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scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_alpha-jason

Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +175 -0
  2. config.json +153 -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: FacebookAI/xlm-roberta-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_1_alpha-jason
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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_1_alpha-jason
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+
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+ This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the massive dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.8052
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+ - Accuracy: 0.8270
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+ - F1: 0.8027
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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: 1123
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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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+ | 5.9621 | 0.27 | 5000 | 5.8534 | 0.3085 | 0.1374 |
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+ | 4.2762 | 0.53 | 10000 | 4.2222 | 0.5506 | 0.4008 |
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+ | 3.3857 | 0.8 | 15000 | 3.3996 | 0.6458 | 0.5306 |
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+ | 2.6536 | 1.07 | 20000 | 2.9551 | 0.6961 | 0.5947 |
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+ | 2.4701 | 1.34 | 25000 | 2.6730 | 0.7256 | 0.6394 |
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+ | 2.2519 | 1.6 | 30000 | 2.4672 | 0.7521 | 0.6770 |
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+ | 2.1267 | 1.87 | 35000 | 2.3678 | 0.7601 | 0.6970 |
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+ | 1.7317 | 2.14 | 40000 | 2.2796 | 0.7712 | 0.7112 |
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+ | 1.6837 | 2.41 | 45000 | 2.2315 | 0.7779 | 0.7218 |
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+ | 1.6611 | 2.67 | 50000 | 2.1565 | 0.7839 | 0.7372 |
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+ | 1.6647 | 2.94 | 55000 | 2.1357 | 0.7868 | 0.7468 |
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+ | 1.3314 | 3.21 | 60000 | 2.1089 | 0.7911 | 0.7511 |
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+ | 1.357 | 3.47 | 65000 | 2.0963 | 0.7926 | 0.7586 |
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+ | 1.3339 | 3.74 | 70000 | 2.0273 | 0.7987 | 0.7619 |
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+ | 1.2323 | 4.01 | 75000 | 2.0556 | 0.8011 | 0.7612 |
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+ | 1.1101 | 4.28 | 80000 | 2.0695 | 0.8017 | 0.7633 |
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+ | 1.1174 | 4.54 | 85000 | 2.0361 | 0.8043 | 0.7687 |
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+ | 1.1121 | 4.81 | 90000 | 2.0272 | 0.8067 | 0.7749 |
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+ | 0.9268 | 5.08 | 95000 | 2.0497 | 0.8053 | 0.7714 |
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+ | 0.9366 | 5.34 | 100000 | 2.0511 | 0.8041 | 0.7726 |
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+ | 0.9756 | 5.61 | 105000 | 2.0082 | 0.8052 | 0.7724 |
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+ | 0.9703 | 5.88 | 110000 | 1.9988 | 0.8065 | 0.7766 |
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+ | 0.8228 | 6.15 | 115000 | 2.0636 | 0.8066 | 0.7765 |
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+ | 0.8037 | 6.41 | 120000 | 2.0380 | 0.8083 | 0.7817 |
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+ | 0.8447 | 6.68 | 125000 | 2.0241 | 0.8101 | 0.7851 |
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+ | 0.8646 | 6.95 | 130000 | 1.9780 | 0.8116 | 0.7830 |
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+ | 0.7335 | 7.22 | 135000 | 2.0400 | 0.8101 | 0.7835 |
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+ | 0.747 | 7.48 | 140000 | 2.0154 | 0.8099 | 0.7844 |
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+ | 0.7445 | 7.75 | 145000 | 2.0174 | 0.8116 | 0.7841 |
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+ | 0.7124 | 8.02 | 150000 | 2.0317 | 0.8116 | 0.7859 |
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+ | 0.683 | 8.28 | 155000 | 2.0252 | 0.8129 | 0.7843 |
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+ | 0.6882 | 8.55 | 160000 | 2.0055 | 0.8133 | 0.7888 |
