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scenario-NON-KD-PR-COPY-D2_data-AmazonScience_massive_all_1_1_betta-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: 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-NON-KD-PR-COPY-D2_data-AmazonScience_massive_all_1_1_betta-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-NON-KD-PR-COPY-D2_data-AmazonScience_massive_all_1_1_betta-jason
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+
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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: 1.4867
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+ - Accuracy: 0.8342
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+ - F1: 0.8095
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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: 222
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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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+ | 1.6555 | 0.27 | 5000 | 1.5175 | 0.5778 | 0.4204 |
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+ | 1.1914 | 0.53 | 10000 | 1.1206 | 0.6936 | 0.5745 |
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+ | 0.9781 | 0.8 | 15000 | 0.9691 | 0.7383 | 0.6438 |
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+ | 0.7905 | 1.07 | 20000 | 0.8906 | 0.7631 | 0.6846 |
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+ | 0.7201 | 1.34 | 25000 | 0.8162 | 0.7809 | 0.7151 |
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+ | 0.6624 | 1.6 | 30000 | 0.7933 | 0.7914 | 0.7310 |
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+ | 0.6399 | 1.87 | 35000 | 0.7562 | 0.8000 | 0.7518 |
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+ | 0.5163 | 2.14 | 40000 | 0.7660 | 0.8057 | 0.7589 |
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+ | 0.503 | 2.41 | 45000 | 0.7659 | 0.8060 | 0.7609 |
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+ | 0.4855 | 2.67 | 50000 | 0.7446 | 0.8110 | 0.7707 |
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+ | 0.4744 | 2.94 | 55000 | 0.7199 | 0.8160 | 0.7793 |
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+ | 0.3807 | 3.21 | 60000 | 0.7582 | 0.8165 | 0.7787 |
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+ | 0.3902 | 3.47 | 65000 | 0.7526 | 0.8177 | 0.7773 |
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+ | 0.3757 | 3.74 | 70000 | 0.7647 | 0.8158 | 0.7771 |
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+ | 0.3496 | 4.01 | 75000 | 0.7529 | 0.8234 | 0.7867 |
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+ | 0.2917 | 4.28 | 80000 | 0.7961 | 0.8181 | 0.7830 |
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+ | 0.3042 | 4.54 | 85000 | 0.7907 | 0.8190 | 0.7837 |
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+ | 0.3098 | 4.81 | 90000 | 0.7699 | 0.8237 | 0.7878 |
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+ | 0.2381 | 5.08 | 95000 | 0.8027 | 0.8224 | 0.7842 |
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+ | 0.244 | 5.34 | 100000 | 0.8074 | 0.8242 | 0.7898 |
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+ | 0.2509 | 5.61 | 105000 | 0.8052 | 0.8260 | 0.7924 |
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+ | 0.2709 | 5.88 | 110000 | 0.8002 | 0.8258 | 0.7925 |
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+ | 0.2001 | 6.15 | 115000 | 0.8449 | 0.8242 | 0.7909 |
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+ | 0.2175 | 6.41 | 120000 | 0.8669 | 0.8236 | 0.7892 |
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+ | 0.214 | 6.68 | 125000 | 0.8765 | 0.8252 | 0.7987 |
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+ | 0.222 | 6.95 | 130000 | 0.8332 | 0.8288 | 0.8003 |
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+ | 0.1713 | 7.22 | 135000 | 0.9019 | 0.8262 | 0.7951 |
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+ | 0.1751 | 7.48 | 140000 | 0.8958 | 0.8255 | 0.7957 |
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+ | 0.1794 | 7.75 | 145000 | 0.9065 | 0.8260 | 0.7982 |
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+ | 0.1544 | 8.02 | 150000 | 0.9200 | 0.8263 | 0.8020 |
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+ | 0.1445 | 8.28 | 155000 | 0.9510 | 0.8240 | 0.7975 |
