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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: xlm-roberta-base-finetuned-ANAD-mlm-0.15-base-25OCT |
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results: [] |
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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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# xlm-roberta-base-finetuned-ANAD-mlm-0.15-base-25OCT |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5193 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 256 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| No log | 0.0941 | 100 | 1.9039 | |
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| No log | 0.1881 | 200 | 1.8793 | |
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| No log | 0.2822 | 300 | 1.8643 | |
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| No log | 0.3763 | 400 | 1.8479 | |
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| 2.0696 | 0.4703 | 500 | 1.8380 | |
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| 2.0696 | 0.5644 | 600 | 1.8336 | |
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| 2.0696 | 0.6585 | 700 | 1.8226 | |
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| 2.0696 | 0.7525 | 800 | 1.8231 | |
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| 2.0696 | 0.8466 | 900 | 1.8136 | |
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| 2.0049 | 0.9407 | 1000 | 1.8161 | |
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| 2.0049 | 1.0347 | 1100 | 1.8056 | |
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| 2.0049 | 1.1288 | 1200 | 1.7934 | |
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| 2.0049 | 1.2229 | 1300 | 1.7887 | |
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| 2.0049 | 1.3169 | 1400 | 1.7749 | |
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| 1.9612 | 1.4110 | 1500 | 1.7726 | |
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| 1.9612 | 1.5051 | 1600 | 1.7679 | |
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| 1.9612 | 1.5992 | 1700 | 1.7543 | |
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| 1.9612 | 1.6932 | 1800 | 1.7473 | |
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| 1.9612 | 1.7873 | 1900 | 1.7413 | |
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| 1.911 | 1.8814 | 2000 | 1.7334 | |
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| 1.911 | 1.9754 | 2100 | 1.7302 | |
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| 1.911 | 2.0695 | 2200 | 1.7172 | |
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| 1.911 | 2.1636 | 2300 | 1.7187 | |
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| 1.911 | 2.2576 | 2400 | 1.7076 | |
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| 1.8628 | 2.3517 | 2500 | 1.7011 | |
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| 1.8628 | 2.4458 | 2600 | 1.7001 | |
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| 1.8628 | 2.5398 | 2700 | 1.6929 | |
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| 1.8628 | 2.6339 | 2800 | 1.6929 | |
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| 1.8628 | 2.7280 | 2900 | 1.6848 | |
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| 1.8328 | 2.8220 | 3000 | 1.6804 | |
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| 1.8328 | 2.9161 | 3100 | 1.6762 | |
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| 1.8328 | 3.0102 | 3200 | 1.6759 | |
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| 1.8328 | 3.1042 | 3300 | 1.6715 | |
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| 1.8328 | 3.1983 | 3400 | 1.6653 | |
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| 1.8018 | 3.2924 | 3500 | 1.6590 | |
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| 1.8018 | 3.3864 | 3600 | 1.6519 | |
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| 1.8018 | 3.4805 | 3700 | 1.6493 | |
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| 1.8018 | 3.5746 | 3800 | 1.6458 | |
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| 1.8018 | 3.6686 | 3900 | 1.6415 | |
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| 1.7708 | 3.7627 | 4000 | 1.6397 | |
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| 1.7708 | 3.8568 | 4100 | 1.6345 | |
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| 1.7708 | 3.9508 | 4200 | 1.6351 | |
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| 1.7708 | 4.0449 | 4300 | 1.6324 | |
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| 1.7708 | 4.1390 | 4400 | 1.6271 | |
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| 1.7501 | 4.2331 | 4500 | 1.6253 | |
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| 1.7501 | 4.3271 | 4600 | 1.6248 | |
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| 1.7501 | 4.4212 | 4700 | 1.6153 | |
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| 1.7501 | 4.5153 | 4800 | 1.6191 | |
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| 1.7501 | 4.6093 | 4900 | 1.6135 | |
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| 1.7283 | 4.7034 | 5000 | 1.6087 | |
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| 1.7283 | 4.7975 | 5100 | 1.6072 | |
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| 1.7283 | 4.8915 | 5200 | 1.5991 | |
