | 0.8399    | 0.8727 | 0.8560 | 0.9615   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2