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simonycl
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best_model-sst-2-64-87

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ base_model: bert-base-uncased
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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-sst-2-64-87
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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-sst-2-64-87
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+
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2746
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+ - Accuracy: 0.8438
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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 | 1.3247 | 0.8438 |
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+ | No log | 2.0 | 8 | 1.3227 | 0.8438 |
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+ | 0.7148 | 3.0 | 12 | 1.3195 | 0.8438 |
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+ | 0.7148 | 4.0 | 16 | 1.3169 | 0.8359 |
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+ | 0.6114 | 5.0 | 20 | 1.3149 | 0.8359 |
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+ | 0.6114 | 6.0 | 24 | 1.3101 | 0.8359 |
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+ | 0.6114 | 7.0 | 28 | 1.2982 | 0.8438 |
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+ | 0.5794 | 8.0 | 32 | 1.2836 | 0.8438 |
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+ | 0.5794 | 9.0 | 36 | 1.2655 | 0.8438 |
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+ | 0.5231 | 10.0 | 40 | 1.2497 | 0.8438 |
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+ | 0.5231 | 11.0 | 44 | 1.2410 | 0.8438 |
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+ | 0.5231 | 12.0 | 48 | 1.2307 | 0.8438 |
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+ | 0.4052 | 13.0 | 52 | 1.2154 | 0.8438 |
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+ | 0.4052 | 14.0 | 56 | 1.2001 | 0.8438 |
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+ | 0.363 | 15.0 | 60 | 1.1877 | 0.8438 |
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+ | 0.363 | 16.0 | 64 | 1.1760 | 0.8516 |
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+ | 0.363 | 17.0 | 68 | 1.1836 | 0.8516 |
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+ | 0.2969 | 18.0 | 72 | 1.1848 | 0.8594 |
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+ | 0.2969 | 19.0 | 76 | 1.1823 | 0.8516 |
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+ | 0.1866 | 20.0 | 80 | 1.1867 | 0.8516 |
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+ | 0.1866 | 21.0 | 84 | 1.1795 | 0.8516 |
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+ | 0.1866 | 22.0 | 88 | 1.1756 | 0.8516 |
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+ | 0.1502 | 23.0 | 92 | 1.1731 | 0.8516 |
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+ | 0.1502 | 24.0 | 96 | 1.1680 | 0.8516 |
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+ | 0.0974 | 25.0 | 100 | 1.1489 | 0.8516 |
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+ | 0.0974 | 26.0 | 104 | 1.1088 | 0.8516 |
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+ | 0.0974 | 27.0 | 108 | 1.0986 | 0.8594 |
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+ | 0.0992 | 28.0 | 112 | 1.0879 | 0.8594 |
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+ | 0.0992 | 29.0 | 116 | 1.0850 | 0.8594 |
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+ | 0.0065 | 30.0 | 120 | 1.1056 | 0.8594 |
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+ | 0.0065 | 31.0 | 124 | 1.1355 | 0.8516 |
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+ | 0.0065 | 32.0 | 128 | 1.1457 | 0.8438 |
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+ | 0.0185 | 33.0 | 132 | 1.1518 | 0.8438 |
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+ | 0.0185 | 34.0 | 136 | 1.1437 | 0.8438 |
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+ | 0.0123 | 35.0 | 140 | 1.1230 | 0.8516 |
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+ | 0.0123 | 36.0 | 144 | 1.1109 | 0.8516 |
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+ | 0.0123 | 37.0 | 148 | 1.1093 | 0.8594 |
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+ | 0.0001 | 38.0 | 152 | 1.1085 | 0.8594 |
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+ | 0.0001 | 39.0 | 156 | 1.1092 | 0.8594 |
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+ | 0.008 | 40.0 | 160 | 1.1163 | 0.8594 |
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+ | 0.008 | 41.0 | 164 | 1.1272 | 0.8516 |
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+ | 0.008 | 42.0 | 168 | 1.1351 | 0.8516 |
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+ | 0.0001 | 43.0 | 172 | 1.1365 | 0.8516 |
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+ | 0.0001 | 44.0 | 176 | 1.1287 | 0.8516 |
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+ | 0.0007 | 45.0 | 180 | 1.1195 | 0.8594 |
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+ | 0.0007 | 46.0 | 184 | 1.1110 | 0.8594 |