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+ | 0.155 | 8.55 | 160000 | 0.9418 | 0.8294 | 0.7999 |
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+ | 0.157 | 8.82 | 165000 | 0.9463 | 0.8295 | 0.8055 |
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+ | 0.1182 | 9.09 | 170000 | 0.9762 | 0.8273 | 0.8020 |
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+ | 0.1303 | 9.35 | 175000 | 0.9829 | 0.8271 | 0.8032 |
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+ | 0.131 | 9.62 | 180000 | 1.0006 | 0.8292 | 0.8019 |
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+ | 0.127 | 9.89 | 185000 | 0.9976 | 0.8251 | 0.8009 |
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+ | 0.092 | 10.15 | 190000 | 1.0280 | 0.8278 | 0.8002 |
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+ | 0.1131 | 10.42 | 195000 | 1.0338 | 0.8271 | 0.8018 |
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+ | 0.1135 | 10.69 | 200000 | 1.0388 | 0.8277 | 0.8009 |
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+ | 0.115 | 10.96 | 205000 | 1.0341 | 0.8278 | 0.8010 |
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+ | 0.0871 | 11.22 | 210000 | 1.0720 | 0.8282 | 0.8022 |
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+ | 0.0992 | 11.49 | 215000 | 1.0691 | 0.8292 | 0.8040 |
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+ | 0.1007 | 11.76 | 220000 | 1.0821 | 0.8279 | 0.8017 |
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+ | 0.0776 | 12.03 | 225000 | 1.1169 | 0.8260 | 0.7960 |
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+ | 0.0833 | 12.29 | 230000 | 1.1196 | 0.8283 | 0.8031 |
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+ | 0.0843 | 12.56 | 235000 | 1.1386 | 0.8286 | 0.8035 |
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+ | 0.0884 | 12.83 | 240000 | 1.1368 | 0.8281 | 0.8006 |
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+ | 0.0637 | 13.09 | 245000 | 1.1611 | 0.8265 | 0.8032 |
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+ | 0.0721 | 13.36 | 250000 | 1.1857 | 0.8248 | 0.7988 |
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+ | 0.0731 | 13.63 | 255000 | 1.1788 | 0.8294 | 0.8028 |
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+ | 0.0754 | 13.9 | 260000 | 1.1879 | 0.8266 | 0.8027 |
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+ | 0.0615 | 14.16 | 265000 | 1.2059 | 0.8308 | 0.8049 |
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+ | 0.0741 | 14.43 | 270000 | 1.2121 | 0.8280 | 0.8019 |
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+ | 0.0677 | 14.7 | 275000 | 1.2192 | 0.8296 | 0.8033 |
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+ | 0.0736 | 14.96 | 280000 | 1.2419 | 0.8266 | 0.7993 |
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+ | 0.0561 | 15.23 | 285000 | 1.2439 | 0.8288 | 0.8006 |
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+ | 0.0554 | 15.5 | 290000 | 1.2603 | 0.8282 | 0.7990 |
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+ | 0.0634 | 15.77 | 295000 | 1.2692 | 0.8279 | 0.8009 |
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+ | 0.0445 | 16.03 | 300000 | 1.2826 | 0.8284 | 0.8010 |
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+ | 0.052 | 16.3 | 305000 | 1.2949 | 0.8287 | 0.8048 |
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+ | 0.0568 | 16.57 | 310000 | 1.3029 | 0.8284 | 0.8031 |
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+ | 0.0484 | 16.84 | 315000 | 1.2977 | 0.8298 | 0.8043 |
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+ | 0.0446 | 17.1 | 320000 | 1.3212 | 0.8280 | 0.8031 |
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+ | 0.0462 | 17.37 | 325000 | 1.3350 | 0.8277 | 0.8013 |
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+ | 0.047 | 17.64 | 330000 | 1.3301 | 0.8297 | 0.8042 |
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+ | 0.048 | 17.9 | 335000 | 1.3293 | 0.8297 | 0.8041 |
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+ | 0.0421 | 18.17 | 340000 | 1.3249 | 0.8286 | 0.8023 |
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+ | 0.0405 | 18.44 | 345000 | 1.3471 | 0.8277 | 0.8025 |
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+ | 0.0458 | 18.71 | 350000 | 1.3654 | 0.8302 | 0.8037 |
