---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: detect-femicide-news-xlmr
  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. -->

# detect-femicide-news-xlmr

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: 0.0161
- Accuracy: 0.9973
- Precision Neg: 0.9975
- Precision Pos: 0.9967
- Recall Neg: 0.9988
- Recall Pos: 0.9933
- F1 Score Neg: 0.9981
- F1 Score Pos: 0.9950

## 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: 1e-05
- train_batch_size: 128
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Neg | Precision Pos | Recall Neg | Recall Pos | F1 Score Neg | F1 Score Pos |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:|
| 0.2758        | 1.0   | 204  | 0.1001          | 0.9718   | 0.9741        | 0.9654        | 0.9875     | 0.93       | 0.9808       | 0.9474       |
| 0.0782        | 2.0   | 408  | 0.0505          | 0.9809   | 0.9839        | 0.9729        | 0.99       | 0.9567     | 0.9869       | 0.9647       |
| 0.0501        | 3.0   | 612  | 0.0272          | 0.9927   | 0.9962        | 0.9834        | 0.9938     | 0.99       | 0.9950       | 0.9867       |
| 0.0389        | 4.0   | 816  | 0.0201          | 0.9945   | 0.9938        | 0.9966        | 0.9988     | 0.9833     | 0.9963       | 0.9899       |
| 0.031         | 5.0   | 1020 | 0.0175          | 0.9964   | 0.9963        | 0.9966        | 0.9988     | 0.99       | 0.9975       | 0.9933       |
| 0.0235        | 6.0   | 1224 | 0.0161          | 0.9973   | 0.9975        | 0.9967        | 0.9988     | 0.9933     | 0.9981       | 0.9950       |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
