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simonycl
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best_model-yelp_polarity-64-100

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
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+ ---
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+ license: mit
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+ base_model: roberta-base
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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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+ model-index:
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+ - name: best_model-yelp_polarity-64-100
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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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+ # best_model-yelp_polarity-64-100
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6421
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+ - Accuracy: 0.9453
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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: 1e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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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_steps: 500
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+ - num_epochs: 150
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 4 | 0.6227 | 0.9219 |
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+ | No log | 2.0 | 8 | 0.6400 | 0.9219 |
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+ | 0.151 | 3.0 | 12 | 0.6723 | 0.9219 |
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+ | 0.151 | 4.0 | 16 | 0.6894 | 0.9219 |
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+ | 0.1653 | 5.0 | 20 | 0.6886 | 0.9219 |
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+ | 0.1653 | 6.0 | 24 | 0.6817 | 0.9219 |
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+ | 0.1653 | 7.0 | 28 | 0.6861 | 0.9219 |
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+ | 0.1389 | 8.0 | 32 | 0.6867 | 0.9219 |
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+ | 0.1389 | 9.0 | 36 | 0.6738 | 0.9219 |
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+ | 0.1365 | 10.0 | 40 | 0.6662 | 0.9219 |
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+ | 0.1365 | 11.0 | 44 | 0.6472 | 0.9219 |
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+ | 0.1365 | 12.0 | 48 | 0.6332 | 0.9219 |
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+ | 0.058 | 13.0 | 52 | 0.6449 | 0.9297 |
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+ | 0.058 | 14.0 | 56 | 0.6604 | 0.9297 |
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+ | 0.0532 | 15.0 | 60 | 0.6461 | 0.9297 |
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+ | 0.0532 | 16.0 | 64 | 0.5893 | 0.9219 |
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+ | 0.0532 | 17.0 | 68 | 0.5498 | 0.9297 |
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+ | 0.0062 | 18.0 | 72 | 0.5295 | 0.9375 |
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+ | 0.0062 | 19.0 | 76 | 0.5262 | 0.9375 |
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+ | 0.0045 | 20.0 | 80 | 0.5270 | 0.9375 |
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+ | 0.0045 | 21.0 | 84 | 0.5261 | 0.9453 |
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+ | 0.0045 | 22.0 | 88 | 0.5272 | 0.9453 |
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+ | 0.0 | 23.0 | 92 | 0.5281 | 0.9453 |
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+ | 0.0 | 24.0 | 96 | 0.5295 | 0.9453 |
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+ | 0.001 | 25.0 | 100 | 0.5315 | 0.9453 |
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+ | 0.001 | 26.0 | 104 | 0.5375 | 0.9375 |
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+ | 0.001 | 27.0 | 108 | 0.5524 | 0.9297 |
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+ | 0.0 | 28.0 | 112 | 0.5820 | 0.9219 |
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+ | 0.0 | 29.0 | 116 | 0.6046 | 0.9297 |
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+ | 0.0 | 30.0 | 120 | 0.6323 | 0.9297 |
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+ | 0.0 | 31.0 | 124 | 0.6660 | 0.9297 |
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+ | 0.0 | 32.0 | 128 | 0.6793 | 0.9297 |
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+ | 0.0 | 33.0 | 132 | 0.6855 | 0.9297 |
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+ | 0.0 | 34.0 | 136 | 0.6604 | 0.9297 |
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+ | 0.0 | 35.0 | 140 | 0.5577 | 0.9297 |
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+ | 0.0 | 36.0 | 144 | 0.5515 | 0.9453 |
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+ | 0.0 | 37.0 | 148 | 0.5494 | 0.9453 |
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+ | 0.0 | 38.0 | 152 | 0.5492 | 0.9453 |
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+ | 0.0 | 39.0 | 156 | 0.5491 | 0.9453 |
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+ | 0.0 | 40.0 | 160 | 0.5493 | 0.9453 |
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+ | 0.0 | 41.0 | 164 | 0.5671 | 0.9453 |