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+ | 0.0463 | 18.97 | 355000 | 1.3435 | 0.8311 | 0.8043 |
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+ | 0.0392 | 19.24 | 360000 | 1.3816 | 0.8294 | 0.8051 |
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+ | 0.0379 | 19.51 | 365000 | 1.3748 | 0.8315 | 0.8077 |
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+ | 0.0358 | 19.77 | 370000 | 1.3599 | 0.8322 | 0.8064 |
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+ | 0.0276 | 20.04 | 375000 | 1.3637 | 0.8318 | 0.8080 |
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+ | 0.0359 | 20.31 | 380000 | 1.3649 | 0.8322 | 0.8068 |
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+ | 0.0322 | 20.58 | 385000 | 1.3857 | 0.8305 | 0.8040 |
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+ | 0.0338 | 21.11 | 395000 | 1.3937 | 0.8311 | 0.8048 |
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+ | 0.0307 | 21.38 | 400000 | 1.4248 | 0.8294 | 0.8043 |
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+ | 0.0301 | 21.65 | 405000 | 1.4184 | 0.8296 | 0.8050 |
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+ | 0.0289 | 21.91 | 410000 | 1.4154 | 0.8307 | 0.8053 |
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+ | 0.0266 | 22.18 | 415000 | 1.4249 | 0.8304 | 0.8057 |
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+ | 0.0282 | 22.45 | 420000 | 1.4311 | 0.8319 | 0.8085 |
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+ | 0.0306 | 22.71 | 425000 | 1.4417 | 0.8306 | 0.8055 |
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+ | 0.0272 | 22.98 | 430000 | 1.4490 | 0.8302 | 0.8036 |
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+ | 0.0264 | 23.25 | 435000 | 1.4372 | 0.8321 | 0.8061 |
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+ | 0.0232 | 23.52 | 440000 | 1.4548 | 0.8304 | 0.8057 |
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+ | 0.0264 | 23.78 | 445000 | 1.4496 | 0.8317 | 0.8065 |
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+ | 0.0194 | 24.05 | 450000 | 1.4454 | 0.8320 | 0.8073 |
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+ | 0.0228 | 24.32 | 455000 | 1.4532 | 0.8327 | 0.8077 |
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+ | 0.0229 | 24.58 | 460000 | 1.4574 | 0.8325 | 0.8068 |
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+ | 0.0203 | 24.85 | 465000 | 1.4709 | 0.8310 | 0.8066 |
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+ | 0.0183 | 25.12 | 470000 | 1.4707 | 0.8327 | 0.8090 |
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+ | 0.0209 | 26.46 | 495000 | 1.4771 | 0.8335 | 0.8084 |
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+ | 0.0193 | 26.72 | 500000 | 1.4882 | 0.8325 | 0.8076 |
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+ | 0.0184 | 26.99 | 505000 | 1.4740 | 0.8333 | 0.8088 |
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+ | 0.0145 | 27.26 | 510000 | 1.4818 | 0.8339 | 0.8095 |
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+ | 0.0141 | 27.52 | 515000 | 1.4909 | 0.8327 | 0.8075 |
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+ | 0.0157 | 27.79 | 520000 | 1.4787 | 0.8331 | 0.8086 |
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+ | 0.0168 | 28.06 | 525000 | 1.4842 | 0.8336 | 0.8078 |
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+ | 0.0179 | 28.33 | 530000 | 1.4847 | 0.8338 | 0.8084 |
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+ | 0.014 | 28.59 | 535000 | 1.4846 | 0.8339 | 0.8089 |
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+ | 0.0145 | 28.86 | 540000 | 1.4856 | 0.8333 | 0.8087 |
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+ | 0.0135 | 29.13 | 545000 | 1.4864 | 0.8336 | 0.8085 |
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+ | 0.0127 | 29.39 | 550000 | 1.4852 | 0.8340 | 0.8088 |
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+ | 0.0159 | 29.66 | 555000 | 1.4874 | 0.8342 | 0.8093 |
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+ | 0.0144 | 29.93 | 560000 | 1.4867 | 0.8342 | 0.8095 |
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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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+ "XLMRobertaForSequenceClassificationKD"
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