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| 1.7283 | 4.9856 | 5300 | 1.6026 | |
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| 1.7283 | 5.0797 | 5400 | 1.5989 | |
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| 1.7105 | 5.1737 | 5500 | 1.6011 | |
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| 1.7105 | 5.2678 | 5600 | 1.5958 | |
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| 1.7105 | 5.3619 | 5700 | 1.5894 | |
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| 1.7105 | 5.4559 | 5800 | 1.5871 | |
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| 1.7105 | 5.5500 | 5900 | 1.5865 | |
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| 1.6816 | 5.6441 | 6000 | 1.5871 | |
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| 1.6816 | 5.7381 | 6100 | 1.5840 | |
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| 1.6816 | 5.8322 | 6200 | 1.5842 | |
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| 1.6816 | 5.9263 | 6300 | 1.5772 | |
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| 1.6816 | 6.0203 | 6400 | 1.5769 | |
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| 1.6745 | 6.1144 | 6500 | 1.5740 | |
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| 1.6745 | 6.2085 | 6600 | 1.5690 | |
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| 1.6745 | 6.3025 | 6700 | 1.5700 | |
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| 1.6745 | 6.3966 | 6800 | 1.5704 | |
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| 1.6745 | 6.4907 | 6900 | 1.5667 | |
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| 1.6639 | 6.5847 | 7000 | 1.5653 | |
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| 1.6639 | 6.6788 | 7100 | 1.5647 | |
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| 1.6639 | 6.7729 | 7200 | 1.5625 | |
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| 1.6639 | 6.8670 | 7300 | 1.5572 | |
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| 1.6639 | 6.9610 | 7400 | 1.5551 | |
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| 1.6509 | 7.0551 | 7500 | 1.5533 | |
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| 1.6509 | 7.1492 | 7600 | 1.5522 | |
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| 1.6509 | 7.2432 | 7700 | 1.5509 | |
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| 1.6509 | 7.3373 | 7800 | 1.5468 | |
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| 1.6509 | 7.4314 | 7900 | 1.5488 | |
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| 1.6344 | 7.5254 | 8000 | 1.5459 | |
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| 1.6344 | 7.6195 | 8100 | 1.5463 | |
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| 1.6344 | 7.7136 | 8200 | 1.5452 | |
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| 1.6344 | 7.8076 | 8300 | 1.5407 | |
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| 1.6344 | 7.9017 | 8400 | 1.5416 | |
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| 1.6281 | 7.9958 | 8500 | 1.5400 | |
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| 1.6281 | 8.0898 | 8600 | 1.5372 | |
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| 1.6281 | 8.1839 | 8700 | 1.5350 | |
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| 1.6281 | 8.2780 | 8800 | 1.5341 | |
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| 1.6281 | 8.3720 | 8900 | 1.5345 | |
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| 1.6132 | 8.4661 | 9000 | 1.5325 | |
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| 1.6132 | 8.5602 | 9100 | 1.5293 | |
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| 1.6132 | 8.6542 | 9200 | 1.5288 | |
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| 1.6132 | 8.7483 | 9300 | 1.5280 | |
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| 1.6132 | 8.8424 | 9400 | 1.5287 | |
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| 1.6123 | 8.9364 | 9500 | 1.5272 | |
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| 1.6123 | 9.0305 | 9600 | 1.5255 | |
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| 1.6123 | 9.1246 | 9700 | 1.5251 | |
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| 1.6123 | 9.2186 | 9800 | 1.5233 | |
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| 1.6123 | 9.3127 | 9900 | 1.5221 | |
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| 1.5993 | 9.4068 | 10000 | 1.5223 | |
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| 1.5993 | 9.5009 | 10100 | 1.5216 | |
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| 1.5993 | 9.5949 | 10200 | 1.5215 | |
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| 1.5993 | 9.6890 | 10300 | 1.5207 | |
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| 1.5993 | 9.7831 | 10400 | 1.5204 | |
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| 1.5959 | 9.8771 | 10500 | 1.5198 | |
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| 1.5959 | 9.9712 | 10600 | 1.5193 | |
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### Framework versions |
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- Transformers 4.43.4 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 3.0.2 |
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- Tokenizers 0.19.1 |
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