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# detect-femicide-news-xlmr-nl-fft-freeze2

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: 0.4119
- Accuracy: 0.8571
- Precision Neg: 0.85
- Precision Pos: 0.875
- Recall Neg: 0.9444
- Recall Pos: 0.7
- F1 Score Neg: 0.8947
- F1 Score Pos: 0.7778

## 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: 24
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Neg | Precision Pos | Recall Neg | Recall Pos | F1 Score Neg | F1 Score Pos |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:|
| 1.3215        | 1.0   | 23   | 1.0782          | 0.75     | 0.7391        | 0.8           | 0.9444     | 0.4        | 0.8293       | 0.5333       |
| 1.0955        | 2.0   | 46   | 0.9057          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.9344        | 3.0   | 69   | 0.7420          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.8303        | 4.0   | 92   | 0.5952          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.6728        | 5.0   | 115  | 0.5078          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.649         | 6.0   | 138  | 0.4546          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.6008        | 7.0   | 161  | 0.4454          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.5439        | 8.0   | 184  | 0.4495          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.5557        | 9.0   | 207  | 0.4479          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.5637        | 10.0  | 230  | 0.4470          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.5709        | 11.0  | 253  | 0.4500          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.5496        | 12.0  | 276  | 0.4456          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5586        | 13.0  | 299  | 0.4484          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
| 0.562         | 14.0  | 322  | 0.4435          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5555        | 15.0  | 345  | 0.4427          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5449        | 16.0  | 368  | 0.4404          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.555         | 17.0  | 391  | 0.4384          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5541        | 18.0  | 414  | 0.4383          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5463        | 19.0  | 437  | 0.4379          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5548        | 20.0  | 460  | 0.4357          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5365        | 21.0  | 483  | 0.4342          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5473        | 22.0  | 506  | 0.4308          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5467        | 23.0  | 529  | 0.4309          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.543         | 24.0  | 552  | 0.4312          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.543         | 25.0  | 575  | 0.4289          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5309        | 26.0  | 598  | 0.4290          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5406        | 27.0  | 621  | 0.4246          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5295        | 28.0  | 644  | 0.4248          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.535         | 29.0  | 667  | 0.4247          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5401        | 30.0  | 690  | 0.4265          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5285        | 31.0  | 713  | 0.4262          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5492        | 32.0  | 736  | 0.4247          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5473        | 33.0  | 759  | 0.4224          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.547         | 34.0  | 782  | 0.4250          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5394        | 35.0  | 805  | 0.4280          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5361        | 36.0  | 828  | 0.4247          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5294        | 37.0  | 851  | 0.4238          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5302        | 38.0  | 874  | 0.4236          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5384        | 39.0  | 897  | 0.4215          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5396        | 40.0  | 920  | 0.4209          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5305        | 41.0  | 943  | 0.4192          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5241        | 42.0  | 966  | 0.4204          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5433        | 43.0  | 989  | 0.4190          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5246        | 44.0  | 1012 | 0.4169          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.525         | 45.0  | 1035 | 0.4177          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5306        | 46.0  | 1058 | 0.4169          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5228        | 47.0  | 1081 | 0.4167          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5094        | 48.0  | 1104 | 0.4176          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5207        | 49.0  | 1127 | 0.4170          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5087        | 50.0  | 1150 | 0.4169          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5229        | 51.0  | 1173 | 0.4163          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5221        | 52.0  | 1196 | 0.4160          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5147        | 53.0  | 1219 | 0.4166          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.524         | 54.0  | 1242 | 0.4157          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5171        | 55.0  | 1265 | 0.4149          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5116        | 56.0  | 1288 | 0.4138          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5373        | 57.0  | 1311 | 0.4139          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5274        | 58.0  | 1334 | 0.4131          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5375        | 59.0  | 1357 | 0.4133          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.528         | 60.0  | 1380 | 0.4136          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5282        | 61.0  | 1403 | 0.4147          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.528         | 62.0  | 1426 | 0.4142          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5357        | 63.0  | 1449 | 0.4132          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5177        | 64.0  | 1472 | 0.4132          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5358        | 65.0  | 1495 | 0.4133          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5224        | 66.0  | 1518 | 0.4124          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5121        | 67.0  | 1541 | 0.4125          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5394        | 68.0  | 1564 | 0.4137          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.52          | 69.0  | 1587 | 0.4140          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5103        | 70.0  | 1610 | 0.4131          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5224        | 71.0  | 1633 | 0.4134          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5351        | 72.0  | 1656 | 0.4129          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5181        | 73.0  | 1679 | 0.4138          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.532         | 74.0  | 1702 | 0.4139          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5216        | 75.0  | 1725 | 0.4142          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5083        | 76.0  | 1748 | 0.4138          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.531         | 77.0  | 1771 | 0.4132          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5245        | 78.0  | 1794 | 0.4125          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5191        | 79.0  | 1817 | 0.4127          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.516         | 80.0  | 1840 | 0.4126          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5098        | 81.0  | 1863 | 0.4128          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5173        | 82.0  | 1886 | 0.4127          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5119        | 83.0  | 1909 | 0.4129          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5296        | 84.0  | 1932 | 0.4125          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5105        | 85.0  | 1955 | 0.4131          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5108        | 86.0  | 1978 | 0.4124          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5156        | 87.0  | 2001 | 0.4125          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5143        | 88.0  | 2024 | 0.4124          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5099        | 89.0  | 2047 | 0.4122          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5163        | 90.0  | 2070 | 0.4120          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5224        | 91.0  | 2093 | 0.4118          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.4936        | 92.0  | 2116 | 0.4120          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5236        | 93.0  | 2139 | 0.4118          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5261        | 94.0  | 2162 | 0.4118          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5134        | 95.0  | 2185 | 0.4119          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5064        | 96.0  | 2208 | 0.4118          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5072        | 97.0  | 2231 | 0.4118          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5264        | 98.0  | 2254 | 0.4118          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.5344        | 99.0  | 2277 | 0.4118          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
| 0.522         | 100.0 | 2300 | 0.4119          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "detect-femicide-news-xlmr-nl-fft-freeze2", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

detect-femicide-news-xlmr-nl-fft-freeze2
========================================


This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4119
* Accuracy: 0.8571
* Precision Neg: 0.85
* Precision Pos: 0.875
* Recall Neg: 0.9444
* Recall Pos: 0.7
* F1 Score Neg: 0.8947
* F1 Score Pos: 0.7778


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: 24
* eval\_batch\_size: 8
* seed: 1996
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 24
* eval\_batch\_size: 8
* seed: 1996
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2+cu113
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 24
* eval\_batch\_size: 8
* seed: 1996
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2+cu113
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 24
* eval\_batch\_size: 8
* seed: 1996
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100### Training results### Framework versions


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