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+ | 0.0 | 42.0 | 168 | 0.5708 | 0.9453 |
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+ | 0.006 | 43.0 | 172 | 0.5740 | 0.9375 |
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+ | 0.006 | 44.0 | 176 | 0.5883 | 0.9297 |
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+ | 0.0 | 45.0 | 180 | 0.6010 | 0.9297 |
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+ | 0.0 | 46.0 | 184 | 0.6081 | 0.9297 |
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+ | 0.0 | 47.0 | 188 | 0.6122 | 0.9297 |
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+ | 0.0 | 48.0 | 192 | 0.6149 | 0.9297 |
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+ | 0.0 | 49.0 | 196 | 0.6166 | 0.9297 |
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+ | 0.0 | 50.0 | 200 | 0.6177 | 0.9297 |
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+ | 0.0 | 51.0 | 204 | 0.6205 | 0.9297 |
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+ | 0.0 | 52.0 | 208 | 0.6229 | 0.9297 |
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+ | 0.0 | 53.0 | 212 | 0.6242 | 0.9297 |
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+ | 0.0 | 54.0 | 216 | 0.6251 | 0.9297 |
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+ | 0.0 | 55.0 | 220 | 0.6205 | 0.9297 |
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+ | 0.0 | 56.0 | 224 | 0.6152 | 0.9297 |
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+ | 0.0 | 57.0 | 228 | 0.6106 | 0.9297 |
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+ | 0.0 | 58.0 | 232 | 0.6068 | 0.9297 |
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+ | 0.0 | 59.0 | 236 | 0.6041 | 0.9297 |
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+ | 0.0 | 60.0 | 240 | 0.6025 | 0.9297 |
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+ | 0.0 | 61.0 | 244 | 0.6008 | 0.9297 |
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+ | 0.0 | 62.0 | 248 | 0.5988 | 0.9297 |
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+ | 0.0 | 63.0 | 252 | 0.5965 | 0.9297 |
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+ | 0.0 | 64.0 | 256 | 0.5944 | 0.9297 |
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+ | 0.0 | 65.0 | 260 | 0.5928 | 0.9297 |
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+ | 0.0 | 66.0 | 264 | 0.5920 | 0.9453 |
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+ | 0.0 | 67.0 | 268 | 0.5914 | 0.9453 |
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+ | 0.0 | 68.0 | 272 | 0.5914 | 0.9453 |
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+ | 0.0 | 69.0 | 276 | 0.5916 | 0.9453 |
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+ | 0.0 | 70.0 | 280 | 0.5919 | 0.9453 |
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+ | 0.0 | 71.0 | 284 | 0.5923 | 0.9453 |
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+ | 0.0 | 72.0 | 288 | 0.5927 | 0.9453 |
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+ | 0.0 | 73.0 | 292 | 0.5931 | 0.9453 |
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+ | 0.0 | 74.0 | 296 | 0.5935 | 0.9453 |
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+ | 0.0 | 75.0 | 300 | 0.5938 | 0.9453 |
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+ | 0.0 | 76.0 | 304 | 0.5942 | 0.9453 |
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+ | 0.0 | 77.0 | 308 | 0.5946 | 0.9453 |
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+ | 0.0 | 78.0 | 312 | 0.5950 | 0.9453 |
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+ | 0.0 | 79.0 | 316 | 0.5954 | 0.9453 |
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+ | 0.0 | 80.0 | 320 | 0.5959 | 0.9453 |
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+ | 0.0 | 81.0 | 324 | 0.5868 | 0.9453 |
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+ | 0.0 | 82.0 | 328 | 0.6180 | 0.9375 |
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+ | 0.0005 | 83.0 | 332 | 0.6404 | 0.9453 |
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+ | 0.0005 | 84.0 | 336 | 0.6560 | 0.9453 |
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+ | 0.0 | 85.0 | 340 | 0.6606 | 0.9453 |
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+ | 0.0 | 86.0 | 344 | 0.6415 | 0.9453 |
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+ | 0.0 | 87.0 | 348 | 0.5770 | 0.9453 |
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+ | 0.0247 | 88.0 | 352 | 0.5282 | 0.9375 |
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+ | 0.0247 | 89.0 | 356 | 0.5532 | 0.9453 |
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+ | 0.0 | 90.0 | 360 | 0.5550 | 0.9453 |
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+ | 0.0 | 91.0 | 364 | 0.5455 | 0.9453 |
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+ | 0.0 | 92.0 | 368 | 0.5395 | 0.9375 |
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+ | 0.0 | 93.0 | 372 | 0.5358 | 0.9375 |
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+ | 0.0 | 94.0 | 376 | 0.5333 | 0.9453 |
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+ | 0.0 | 95.0 | 380 | 0.5314 | 0.9453 |
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+ | 0.0 | 96.0 | 384 | 0.5303 | 0.9453 |
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+ | 0.0 | 97.0 | 388 | 0.5295 | 0.9453 |
