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scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_144

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PyTorch
deberta-v2
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +175 -0
  2. config.json +159 -0
  3. pytorch_model.bin +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: microsoft/mdeberta-v3-base
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - massive
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_144
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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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+ # scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_144
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+
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+ This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: nan
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+ - Accuracy: 0.0315
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+ - F1: 0.0010
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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: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 44
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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: 30
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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 | F1 |
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+ |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
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+ | 0.0 | 0.27 | 5000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 0.53 | 10000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 0.8 | 15000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 1.07 | 20000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 1.34 | 25000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 1.6 | 30000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 1.87 | 35000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 2.14 | 40000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 2.41 | 45000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 2.67 | 50000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 2.94 | 55000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 3.21 | 60000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 3.47 | 65000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 3.74 | 70000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 4.01 | 75000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 4.28 | 80000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 4.54 | 85000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 4.81 | 90000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 5.08 | 95000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 5.34 | 100000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 5.61 | 105000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 5.88 | 110000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 6.15 | 115000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 6.41 | 120000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 6.68 | 125000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 6.95 | 130000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 7.22 | 135000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 7.48 | 140000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 7.75 | 145000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 8.02 | 150000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 8.28 | 155000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 8.55 | 160000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 8.82 | 165000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 9.09 | 170000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 9.35 | 175000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 9.62 | 180000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 9.89 | 185000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 10.15 | 190000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 10.69 | 200000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 10.96 | 205000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 11.22 | 210000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 11.49 | 215000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 11.76 | 220000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 12.03 | 225000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 12.29 | 230000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 12.56 | 235000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 29.93 | 560000 | nan | 0.0315 | 0.0010 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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+ {
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+ "DebertaForSequenceClassificationKD"
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