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+ | 0.6939 | 8.82 | 165000 | 2.0076 | 0.8129 | 0.7912 |
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+ | 0.6298 | 9.09 | 170000 | 2.0252 | 0.8126 | 0.7863 |
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+ | 0.6187 | 9.35 | 175000 | 2.0216 | 0.8120 | 0.7865 |
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+ | 0.6316 | 9.62 | 180000 | 2.0150 | 0.8123 | 0.7900 |
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+ | 0.6566 | 9.89 | 185000 | 1.9868 | 0.8155 | 0.7911 |
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+ | 0.5797 | 10.15 | 190000 | 1.9903 | 0.8158 | 0.7918 |
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+ | 0.5911 | 10.42 | 195000 | 1.9915 | 0.8152 | 0.7909 |
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+ | 0.6004 | 10.69 | 200000 | 1.9793 | 0.8167 | 0.7929 |
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+ | 0.6141 | 10.96 | 205000 | 1.9958 | 0.8145 | 0.7904 |
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+ | 0.549 | 11.22 | 210000 | 1.9843 | 0.8155 | 0.7912 |
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+ | 0.5552 | 11.49 | 215000 | 1.9643 | 0.8163 | 0.7926 |
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+ | 0.5622 | 11.76 | 220000 | 1.9738 | 0.8179 | 0.7913 |
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+ | 0.512 | 12.03 | 225000 | 1.9708 | 0.8166 | 0.7925 |
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+ | 0.5349 | 12.29 | 230000 | 1.9533 | 0.8190 | 0.7953 |
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+ | 0.531 | 12.56 | 235000 | 1.9436 | 0.8201 | 0.7952 |
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+ | 0.5472 | 12.83 | 240000 | 1.9548 | 0.8174 | 0.7926 |
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+ | 0.4982 | 13.09 | 245000 | 1.9473 | 0.8195 | 0.7958 |
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+ | 0.4978 | 13.36 | 250000 | 1.9753 | 0.8190 | 0.7969 |
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+ | 0.5024 | 13.63 | 255000 | 1.9694 | 0.8186 | 0.7957 |
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+ | 0.5089 | 13.9 | 260000 | 1.9483 | 0.8188 | 0.7952 |
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+ | 0.4887 | 14.16 | 265000 | 1.9363 | 0.8200 | 0.7960 |
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+ | 0.4877 | 14.43 | 270000 | 1.9545 | 0.8189 | 0.7955 |
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+ | 0.4843 | 14.7 | 275000 | 1.9503 | 0.8188 | 0.7962 |
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+ | 0.4847 | 14.96 | 280000 | 1.9344 | 0.8195 | 0.7958 |
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+ | 0.4617 | 15.5 | 290000 | 1.9180 | 0.8217 | 0.7986 |
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+ | 0.4643 | 15.77 | 295000 | 1.9007 | 0.8217 | 0.7982 |
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+ | 0.4525 | 16.03 | 300000 | 1.9034 | 0.8199 | 0.7949 |
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+ | 0.4446 | 16.3 | 305000 | 1.8952 | 0.8215 | 0.7987 |
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+ | 0.4525 | 16.57 | 310000 | 1.9047 | 0.8226 | 0.7995 |
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+ | 0.4566 | 16.84 | 315000 | 1.9056 | 0.8230 | 0.8001 |
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+ | 0.4354 | 17.1 | 320000 | 1.8935 | 0.8227 | 0.7989 |
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+ | 0.4356 | 17.64 | 330000 | 1.9043 | 0.8217 | 0.7981 |
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+ | 0.4334 | 17.9 | 335000 | 1.8808 | 0.8224 | 0.7972 |
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+ | 0.4209 | 18.17 | 340000 | 1.8816 | 0.8227 | 0.7987 |
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+ | 0.4187 | 18.44 | 345000 | 1.8874 | 0.8226 | 0.7993 |
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+ | 0.4256 | 18.97 | 355000 | 1.8734 | 0.8236 | 0.7978 |
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+ | 0.3767 | 22.98 | 430000 | 1.8476 | 0.8236 | 0.7983 |
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+ | 0.377 | 23.25 | 435000 | 1.8286 | 0.8256 | 0.8000 |
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+ | 0.369 | 23.52 | 440000 | 1.8356 | 0.8263 | 0.8019 |
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+ | 0.3403 | 29.93 | 560000 | 1.8052 | 0.8270 | 0.8027 |
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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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+ "XLMRobertaForSequenceClassificationKD"
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