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+ | 0.0 | 98.0 | 392 | 0.5288 | 0.9453 |
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+ | 0.0 | 99.0 | 396 | 0.5279 | 0.9453 |
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+ | 0.0 | 100.0 | 400 | 0.5270 | 0.9453 |
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+ | 0.0 | 101.0 | 404 | 0.5264 | 0.9453 |
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+ | 0.0 | 102.0 | 408 | 0.5260 | 0.9453 |
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+ | 0.0 | 103.0 | 412 | 0.5257 | 0.9453 |
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+ | 0.0 | 104.0 | 416 | 0.5256 | 0.9453 |
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+ | 0.0 | 105.0 | 420 | 0.5255 | 0.9453 |
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+ | 0.0 | 106.0 | 424 | 0.5255 | 0.9453 |
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+ | 0.0 | 107.0 | 428 | 0.5256 | 0.9453 |
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+ | 0.0 | 108.0 | 432 | 0.5257 | 0.9453 |
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+ | 0.0 | 109.0 | 436 | 0.5258 | 0.9453 |
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+ | 0.0 | 110.0 | 440 | 0.5259 | 0.9453 |
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+ | 0.0 | 111.0 | 444 | 0.5262 | 0.9453 |
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+ | 0.0 | 112.0 | 448 | 0.5264 | 0.9453 |
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+ | 0.0 | 113.0 | 452 | 0.5266 | 0.9453 |
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+ | 0.0 | 114.0 | 456 | 0.5265 | 0.9453 |
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+ | 0.0 | 115.0 | 460 | 0.5266 | 0.9453 |
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+ | 0.0 | 116.0 | 464 | 0.5268 | 0.9453 |
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+ | 0.0 | 117.0 | 468 | 0.5263 | 0.9453 |
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+ | 0.0 | 118.0 | 472 | 0.5401 | 0.9453 |
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+ | 0.0 | 119.0 | 476 | 0.5557 | 0.9453 |
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+ | 0.0 | 120.0 | 480 | 0.5663 | 0.9453 |
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+ | 0.0 | 121.0 | 484 | 0.5731 | 0.9453 |
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+ | 0.0 | 122.0 | 488 | 0.5776 | 0.9453 |
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+ | 0.0 | 123.0 | 492 | 0.5804 | 0.9453 |
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+ | 0.0 | 124.0 | 496 | 0.5823 | 0.9453 |
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+ | 0.0 | 125.0 | 500 | 0.5836 | 0.9453 |
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+ | 0.0 | 126.0 | 504 | 0.5842 | 0.9453 |
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+ | 0.0 | 127.0 | 508 | 0.5844 | 0.9453 |
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+ | 0.0 | 128.0 | 512 | 0.5844 | 0.9453 |
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+ | 0.0 | 129.0 | 516 | 0.5826 | 0.9453 |
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+ | 0.0 | 130.0 | 520 | 0.5813 | 0.9453 |
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+ | 0.0 | 131.0 | 524 | 0.5805 | 0.9453 |
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+ | 0.0 | 132.0 | 528 | 0.5800 | 0.9453 |
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+ | 0.0 | 133.0 | 532 | 0.5796 | 0.9453 |
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+ | 0.0 | 134.0 | 536 | 0.6100 | 0.9453 |
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+ | 0.0 | 135.0 | 540 | 0.6275 | 0.9453 |
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+ | 0.0 | 136.0 | 544 | 0.6351 | 0.9453 |
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+ | 0.0 | 137.0 | 548 | 0.6393 | 0.9453 |
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+ | 0.0 | 138.0 | 552 | 0.6424 | 0.9453 |
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+ | 0.0 | 139.0 | 556 | 0.6445 | 0.9453 |
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+ | 0.0 | 140.0 | 560 | 0.6439 | 0.9453 |
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+ | 0.0 | 141.0 | 564 | 0.6435 | 0.9453 |
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+ | 0.0 | 142.0 | 568 | 0.6431 | 0.9453 |
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+ | 0.0 | 143.0 | 572 | 0.6428 | 0.9453 |
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+ | 0.0 | 144.0 | 576 | 0.6425 | 0.9453 |
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+ | 0.0 | 145.0 | 580 | 0.6423 | 0.9453 |
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+ | 0.0 | 146.0 | 584 | 0.6422 | 0.9453 |
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+ | 0.0 | 147.0 | 588 | 0.6422 | 0.9453 |
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+ | 0.0 | 148.0 | 592 | 0.6421 | 0.9453 |
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+ | 0.0 | 149.0 | 596 | 0.6421 | 0.9453 |
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+ | 0.0 | 150.0 | 600 | 0.6421 | 0.9453 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.0.dev0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.4.0
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+ - Tokenizers 0.13.3