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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: detect-femicide-news-xlmr |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# detect-femicide-news-xlmr |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0161 |
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- Accuracy: 0.9973 |
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- Precision Neg: 0.9975 |
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- Precision Pos: 0.9967 |
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- Recall Neg: 0.9988 |
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- Recall Pos: 0.9933 |
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- F1 Score Neg: 0.9981 |
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- F1 Score Pos: 0.9950 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 8 |
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- seed: 1996 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Neg | Precision Pos | Recall Neg | Recall Pos | F1 Score Neg | F1 Score Pos | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:| |
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| 0.2758 | 1.0 | 204 | 0.1001 | 0.9718 | 0.9741 | 0.9654 | 0.9875 | 0.93 | 0.9808 | 0.9474 | |
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| 0.0782 | 2.0 | 408 | 0.0505 | 0.9809 | 0.9839 | 0.9729 | 0.99 | 0.9567 | 0.9869 | 0.9647 | |
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| 0.0501 | 3.0 | 612 | 0.0272 | 0.9927 | 0.9962 | 0.9834 | 0.9938 | 0.99 | 0.9950 | 0.9867 | |
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| 0.0389 | 4.0 | 816 | 0.0201 | 0.9945 | 0.9938 | 0.9966 | 0.9988 | 0.9833 | 0.9963 | 0.9899 | |
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| 0.031 | 5.0 | 1020 | 0.0175 | 0.9964 | 0.9963 | 0.9966 | 0.9988 | 0.99 | 0.9975 | 0.9933 | |
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| 0.0235 | 6.0 | 1224 | 0.0161 | 0.9973 | 0.9975 | 0.9967 | 0.9988 | 0.9933 | 0.9981 | 0.9950 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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