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scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only_alpha

Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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  1. README.md +30 -30
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -18,9 +18,9 @@ should probably proofread and complete it, then remove this comment. -->
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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: 6.4834
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- - Accuracy: 0.3758
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- - F1: 0.3692
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  ## Model description
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@@ -51,33 +51,33 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | No log | 1.09 | 250 | 1.1269 | 0.3302 | 0.2264 |
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- | 1.0552 | 2.17 | 500 | 1.4862 | 0.3673 | 0.3240 |
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- | 1.0552 | 3.26 | 750 | 2.0425 | 0.3580 | 0.3105 |
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- | 0.4849 | 4.35 | 1000 | 2.7827 | 0.3673 | 0.3562 |
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- | 0.4849 | 5.43 | 1250 | 2.3787 | 0.3804 | 0.3790 |
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- | 0.1731 | 6.52 | 1500 | 3.5404 | 0.3873 | 0.3523 |
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- | 0.1731 | 7.61 | 1750 | 4.1335 | 0.3765 | 0.3561 |
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- | 0.0878 | 8.7 | 2000 | 4.4114 | 0.375 | 0.3748 |
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- | 0.0878 | 9.78 | 2250 | 4.9951 | 0.3650 | 0.3587 |
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- | 0.036 | 10.87 | 2500 | 5.1386 | 0.3642 | 0.3569 |
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- | 0.036 | 11.96 | 2750 | 5.4524 | 0.3711 | 0.3499 |
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- | 0.0297 | 13.04 | 3000 | 5.4964 | 0.3681 | 0.3565 |
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- | 0.0297 | 14.13 | 3250 | 5.3291 | 0.3765 | 0.3709 |
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- | 0.0225 | 15.22 | 3500 | 5.6184 | 0.3719 | 0.3346 |
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- | 0.0225 | 16.3 | 3750 | 5.4079 | 0.3719 | 0.3466 |
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- | 0.0144 | 17.39 | 4000 | 5.6999 | 0.3634 | 0.3538 |
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- | 0.0144 | 18.48 | 4250 | 5.7290 | 0.3681 | 0.3575 |
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- | 0.007 | 19.57 | 4500 | 5.9349 | 0.3673 | 0.3272 |
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- | 0.007 | 20.65 | 4750 | 5.7265 | 0.3789 | 0.3760 |
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- | 0.0064 | 21.74 | 5000 | 6.1464 | 0.3611 | 0.3370 |
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- | 0.0064 | 22.83 | 5250 | 6.1483 | 0.3765 | 0.3678 |
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- | 0.0026 | 23.91 | 5500 | 6.3733 | 0.3742 | 0.3688 |
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- | 0.0026 | 25.0 | 5750 | 6.3795 | 0.3704 | 0.3498 |
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- | 0.002 | 26.09 | 6000 | 6.4692 | 0.3735 | 0.3582 |
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- | 0.002 | 27.17 | 6250 | 6.5994 | 0.3727 | 0.3448 |
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- | 0.0006 | 28.26 | 6500 | 6.4658 | 0.3758 | 0.3693 |
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- | 0.0006 | 29.35 | 6750 | 6.4834 | 0.3758 | 0.3692 |
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  ### Framework versions
 
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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: 6.7853
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+ - Accuracy: 0.3580
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+ - F1: 0.3378
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 1.09 | 250 | 1.1518 | 0.3495 | 0.3449 |
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+ | 1.0509 | 2.17 | 500 | 1.5848 | 0.3665 | 0.3279 |
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+ | 1.0509 | 3.26 | 750 | 2.0537 | 0.3673 | 0.3351 |
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+ | 0.4971 | 4.35 | 1000 | 2.6254 | 0.3642 | 0.3389 |
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+ | 0.4971 | 5.43 | 1250 | 3.3984 | 0.3495 | 0.3056 |
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+ | 0.189 | 6.52 | 1500 | 3.9545 | 0.3588 | 0.3213 |
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+ | 0.189 | 7.61 | 1750 | 4.3147 | 0.3634 | 0.3250 |
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+ | 0.0832 | 8.7 | 2000 | 4.5326 | 0.3495 | 0.3223 |
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+ | 0.0832 | 9.78 | 2250 | 4.8999 | 0.3627 | 0.3396 |
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+ | 0.0407 | 10.87 | 2500 | 5.2749 | 0.3503 | 0.3354 |
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+ | 0.0407 | 11.96 | 2750 | 5.2814 | 0.3634 | 0.3500 |
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+ | 0.0279 | 13.04 | 3000 | 5.3923 | 0.3657 | 0.3502 |
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+ | 0.0279 | 14.13 | 3250 | 5.7450 | 0.3565 | 0.3397 |
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+ | 0.0153 | 15.22 | 3500 | 5.6113 | 0.3681 | 0.3582 |
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+ | 0.0153 | 16.3 | 3750 | 5.1689 | 0.3704 | 0.3615 |
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+ | 0.0145 | 17.39 | 4000 | 5.8264 | 0.3650 | 0.3579 |
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+ | 0.0145 | 18.48 | 4250 | 5.6710 | 0.3650 | 0.3603 |
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+ | 0.0092 | 19.57 | 4500 | 6.0070 | 0.3650 | 0.3547 |
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+ | 0.0092 | 20.65 | 4750 | 6.2579 | 0.3519 | 0.3287 |
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+ | 0.0034 | 21.74 | 5000 | 6.3540 | 0.3650 | 0.3550 |
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+ | 0.0034 | 22.83 | 5250 | 6.4666 | 0.3619 | 0.3438 |
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+ | 0.0031 | 23.91 | 5500 | 6.6982 | 0.3580 | 0.3286 |
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+ | 0.0031 | 25.0 | 5750 | 6.6139 | 0.3657 | 0.3611 |
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+ | 0.0016 | 26.09 | 6000 | 6.6320 | 0.3688 | 0.3632 |
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+ | 0.0016 | 27.17 | 6250 | 6.7619 | 0.3673 | 0.3544 |
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+ | 0.0015 | 28.26 | 6500 | 6.7368 | 0.3627 | 0.3426 |
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+ | 0.0015 | 29.35 | 6750 | 6.7853 | 0.3580 | 0.3378 |
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  ### Framework versions
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