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+ | 0.0007 | 47.0 | 188 | 1.1261 | 0.8594 |
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+ | 0.0003 | 48.0 | 192 | 1.1236 | 0.8594 |
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+ | 0.0003 | 49.0 | 196 | 1.1083 | 0.8594 |
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+ | 0.0018 | 50.0 | 200 | 1.1057 | 0.8594 |
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+ | 0.0018 | 51.0 | 204 | 1.1077 | 0.8594 |
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+ | 0.0018 | 52.0 | 208 | 1.1095 | 0.8516 |
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+ | 0.0001 | 53.0 | 212 | 1.1116 | 0.8594 |
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+ | 0.0001 | 54.0 | 216 | 1.1149 | 0.8594 |
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+ | 0.0017 | 55.0 | 220 | 1.1500 | 0.8516 |
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+ | 0.0017 | 56.0 | 224 | 1.1396 | 0.8516 |
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+ | 0.0017 | 57.0 | 228 | 1.1474 | 0.8516 |
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+ | 0.0002 | 58.0 | 232 | 1.1402 | 0.8594 |
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+ | 0.0002 | 59.0 | 236 | 1.1367 | 0.8594 |
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+ | 0.0001 | 60.0 | 240 | 1.1349 | 0.8516 |
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+ | 0.0001 | 61.0 | 244 | 1.1350 | 0.8516 |
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+ | 0.0001 | 62.0 | 248 | 1.1366 | 0.8516 |
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+ | 0.0001 | 63.0 | 252 | 1.1389 | 0.8594 |
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+ | 0.0001 | 64.0 | 256 | 1.1395 | 0.8594 |
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+ | 0.0001 | 65.0 | 260 | 1.1380 | 0.8594 |
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+ | 0.0001 | 66.0 | 264 | 1.1378 | 0.8594 |
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+ | 0.0001 | 67.0 | 268 | 1.1411 | 0.8594 |
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+ | 0.0001 | 68.0 | 272 | 1.1439 | 0.8594 |
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+ | 0.0001 | 69.0 | 276 | 1.1452 | 0.8594 |
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+ | 0.0122 | 70.0 | 280 | 1.1270 | 0.8594 |
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+ | 0.0122 | 71.0 | 284 | 1.1514 | 0.8594 |
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+ | 0.0122 | 72.0 | 288 | 1.1908 | 0.8516 |
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+ | 0.0001 | 73.0 | 292 | 1.2155 | 0.8516 |
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+ | 0.0001 | 74.0 | 296 | 1.2281 | 0.8516 |
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+ | 0.0001 | 75.0 | 300 | 1.2353 | 0.8516 |
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+ | 0.0001 | 76.0 | 304 | 1.2387 | 0.8516 |
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+ | 0.0001 | 77.0 | 308 | 1.2380 | 0.8516 |
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+ | 0.0177 | 78.0 | 312 | 1.1050 | 0.8594 |
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+ | 0.0177 | 79.0 | 316 | 1.1201 | 0.8594 |
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+ | 0.0123 | 80.0 | 320 | 1.1227 | 0.8516 |
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+ | 0.0123 | 81.0 | 324 | 1.1249 | 0.8594 |
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+ | 0.0123 | 82.0 | 328 | 1.1305 | 0.8594 |
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+ | 0.0001 | 83.0 | 332 | 1.1371 | 0.8672 |
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+ | 0.0001 | 84.0 | 336 | 1.1424 | 0.8672 |
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+ | 0.0001 | 85.0 | 340 | 1.1449 | 0.8672 |
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+ | 0.0001 | 86.0 | 344 | 1.1464 | 0.8672 |
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+ | 0.0001 | 87.0 | 348 | 1.1469 | 0.8672 |
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+ | 0.0001 | 88.0 | 352 | 1.1448 | 0.8594 |
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+ | 0.0001 | 89.0 | 356 | 1.1444 | 0.8594 |
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+ | 0.0 | 90.0 | 360 | 1.1452 | 0.8594 |
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+ | 0.0 | 91.0 | 364 | 1.1464 | 0.8594 |
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+ | 0.0 | 92.0 | 368 | 1.1484 | 0.8594 |
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+ | 0.0001 | 93.0 | 372 | 1.1504 | 0.8594 |
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+ | 0.0001 | 94.0 | 376 | 1.1521 | 0.8516 |
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+ | 0.0 | 95.0 | 380 | 1.1537 | 0.8516 |
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+ | 0.0 | 96.0 | 384 | 1.1553 | 0.8516 |
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+ | 0.0 | 97.0 | 388 | 1.1571 | 0.8516 |
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+ | 0.0001 | 98.0 | 392 | 1.1605 | 0.8594 |
