File size: 2,488 Bytes
964e532 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
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
|