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# detect-femicide-news-bert-nl-None

This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8162
- Accuracy: 0.75
- Precision Neg: 0.8235
- Precision Pos: 0.6364
- Recall Neg: 0.7778
- Recall Pos: 0.7
- F1 Score Neg: 0.8000
- F1 Score Pos: 0.6667

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:|
| 0.6636        | 1.0   | 23   | 0.6474          | 0.6429   | 0.8333        | 0.5           | 0.5556     | 0.8        | 0.6667       | 0.6154       |
| 0.572         | 2.0   | 46   | 0.5653          | 0.6071   | 0.6842        | 0.4444        | 0.7222     | 0.4        | 0.7027       | 0.4211       |
| 0.502         | 3.0   | 69   | 0.5601          | 0.6786   | 0.8462        | 0.5333        | 0.6111     | 0.8        | 0.7097       | 0.64         |
| 0.4576        | 4.0   | 92   | 0.5199          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.3803        | 5.0   | 115  | 0.5219          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.3466        | 6.0   | 138  | 0.5125          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.3325        | 7.0   | 161  | 0.4930          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
| 0.3022        | 8.0   | 184  | 0.5144          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
| 0.2854        | 9.0   | 207  | 0.5588          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2797        | 10.0  | 230  | 0.5700          | 0.6786   | 0.7647        | 0.5455        | 0.7222     | 0.6        | 0.7429       | 0.5714       |
| 0.2645        | 11.0  | 253  | 0.5806          | 0.6786   | 0.7647        | 0.5455        | 0.7222     | 0.6        | 0.7429       | 0.5714       |
| 0.2411        | 12.0  | 276  | 0.5642          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2554        | 13.0  | 299  | 0.6364          | 0.6786   | 0.8           | 0.5385        | 0.6667     | 0.7        | 0.7273       | 0.6087       |
| 0.2682        | 14.0  | 322  | 0.5656          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2429        | 15.0  | 345  | 0.6249          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2368        | 16.0  | 368  | 0.5914          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2398        | 17.0  | 391  | 0.7456          | 0.6786   | 0.8462        | 0.5333        | 0.6111     | 0.8        | 0.7097       | 0.64         |
| 0.251         | 18.0  | 414  | 0.5602          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
| 0.2403        | 19.0  | 437  | 0.5803          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
| 0.2237        | 20.0  | 460  | 0.8165          | 0.6786   | 0.9091        | 0.5294        | 0.5556     | 0.9        | 0.6897       | 0.6667       |
| 0.2481        | 21.0  | 483  | 0.6195          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2357        | 22.0  | 506  | 0.7081          | 0.6429   | 0.75          | 0.5           | 0.6667     | 0.6        | 0.7059       | 0.5455       |
| 0.2227        | 23.0  | 529  | 0.6786          | 0.6786   | 0.8           | 0.5385        | 0.6667     | 0.7        | 0.7273       | 0.6087       |
| 0.2137        | 24.0  | 552  | 0.6567          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.2216        | 25.0  | 575  | 0.7286          | 0.7143   | 0.8571        | 0.5714        | 0.6667     | 0.8        | 0.75         | 0.6667       |
| 0.2289        | 26.0  | 598  | 0.6146          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2268        | 27.0  | 621  | 0.6721          | 0.6786   | 0.8           | 0.5385        | 0.6667     | 0.7        | 0.7273       | 0.6087       |
| 0.2208        | 28.0  | 644  | 0.6894          | 0.6786   | 0.8           | 0.5385        | 0.6667     | 0.7        | 0.7273       | 0.6087       |
| 0.2252        | 29.0  | 667  | 0.5986          | 0.7857   | 0.8           | 0.75          | 0.8889     | 0.6        | 0.8421       | 0.6667       |
| 0.2127        | 30.0  | 690  | 0.6868          | 0.6429   | 0.75          | 0.5           | 0.6667     | 0.6        | 0.7059       | 0.5455       |
| 0.2259        | 31.0  | 713  | 0.6682          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2253        | 32.0  | 736  | 0.8906          | 0.6786   | 0.9091        | 0.5294        | 0.5556     | 0.9        | 0.6897       | 0.6667       |
| 0.2421        | 33.0  | 759  | 0.6461          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2181        | 34.0  | 782  | 0.7014          | 0.6786   | 0.7647        | 0.5455        | 0.7222     | 0.6        | 0.7429       | 0.5714       |
| 0.2199        | 35.0  | 805  | 0.7655          | 0.6786   | 0.8           | 0.5385        | 0.6667     | 0.7        | 0.7273       | 0.6087       |
| 0.201         | 36.0  | 828  | 0.7356          | 0.6429   | 0.75          | 0.5           | 0.6667     | 0.6        | 0.7059       | 0.5455       |
| 0.2192        | 37.0  | 851  | 0.6958          | 0.6786   | 0.7647        | 0.5455        | 0.7222     | 0.6        | 0.7429       | 0.5714       |
| 0.2164        | 38.0  | 874  | 0.7475          | 0.6429   | 0.75          | 0.5           | 0.6667     | 0.6        | 0.7059       | 0.5455       |
| 0.22          | 39.0  | 897  | 0.6847          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2177        | 40.0  | 920  | 0.6463          | 0.7857   | 0.8           | 0.75          | 0.8889     | 0.6        | 0.8421       | 0.6667       |
| 0.2126        | 41.0  | 943  | 0.6793          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2069        | 42.0  | 966  | 0.7303          | 0.7143   | 0.8125        | 0.5833        | 0.7222     | 0.7        | 0.7647       | 0.6364       |
| 0.2099        | 43.0  | 989  | 0.6598          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2104        | 44.0  | 1012 | 0.7276          | 0.6786   | 0.7647        | 0.5455        | 0.7222     | 0.6        | 0.7429       | 0.5714       |
| 0.213         | 45.0  | 1035 | 0.7099          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2083        | 46.0  | 1058 | 0.7545          | 0.6786   | 0.8           | 0.5385        | 0.6667     | 0.7        | 0.7273       | 0.6087       |