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+ | 0.0001 | 99.0 | 396 | 1.1645 | 0.8594 |
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+ | 0.0 | 100.0 | 400 | 1.1678 | 0.8594 |
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+ | 0.0 | 101.0 | 404 | 1.1706 | 0.8594 |
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+ | 0.0 | 102.0 | 408 | 1.1729 | 0.8594 |
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+ | 0.0 | 103.0 | 412 | 1.1747 | 0.8594 |
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+ | 0.0 | 104.0 | 416 | 1.1762 | 0.8594 |
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+ | 0.0001 | 105.0 | 420 | 1.1777 | 0.8594 |
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+ | 0.0001 | 106.0 | 424 | 1.1792 | 0.8594 |
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+ | 0.0001 | 107.0 | 428 | 1.1808 | 0.8594 |
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+ | 0.0034 | 108.0 | 432 | 1.2561 | 0.8516 |
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+ | 0.0034 | 109.0 | 436 | 1.3098 | 0.8516 |
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+ | 0.0063 | 110.0 | 440 | 1.2197 | 0.8516 |
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+ | 0.0063 | 111.0 | 444 | 1.1982 | 0.8516 |
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+ | 0.0063 | 112.0 | 448 | 1.2230 | 0.8516 |
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+ | 0.0 | 113.0 | 452 | 1.2172 | 0.8594 |
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+ | 0.0 | 114.0 | 456 | 1.2165 | 0.8516 |
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+ | 0.0 | 115.0 | 460 | 1.2187 | 0.8516 |
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+ | 0.0 | 116.0 | 464 | 1.2213 | 0.8516 |
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+ | 0.0 | 117.0 | 468 | 1.2234 | 0.8516 |
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+ | 0.0 | 118.0 | 472 | 1.2248 | 0.8516 |
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+ | 0.0 | 119.0 | 476 | 1.2267 | 0.8516 |
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+ | 0.0 | 120.0 | 480 | 1.2288 | 0.8594 |
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+ | 0.0 | 121.0 | 484 | 1.2316 | 0.8594 |
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+ | 0.0 | 122.0 | 488 | 1.2342 | 0.8594 |
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+ | 0.0 | 123.0 | 492 | 1.2364 | 0.8594 |
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+ | 0.0 | 124.0 | 496 | 1.2436 | 0.8594 |
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+ | 0.001 | 125.0 | 500 | 1.2770 | 0.8438 |
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+ | 0.001 | 126.0 | 504 | 1.3138 | 0.8594 |
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+ | 0.001 | 127.0 | 508 | 1.3084 | 0.8594 |
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+ | 0.0 | 128.0 | 512 | 1.3102 | 0.8438 |
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+ | 0.0 | 129.0 | 516 | 1.3333 | 0.8438 |
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+ | 0.0002 | 130.0 | 520 | 1.3251 | 0.8516 |
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+ | 0.0002 | 131.0 | 524 | 1.2928 | 0.8594 |
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+ | 0.0002 | 132.0 | 528 | 1.2468 | 0.8438 |
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+ | 0.0 | 133.0 | 532 | 1.2295 | 0.8438 |
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+ | 0.0 | 134.0 | 536 | 1.2483 | 0.8438 |
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+ | 0.0 | 135.0 | 540 | 1.2652 | 0.8438 |
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+ | 0.0 | 136.0 | 544 | 1.2741 | 0.8438 |
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+ | 0.0 | 137.0 | 548 | 1.2786 | 0.8438 |
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+ | 0.0 | 138.0 | 552 | 1.2811 | 0.8438 |
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+ | 0.0 | 139.0 | 556 | 1.2824 | 0.8438 |
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+ | 0.0 | 140.0 | 560 | 1.2833 | 0.8438 |
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+ | 0.0 | 141.0 | 564 | 1.2837 | 0.8438 |
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+ | 0.0 | 142.0 | 568 | 1.2833 | 0.8438 |
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+ | 0.0 | 143.0 | 572 | 1.2830 | 0.8438 |
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+ | 0.0 | 144.0 | 576 | 1.2828 | 0.8438 |
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+ | 0.0 | 145.0 | 580 | 1.2827 | 0.8438 |
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+ | 0.0 | 146.0 | 584 | 1.2827 | 0.8438 |
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+ | 0.0 | 147.0 | 588 | 1.2827 | 0.8438 |
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+ | 0.0001 | 148.0 | 592 | 1.2786 | 0.8438 |
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+ | 0.0001 | 149.0 | 596 | 1.2755 | 0.8438 |
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+ | 0.0 | 150.0 | 600 | 1.2746 | 0.8438 |
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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