
<!-- 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-nl-mono-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: 0.6487
- Accuracy: 0.6429
- Precision Neg: 0.6429
- Precision Pos: 0.0
- Recall Neg: 1.0
- Recall Pos: 0.0
- F1 Score Neg: 0.7826
- F1 Score Pos: 0.0

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:|
| 0.7312        | 1.0   | 23   | 0.7413          | 0.3571   | 0.0           | 0.3571        | 0.0        | 1.0        | 0.0          | 0.5263       |
| 0.7151        | 2.0   | 46   | 0.7177          | 0.3571   | 0.0           | 0.3571        | 0.0        | 1.0        | 0.0          | 0.5263       |
| 0.7049        | 3.0   | 69   | 0.6988          | 0.3571   | 0.0           | 0.3571        | 0.0        | 1.0        | 0.0          | 0.5263       |
| 0.6934        | 4.0   | 92   | 0.6945          | 0.3571   | 0.0           | 0.3571        | 0.0        | 1.0        | 0.0          | 0.5263       |
| 0.6886        | 5.0   | 115  | 0.6903          | 0.6071   | 0.8182        | 0.4706        | 0.5        | 0.8        | 0.6207       | 0.5926       |
| 0.6911        | 6.0   | 138  | 0.6846          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6856        | 7.0   | 161  | 0.6786          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6888        | 8.0   | 184  | 0.6783          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6862        | 9.0   | 207  | 0.6819          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6807        | 10.0  | 230  | 0.6758          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6839        | 11.0  | 253  | 0.6721          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6878        | 12.0  | 276  | 0.6708          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6799        | 13.0  | 299  | 0.6692          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6813        | 14.0  | 322  | 0.6673          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6792        | 15.0  | 345  | 0.6676          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6774        | 16.0  | 368  | 0.6683          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6807        | 17.0  | 391  | 0.6679          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6834        | 18.0  | 414  | 0.6693          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6784        | 19.0  | 437  | 0.6679          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.676         | 20.0  | 460  | 0.6698          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6791        | 21.0  | 483  | 0.6661          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6775        | 22.0  | 506  | 0.6633          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6688        | 23.0  | 529  | 0.6589          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6748        | 24.0  | 552  | 0.6580          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6771        | 25.0  | 575  | 0.6619          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6761        | 26.0  | 598  | 0.6639          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6773        | 27.0  | 621  | 0.6651          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6737        | 28.0  | 644  | 0.6656          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6721        | 29.0  | 667  | 0.6650          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6683        | 30.0  | 690  | 0.6612          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6663        | 31.0  | 713  | 0.6592          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6724        | 32.0  | 736  | 0.6576          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6739        | 33.0  | 759  | 0.6601          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6691        | 34.0  | 782  | 0.6602          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6652        | 35.0  | 805  | 0.6588          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6717        | 36.0  | 828  | 0.6596          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6637        | 37.0  | 851  | 0.6587          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6704        | 38.0  | 874  | 0.6579          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6608        | 39.0  | 897  | 0.6599          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6615        | 40.0  | 920  | 0.6580          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6662        | 41.0  | 943  | 0.6592          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6622        | 42.0  | 966  | 0.6616          | 0.6071   | 0.6296        | 0.0           | 0.9444     | 0.0        | 0.7556       | 0.0          |
| 0.664         | 43.0  | 989  | 0.6610          | 0.6071   | 0.6296        | 0.0           | 0.9444     | 0.0        | 0.7556       | 0.0          |
| 0.6695        | 44.0  | 1012 | 0.6570          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6677        | 45.0  | 1035 | 0.6557          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6705        | 46.0  | 1058 | 0.6546          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6591        | 47.0  | 1081 | 0.6547          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6675        | 48.0  | 1104 | 0.6532          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6622        | 49.0  | 1127 | 0.6544          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6571        | 50.0  | 1150 | 0.6552          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6678        | 51.0  | 1173 | 0.6555          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6596        | 52.0  | 1196 | 0.6544          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6583        | 53.0  | 1219 | 0.6517          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6641        | 54.0  | 1242 | 0.6508          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.671         | 55.0  | 1265 | 0.6502          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6645        | 56.0  | 1288 | 0.6513          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6604        | 57.0  | 1311 | 0.6510          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6644        | 58.0  | 1334 | 0.6509          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6617        | 59.0  | 1357 | 0.6528          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6608        | 60.0  | 1380 | 0.6536          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6533        | 61.0  | 1403 | 0.6533          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6596        | 62.0  | 1426 | 0.6518          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6607        | 63.0  | 1449 | 0.6511          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.658         | 64.0  | 1472 | 0.6509          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6546        | 65.0  | 1495 | 0.6514          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6613        | 66.0  | 1518 | 0.6516          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.662         | 67.0  | 1541 | 0.6506          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.661         | 68.0  | 1564 | 0.6503          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6571        | 69.0  | 1587 | 0.6497          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6656        | 70.0  | 1610 | 0.6500          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6637        | 71.0  | 1633 | 0.6508          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6519        | 72.0  | 1656 | 0.6518          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6593        | 73.0  | 1679 | 0.6516          | 0.6071   | 0.6296        | 0.0           | 0.9444     | 0.0        | 0.7556       | 0.0          |
| 0.6539        | 74.0  | 1702 | 0.6514          | 0.6071   | 0.6296        | 0.0           | 0.9444     | 0.0        | 0.7556       | 0.0          |
| 0.6568        | 75.0  | 1725 | 0.6506          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6581        | 76.0  | 1748 | 0.6504          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6557        | 77.0  | 1771 | 0.6499          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6542        | 78.0  | 1794 | 0.6500          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6557        | 79.0  | 1817 | 0.6498          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6637        | 80.0  | 1840 | 0.6493          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6603        | 81.0  | 1863 | 0.6490          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6568        | 82.0  | 1886 | 0.6485          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6561        | 83.0  | 1909 | 0.6490          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6665        | 84.0  | 1932 | 0.6499          | 0.6071   | 0.6296        | 0.0           | 0.9444     | 0.0        | 0.7556       | 0.0          |
| 0.655         | 85.0  | 1955 | 0.6492          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6509        | 86.0  | 1978 | 0.6493          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6549        | 87.0  | 2001 | 0.6493          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.655         | 88.0  | 2024 | 0.6489          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6576        | 89.0  | 2047 | 0.6493          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6612        | 90.0  | 2070 | 0.6492          | 0.6071   | 0.6296        | 0.0           | 0.9444     | 0.0        | 0.7556       | 0.0          |
| 0.6641        | 91.0  | 2093 | 0.6492          | 0.6071   | 0.6296        | 0.0           | 0.9444     | 0.0        | 0.7556       | 0.0          |
| 0.654         | 92.0  | 2116 | 0.6487          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6556        | 93.0  | 2139 | 0.6488          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6566        | 94.0  | 2162 | 0.6486          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6565        | 95.0  | 2185 | 0.6487          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6516        | 96.0  | 2208 | 0.6488          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6509        | 97.0  | 2231 | 0.6487          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6588        | 98.0  | 2254 | 0.6487          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6532        | 99.0  | 2277 | 0.6487          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |
| 0.6548        | 100.0 | 2300 | 0.6487          | 0.6429   | 0.6429        | 0.0           | 1.0        | 0.0        | 0.7826       | 0.0          |


### 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-mono-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-mono-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: 0.6487
* Accuracy: 0.6429
* Precision Neg: 0.6429
* Precision Pos: 0.0
* Recall Neg: 1.0
* Recall Pos: 0.0
* F1 Score Neg: 0.7826
* F1 Score Pos: 0.0


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