---
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_Test_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_Test_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: 0.3660
- Negref Precision: 0.5752
- Negref Recall: 0.5786
- Negref F1: 0.5769
- Neg Precision: 0.9489
- Neg Recall: 0.9642
- Neg F1: 0.9565
- Nsco Precision: 0.8852
- Nsco Recall: 0.9039
- Nsco F1: 0.8945
- Precision: 0.8507
- Recall: 0.8643
- F1: 0.8574
- Accuracy: 0.9575

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.0268        | 1.0   | 1729  | 0.2889          | 0.5289           | 0.5639        | 0.5458    | 0.9560        | 0.9459     | 0.9509 | 0.8616         | 0.8616      | 0.8616  | 0.8302    | 0.8375 | 0.8338 | 0.9537   |
| 0.0233        | 2.0   | 3458  | 0.3034          | 0.5161           | 0.5419        | 0.5287    | 0.9448        | 0.9614     | 0.9530 | 0.8731         | 0.8949      | 0.8838  | 0.8301    | 0.8523 | 0.8411 | 0.9522   |
| 0.0205        | 3.0   | 5187  | 0.2748          | 0.5591           | 0.5624        | 0.5608    | 0.9502        | 0.9649     | 0.9575 | 0.8645         | 0.8926      | 0.8783  | 0.8401    | 0.8570 | 0.8485 | 0.9557   |
| 0.012         | 4.0   | 6916  | 0.2915          | 0.5238           | 0.5653        | 0.5438    | 0.9525        | 0.9586     | 0.9555 | 0.8693         | 0.8858      | 0.8775  | 0.8310    | 0.8523 | 0.8415 | 0.9558   |
| 0.0105        | 5.0   | 8645  | 0.3158          | 0.5526           | 0.5786        | 0.5653    | 0.9515        | 0.9635     | 0.9574 | 0.8663         | 0.9017      | 0.8836  | 0.8377    | 0.8631 | 0.8502 | 0.9559   |
| 0.0095        | 6.0   | 10374 | 0.3048          | 0.5424           | 0.6197        | 0.5785    | 0.9504        | 0.9684     | 0.9593 | 0.8786         | 0.9085      | 0.8933  | 0.8348    | 0.8760 | 0.8549 | 0.9584   |
| 0.0073        | 7.0   | 12103 | 0.3238          | 0.5511           | 0.6021        | 0.5754    | 0.9528        | 0.9642     | 0.9585 | 0.8751         | 0.8956      | 0.8852  | 0.8386    | 0.8658 | 0.8520 | 0.9558   |
| 0.0072        | 8.0   | 13832 | 0.3561          | 0.5737           | 0.5712        | 0.5725    | 0.9547        | 0.9614     | 0.9580 | 0.88           | 0.8986      | 0.8892  | 0.8510    | 0.8596 | 0.8553 | 0.9557   |
| 0.0048        | 9.0   | 15561 | 0.3452          | 0.5739           | 0.5815        | 0.5777    | 0.9447        | 0.9719     | 0.9581 | 0.8767         | 0.9092      | 0.8927  | 0.8457    | 0.8701 | 0.8578 | 0.9573   |
| 0.0039        | 10.0  | 17290 | 0.3492          | 0.5700           | 0.5742        | 0.5721    | 0.9528        | 0.9642     | 0.9585 | 0.8825         | 0.9032      | 0.8927  | 0.85      | 0.8631 | 0.8565 | 0.9586   |
| 0.0019        | 11.0  | 19019 | 0.3629          | 0.5796           | 0.5771        | 0.5784    | 0.9502        | 0.9656     | 0.9579 | 0.8824         | 0.9077      | 0.8949  | 0.8516    | 0.8661 | 0.8588 | 0.9567   |
| 0.0007        | 12.0  | 20748 | 0.3660          | 0.5752           | 0.5786        | 0.5769    | 0.9489        | 0.9642     | 0.9565 | 0.8852         | 0.9039      | 0.8945  | 0.8507    | 0.8643 | 0.8574 | 0.9575   |


### Framework versions

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