| 0.1958        | 47.0  | 1081 | 0.6533          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
| 0.2096        | 48.0  | 1104 | 0.7141          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2134        | 49.0  | 1127 | 0.7008          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.203         | 50.0  | 1150 | 0.6557          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
| 0.2024        | 51.0  | 1173 | 0.7348          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.2095        | 52.0  | 1196 | 0.7708          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1997        | 53.0  | 1219 | 0.7106          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2048        | 54.0  | 1242 | 0.7530          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1963        | 55.0  | 1265 | 0.7520          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2039        | 56.0  | 1288 | 0.7230          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2023        | 57.0  | 1311 | 0.7644          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.2022        | 58.0  | 1334 | 0.7666          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1898        | 59.0  | 1357 | 0.7961          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.2155        | 60.0  | 1380 | 0.7763          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1948        | 61.0  | 1403 | 0.7545          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.2124        | 62.0  | 1426 | 0.7344          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1979        | 63.0  | 1449 | 0.7676          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1958        | 64.0  | 1472 | 0.7567          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1946        | 65.0  | 1495 | 0.7349          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1888        | 66.0  | 1518 | 0.7472          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1889        | 67.0  | 1541 | 0.7202          | 0.7857   | 0.8           | 0.75          | 0.8889     | 0.6        | 0.8421       | 0.6667       |
| 0.2077        | 68.0  | 1564 | 0.7193          | 0.7857   | 0.8           | 0.75          | 0.8889     | 0.6        | 0.8421       | 0.6667       |
| 0.1882        | 69.0  | 1587 | 0.7541          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1903        | 70.0  | 1610 | 0.8058          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.2017        | 71.0  | 1633 | 0.7862          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1929        | 72.0  | 1656 | 0.8000          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.192         | 73.0  | 1679 | 0.8199          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1903        | 74.0  | 1702 | 0.8044          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1953        | 75.0  | 1725 | 0.7943          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1908        | 76.0  | 1748 | 0.7805          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1975        | 77.0  | 1771 | 0.7595          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1943        | 78.0  | 1794 | 0.7908          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.192         | 79.0  | 1817 | 0.8389          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1879        | 80.0  | 1840 | 0.7925          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1933        | 81.0  | 1863 | 0.8149          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1867        | 82.0  | 1886 | 0.7925          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1906        | 83.0  | 1909 | 0.8118          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1895        | 84.0  | 1932 | 0.8108          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1925        | 85.0  | 1955 | 0.7962          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1851        | 86.0  | 1978 | 0.7942          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
| 0.1952        | 87.0  | 2001 | 0.8104          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1821        | 88.0  | 2024 | 0.8187          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1946        | 89.0  | 2047 | 0.8378          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1904        | 90.0  | 2070 | 0.8407          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1931        | 91.0  | 2093 | 0.8351          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1883        | 92.0  | 2116 | 0.8269          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1845        | 93.0  | 2139 | 0.8110          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1883        | 94.0  | 2162 | 0.8209          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1991        | 95.0  | 2185 | 0.8194          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.187         | 96.0  | 2208 | 0.8182          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1842        | 97.0  | 2231 | 0.8168          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1837        | 98.0  | 2254 | 0.8164          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.1849        | 99.0  | 2277 | 0.8173          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |
| 0.189         | 100.0 | 2300 | 0.8162          | 0.75     | 0.8235        | 0.6364        | 0.7778     | 0.7        | 0.8000       | 0.6667       |


### Framework versions

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

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "detect-femicide-news-bert-nl-None", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

detect-femicide-news-bert-nl-None
=================================


This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8162
* Accuracy: 0.75
* Precision Neg: 0.8235
* Precision Pos: 0.6364
* Recall Neg: 0.7778
* Recall Pos: 0.7
* F1 Score Neg: 0.8000
* F1 Score Pos: 0.6667


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 #bert #text-classification #generated_from_trainer #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 #bert #text-classification #generated_from_trainer #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