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

Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
Inference Endpoints
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+ ---
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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-nl-fft-freeze2
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+ results: []
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+ ---
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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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+
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+ # detect-femicide-news-xlmr-nl-fft-freeze2
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+
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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: 0.4112
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+ - Accuracy: 0.8571
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+ - Precision Neg: 0.85
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+ - Precision Pos: 0.875
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+ - Recall Neg: 0.9444
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+ - Recall Pos: 0.7
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+ - F1 Score Neg: 0.8947
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+ - F1 Score Pos: 0.7778
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 24
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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: 100
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+
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+ ### Training results
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+
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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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+ | 1.2929 | 1.0 | 23 | 1.0782 | 0.75 | 0.7391 | 0.8 | 0.9444 | 0.4 | 0.8293 | 0.5333 |
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+ | 1.1345 | 2.0 | 46 | 0.8942 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.9799 | 3.0 | 69 | 0.7418 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.7871 | 4.0 | 92 | 0.5905 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.6852 | 5.0 | 115 | 0.4981 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.5988 | 6.0 | 138 | 0.4501 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.5976 | 7.0 | 161 | 0.4441 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.5877 | 8.0 | 184 | 0.4501 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.5621 | 9.0 | 207 | 0.4503 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.5658 | 10.0 | 230 | 0.4514 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.5648 | 11.0 | 253 | 0.4505 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.559 | 12.0 | 276 | 0.4499 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 |
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+ | 0.5668 | 13.0 | 299 | 0.4449 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5542 | 14.0 | 322 | 0.4448 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5496 | 15.0 | 345 | 0.4406 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.555 | 16.0 | 368 | 0.4392 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5479 | 17.0 | 391 | 0.4389 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5487 | 18.0 | 414 | 0.4345 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5529 | 19.0 | 437 | 0.4312 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5439 | 20.0 | 460 | 0.4314 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5444 | 21.0 | 483 | 0.4317 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5322 | 22.0 | 506 | 0.4299 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5314 | 23.0 | 529 | 0.4265 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5286 | 24.0 | 552 | 0.4245 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5395 | 25.0 | 575 | 0.4256 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5419 | 26.0 | 598 | 0.4253 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.55 | 27.0 | 621 | 0.4264 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5525 | 28.0 | 644 | 0.4261 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5465 | 29.0 | 667 | 0.4251 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5304 | 30.0 | 690 | 0.4277 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.541 | 31.0 | 713 | 0.4268 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5344 | 32.0 | 736 | 0.4262 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5316 | 33.0 | 759 | 0.4219 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5415 | 34.0 | 782 | 0.4244 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5419 | 35.0 | 805 | 0.4221 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5284 | 36.0 | 828 | 0.4206 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5472 | 37.0 | 851 | 0.4193 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5172 | 38.0 | 874 | 0.4185 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.522 | 39.0 | 897 | 0.4168 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5261 | 40.0 | 920 | 0.4172 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5246 | 41.0 | 943 | 0.4167 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5249 | 42.0 | 966 | 0.4164 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5229 | 43.0 | 989 | 0.4155 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5144 | 44.0 | 1012 | 0.4155 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.527 | 45.0 | 1035 | 0.4181 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.525 | 46.0 | 1058 | 0.4184 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5258 | 47.0 | 1081 | 0.4167 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5297 | 48.0 | 1104 | 0.4156 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5299 | 49.0 | 1127 | 0.4167 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5273 | 50.0 | 1150 | 0.4167 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5296 | 51.0 | 1173 | 0.4170 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5094 | 52.0 | 1196 | 0.4168 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5171 | 53.0 | 1219 | 0.4167 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5179 | 54.0 | 1242 | 0.4161 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5144 | 55.0 | 1265 | 0.4158 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5452 | 56.0 | 1288 | 0.4141 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5193 | 57.0 | 1311 | 0.4155 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5288 | 58.0 | 1334 | 0.4146 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5343 | 59.0 | 1357 | 0.4139 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5224 | 60.0 | 1380 | 0.4132 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5177 | 61.0 | 1403 | 0.4137 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5299 | 62.0 | 1426 | 0.4146 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5293 | 63.0 | 1449 | 0.4139 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5183 | 64.0 | 1472 | 0.4131 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5124 | 65.0 | 1495 | 0.4132 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5102 | 66.0 | 1518 | 0.4131 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.523 | 67.0 | 1541 | 0.4137 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5142 | 68.0 | 1564 | 0.4135 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5122 | 69.0 | 1587 | 0.4131 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5198 | 70.0 | 1610 | 0.4132 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5195 | 71.0 | 1633 | 0.4133 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5228 | 72.0 | 1656 | 0.4131 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5065 | 73.0 | 1679 | 0.4129 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5174 | 74.0 | 1702 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5189 | 75.0 | 1725 | 0.4119 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5243 | 76.0 | 1748 | 0.4119 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5102 | 77.0 | 1771 | 0.4128 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5318 | 78.0 | 1794 | 0.4130 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5214 | 79.0 | 1817 | 0.4128 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5163 | 80.0 | 1840 | 0.4133 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5235 | 81.0 | 1863 | 0.4128 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5393 | 82.0 | 1886 | 0.4131 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5284 | 83.0 | 1909 | 0.4128 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5242 | 84.0 | 1932 | 0.4122 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.505 | 85.0 | 1955 | 0.4120 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5211 | 86.0 | 1978 | 0.4120 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5367 | 87.0 | 2001 | 0.4122 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5068 | 88.0 | 2024 | 0.4122 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.51 | 89.0 | 2047 | 0.4119 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5302 | 90.0 | 2070 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5208 | 91.0 | 2093 | 0.4119 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5199 | 92.0 | 2116 | 0.4119 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5142 | 93.0 | 2139 | 0.4116 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5225 | 94.0 | 2162 | 0.4116 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5123 | 95.0 | 2185 | 0.4116 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5123 | 96.0 | 2208 | 0.4113 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.4929 | 97.0 | 2231 | 0.4114 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5067 | 98.0 | 2254 | 0.4112 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5254 | 99.0 | 2277 | 0.4112 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+ | 0.5131 | 100.0 | 2300 | 0.4112 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 |
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+
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+
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+ ### Framework versions
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+
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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