metadata
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-ANAD-mlm-0.15-base-25OCT
results: []
xlm-roberta-base-finetuned-ANAD-mlm-0.15-base-25OCT
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5193
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0941 | 100 | 1.9039 |
| No log | 0.1881 | 200 | 1.8793 |
| No log | 0.2822 | 300 | 1.8643 |
| No log | 0.3763 | 400 | 1.8479 |
| 2.0696 | 0.4703 | 500 | 1.8380 |
| 2.0696 | 0.5644 | 600 | 1.8336 |
| 2.0696 | 0.6585 | 700 | 1.8226 |
| 2.0696 | 0.7525 | 800 | 1.8231 |
| 2.0696 | 0.8466 | 900 | 1.8136 |
| 2.0049 | 0.9407 | 1000 | 1.8161 |
| 2.0049 | 1.0347 | 1100 | 1.8056 |
| 2.0049 | 1.1288 | 1200 | 1.7934 |
| 2.0049 | 1.2229 | 1300 | 1.7887 |
| 2.0049 | 1.3169 | 1400 | 1.7749 |
| 1.9612 | 1.4110 | 1500 | 1.7726 |
| 1.9612 | 1.5051 | 1600 | 1.7679 |
| 1.9612 | 1.5992 | 1700 | 1.7543 |
| 1.9612 | 1.6932 | 1800 | 1.7473 |
| 1.9612 | 1.7873 | 1900 | 1.7413 |
| 1.911 | 1.8814 | 2000 | 1.7334 |
| 1.911 | 1.9754 | 2100 | 1.7302 |
| 1.911 | 2.0695 | 2200 | 1.7172 |
| 1.911 | 2.1636 | 2300 | 1.7187 |
| 1.911 | 2.2576 | 2400 | 1.7076 |
| 1.8628 | 2.3517 | 2500 | 1.7011 |
| 1.8628 | 2.4458 | 2600 | 1.7001 |
| 1.8628 | 2.5398 | 2700 | 1.6929 |
| 1.8628 | 2.6339 | 2800 | 1.6929 |
| 1.8628 | 2.7280 | 2900 | 1.6848 |
| 1.8328 | 2.8220 | 3000 | 1.6804 |
| 1.8328 | 2.9161 | 3100 | 1.6762 |
| 1.8328 | 3.0102 | 3200 | 1.6759 |
| 1.8328 | 3.1042 | 3300 | 1.6715 |
| 1.8328 | 3.1983 | 3400 | 1.6653 |
| 1.8018 | 3.2924 | 3500 | 1.6590 |
| 1.8018 | 3.3864 | 3600 | 1.6519 |
| 1.8018 | 3.4805 | 3700 | 1.6493 |
| 1.8018 | 3.5746 | 3800 | 1.6458 |
| 1.8018 | 3.6686 | 3900 | 1.6415 |
| 1.7708 | 3.7627 | 4000 | 1.6397 |
| 1.7708 | 3.8568 | 4100 | 1.6345 |
| 1.7708 | 3.9508 | 4200 | 1.6351 |
| 1.7708 | 4.0449 | 4300 | 1.6324 |
| 1.7708 | 4.1390 | 4400 | 1.6271 |
| 1.7501 | 4.2331 | 4500 | 1.6253 |
| 1.7501 | 4.3271 | 4600 | 1.6248 |
| 1.7501 | 4.4212 | 4700 | 1.6153 |
| 1.7501 | 4.5153 | 4800 | 1.6191 |
| 1.7501 | 4.6093 | 4900 | 1.6135 |
| 1.7283 | 4.7034 | 5000 | 1.6087 |
| 1.7283 | 4.7975 | 5100 | 1.6072 |
| 1.7283 | 4.8915 | 5200 | 1.5991 |
| 1.7283 | 4.9856 | 5300 | 1.6026 |
| 1.7283 | 5.0797 | 5400 | 1.5989 |
| 1.7105 | 5.1737 | 5500 | 1.6011 |
| 1.7105 | 5.2678 | 5600 | 1.5958 |
| 1.7105 | 5.3619 | 5700 | 1.5894 |
| 1.7105 | 5.4559 | 5800 | 1.5871 |
| 1.7105 | 5.5500 | 5900 | 1.5865 |
| 1.6816 | 5.6441 | 6000 | 1.5871 |
| 1.6816 | 5.7381 | 6100 | 1.5840 |
| 1.6816 | 5.8322 | 6200 | 1.5842 |
| 1.6816 | 5.9263 | 6300 | 1.5772 |
| 1.6816 | 6.0203 | 6400 | 1.5769 |
| 1.6745 | 6.1144 | 6500 | 1.5740 |
| 1.6745 | 6.2085 | 6600 | 1.5690 |
| 1.6745 | 6.3025 | 6700 | 1.5700 |
| 1.6745 | 6.3966 | 6800 | 1.5704 |
| 1.6745 | 6.4907 | 6900 | 1.5667 |
| 1.6639 | 6.5847 | 7000 | 1.5653 |
| 1.6639 | 6.6788 | 7100 | 1.5647 |
| 1.6639 | 6.7729 | 7200 | 1.5625 |
| 1.6639 | 6.8670 | 7300 | 1.5572 |
| 1.6639 | 6.9610 | 7400 | 1.5551 |
| 1.6509 | 7.0551 | 7500 | 1.5533 |
| 1.6509 | 7.1492 | 7600 | 1.5522 |
| 1.6509 | 7.2432 | 7700 | 1.5509 |
| 1.6509 | 7.3373 | 7800 | 1.5468 |
| 1.6509 | 7.4314 | 7900 | 1.5488 |
| 1.6344 | 7.5254 | 8000 | 1.5459 |
| 1.6344 | 7.6195 | 8100 | 1.5463 |
| 1.6344 | 7.7136 | 8200 | 1.5452 |
| 1.6344 | 7.8076 | 8300 | 1.5407 |
| 1.6344 | 7.9017 | 8400 | 1.5416 |
| 1.6281 | 7.9958 | 8500 | 1.5400 |
| 1.6281 | 8.0898 | 8600 | 1.5372 |
| 1.6281 | 8.1839 | 8700 | 1.5350 |
| 1.6281 | 8.2780 | 8800 | 1.5341 |
| 1.6281 | 8.3720 | 8900 | 1.5345 |
| 1.6132 | 8.4661 | 9000 | 1.5325 |
| 1.6132 | 8.5602 | 9100 | 1.5293 |
| 1.6132 | 8.6542 | 9200 | 1.5288 |
| 1.6132 | 8.7483 | 9300 | 1.5280 |
| 1.6132 | 8.8424 | 9400 | 1.5287 |
| 1.6123 | 8.9364 | 9500 | 1.5272 |
| 1.6123 | 9.0305 | 9600 | 1.5255 |
| 1.6123 | 9.1246 | 9700 | 1.5251 |
| 1.6123 | 9.2186 | 9800 | 1.5233 |
| 1.6123 | 9.3127 | 9900 | 1.5221 |
| 1.5993 | 9.4068 | 10000 | 1.5223 |
| 1.5993 | 9.5009 | 10100 | 1.5216 |
| 1.5993 | 9.5949 | 10200 | 1.5215 |
| 1.5993 | 9.6890 | 10300 | 1.5207 |
| 1.5993 | 9.7831 | 10400 | 1.5204 |
| 1.5959 | 9.8771 | 10500 | 1.5198 |
| 1.5959 | 9.9712 | 10600 | 1.5193 |
Framework versions
- Transformers 4.43.4
- Pytorch 2.1.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1