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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: tasksource/deberta-small-long-nli |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: ms-deberta-v2-xlarge-mnli-finetuned-pt |
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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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# ms-deberta-v2-xlarge-mnli-finetuned-pt |
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This model is a fine-tuned version of [tasksource/deberta-small-long-nli](https://huggingface.co/tasksource/deberta-small-long-nli) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2954 |
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- Accuracy: 1.0 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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- F1: 1.0 |
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- Ratio: 0.11 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 0.06 |
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- lr_scheduler_warmup_steps: 4 |
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- num_epochs: 1 |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
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| 1.4129 | 0.0237 | 10 | 0.5425 | 0.89 | 0.445 | 0.5 | 0.4709 | 0.0 | |
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| 0.5102 | 0.0474 | 20 | 0.4968 | 0.89 | 0.445 | 0.5 | 0.4709 | 0.0 | |
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| 0.4597 | 0.0711 | 30 | 0.4763 | 0.88 | 0.6225 | 0.5395 | 0.5471 | 0.0327 | |
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| 0.4975 | 0.0948 | 40 | 0.4605 | 0.87 | 0.6658 | 0.6614 | 0.6636 | 0.1067 | |
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| 0.4639 | 0.1185 | 50 | 0.4434 | 0.8947 | 0.7355 | 0.5850 | 0.6125 | 0.0367 | |
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| 0.4687 | 0.1422 | 60 | 0.4557 | 0.892 | 0.7177 | 0.6498 | 0.6747 | 0.0727 | |
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| 0.4489 | 0.1659 | 70 | 0.4353 | 0.9293 | 0.8174 | 0.8275 | 0.8224 | 0.114 | |
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| 0.4318 | 0.1896 | 80 | 0.4269 | 0.924 | 0.8010 | 0.8325 | 0.8156 | 0.1233 | |
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| 0.4723 | 0.2133 | 90 | 0.4202 | 0.9173 | 0.7832 | 0.8580 | 0.8140 | 0.1447 | |
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| 0.4052 | 0.2370 | 100 | 0.4016 | 0.9307 | 0.8207 | 0.8309 | 0.8257 | 0.114 | |
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| 0.4284 | 0.2607 | 110 | 0.4115 | 0.9187 | 0.7855 | 0.8906 | 0.8255 | 0.1593 | |
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| 0.3635 | 0.2844 | 120 | 0.3963 | 0.94 | 0.8308 | 0.9052 | 0.8625 | 0.1393 | |
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| 0.3894 | 0.3081 | 130 | 0.3910 | 0.944 | 0.8409 | 0.9075 | 0.8699 | 0.1353 | |
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| 0.3537 | 0.3318 | 140 | 0.3598 | 0.9693 | 0.8983 | 0.9642 | 0.9277 | 0.1313 | |
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| 0.3776 | 0.3555 | 150 | 0.3868 | 0.944 | 0.8313 | 0.9685 | 0.8823 | 0.166 | |
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| 0.3626 | 0.3791 | 160 | 0.3235 | 0.9887 | 0.9699 | 0.9724 | 0.9711 | 0.1107 | |
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| 0.3683 | 0.4028 | 170 | 0.3272 | 0.99 | 0.9583 | 0.9944 | 0.9754 | 0.12 | |
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| 0.3358 | 0.4265 | 180 | 0.3321 | 0.9873 | 0.9484 | 0.9929 | 0.9692 | 0.1227 | |
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| 0.3435 | 0.4502 | 190 | 0.3370 | 0.982 | 0.9297 | 0.9899 | 0.9571 | 0.128 | |
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| 0.3613 | 0.4739 | 200 | 0.3136 | 0.9893 | 0.9728 | 0.9728 | 0.9728 | 0.11 | |
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| 0.3323 | 0.4976 | 210 | 0.3193 | 0.9887 | 0.9533 | 0.9936 | 0.9723 | 0.1213 | |
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| 0.3181 | 0.5213 | 220 | 0.3078 | 0.9947 | 0.9970 | 0.9758 | 0.9861 | 0.1047 | |
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| 0.3043 | 0.5450 | 230 | 0.3047 | 0.9947 | 0.9970 | 0.9758 | 0.9861 | 0.1047 | |
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| 0.3139 | 0.5687 | 240 | 0.3101 | 0.996 | 0.9825 | 0.9978 | 0.9899 | 0.114 | |
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| 0.3247 | 0.5924 | 250 | 0.3048 | 0.9947 | 0.9970 | 0.9758 | 0.9861 | 0.1047 | |
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| 0.3217 | 0.6161 | 260 | 0.3126 | 0.9913 | 0.9635 | 0.9951 | 0.9786 | 0.1187 | |
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| 0.3071 | 0.6398 | 270 | 0.3021 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.3048 | 0.6635 | 280 | 0.3048 | 0.9973 | 0.9882 | 0.9985 | 0.9933 | 0.1127 | |
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| 0.3054 | 0.6872 | 290 | 0.2996 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.3182 | 0.7109 | 300 | 0.2979 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.3059 | 0.7346 | 310 | 0.3103 | 0.9927 | 0.9688 | 0.9959 | 0.9818 | 0.1173 | |
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| 0.3044 | 0.7583 | 320 | 0.2991 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.3002 | 0.7820 | 330 | 0.2967 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.2957 | 0.8057 | 340 | 0.2967 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.2971 | 0.8294 | 350 | 0.2968 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.2964 | 0.8531 | 360 | 0.2970 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.297 | 0.8768 | 370 | 0.2969 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.3039 | 0.9005 | 380 | 0.2968 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.3002 | 0.9242 | 390 | 0.2960 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.2968 | 0.9479 | 400 | 0.2956 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.2956 | 0.9716 | 410 | 0.2955 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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| 0.2959 | 0.9953 | 420 | 0.2954 | 1.0 | 1.0 | 1.0 | 1.0 | 0.11 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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