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simonycl
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roberta-large-sst-2-64-13

Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Inference Endpoints
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+ ---
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+ license: mit
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+ base_model: roberta-large
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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: roberta-large-sst-2-64-13
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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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+ # roberta-large-sst-2-64-13
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+
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+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7488
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+ - Accuracy: 0.9141
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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: 50
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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 | 0.7118 | 0.5 |
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+ | No log | 2.0 | 8 | 0.7101 | 0.5 |
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+ | 0.7289 | 3.0 | 12 | 0.7072 | 0.5 |
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+ | 0.7289 | 4.0 | 16 | 0.7042 | 0.5 |
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+ | 0.6989 | 5.0 | 20 | 0.6999 | 0.5 |
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+ | 0.6989 | 6.0 | 24 | 0.6966 | 0.5 |
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+ | 0.6989 | 7.0 | 28 | 0.6938 | 0.5 |
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+ | 0.6959 | 8.0 | 32 | 0.6938 | 0.5 |
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+ | 0.6959 | 9.0 | 36 | 0.6990 | 0.4766 |
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+ | 0.6977 | 10.0 | 40 | 0.6931 | 0.5 |
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+ | 0.6977 | 11.0 | 44 | 0.6854 | 0.5156 |
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+ | 0.6977 | 12.0 | 48 | 0.6882 | 0.6016 |
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+ | 0.6514 | 13.0 | 52 | 0.6495 | 0.7578 |
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+ | 0.6514 | 14.0 | 56 | 0.5930 | 0.7656 |
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+ | 0.5232 | 15.0 | 60 | 0.5280 | 0.8203 |
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+ | 0.5232 | 16.0 | 64 | 0.4286 | 0.875 |
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+ | 0.5232 | 17.0 | 68 | 0.2916 | 0.8906 |
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+ | 0.2793 | 18.0 | 72 | 0.3444 | 0.9141 |
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+ | 0.2793 | 19.0 | 76 | 0.4673 | 0.8984 |
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+ | 0.0537 | 20.0 | 80 | 0.4232 | 0.9062 |
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+ | 0.0537 | 21.0 | 84 | 0.4351 | 0.9297 |
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+ | 0.0537 | 22.0 | 88 | 0.5124 | 0.9297 |
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+ | 0.0032 | 23.0 | 92 | 0.4585 | 0.9375 |
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+ | 0.0032 | 24.0 | 96 | 0.5067 | 0.9219 |
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+ | 0.0016 | 25.0 | 100 | 0.5244 | 0.9375 |
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+ | 0.0016 | 26.0 | 104 | 0.7050 | 0.9141 |
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+ | 0.0016 | 27.0 | 108 | 0.5847 | 0.9297 |
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+ | 0.0004 | 28.0 | 112 | 0.5744 | 0.9297 |
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+ | 0.0004 | 29.0 | 116 | 0.5828 | 0.9375 |
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+ | 0.0001 | 30.0 | 120 | 0.5884 | 0.9375 |
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+ | 0.0001 | 31.0 | 124 | 0.5931 | 0.9375 |
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+ | 0.0001 | 32.0 | 128 | 0.5983 | 0.9375 |
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+ | 0.0001 | 33.0 | 132 | 0.6038 | 0.9375 |
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+ | 0.0001 | 34.0 | 136 | 0.6076 | 0.9375 |
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+ | 0.0001 | 35.0 | 140 | 0.6083 | 0.9375 |
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+ | 0.0001 | 36.0 | 144 | 0.7169 | 0.9219 |
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+ | 0.0001 | 37.0 | 148 | 0.6166 | 0.9375 |
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+ | 0.0336 | 38.0 | 152 | 0.8108 | 0.9141 |
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+ | 0.0336 | 39.0 | 156 | 0.7454 | 0.9141 |
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+ | 0.0348 | 40.0 | 160 | 0.6944 | 0.9141 |
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+ | 0.0348 | 41.0 | 164 | 0.7467 | 0.9141 |
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+ | 0.0348 | 42.0 | 168 | 0.6764 | 0.9141 |
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+ | 0.0402 | 43.0 | 172 | 0.6839 | 0.9219 |
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+ | 0.0402 | 44.0 | 176 | 0.7118 | 0.9219 |
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+ | 0.0002 | 45.0 | 180 | 0.6943 | 0.9219 |
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+ | 0.0002 | 46.0 | 184 | 0.7469 | 0.9141 |
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+ | 0.0002 | 47.0 | 188 | 0.7264 | 0.9219 |
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+ | 0.0001 | 48.0 | 192 | 0.7112 | 0.9219 |
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+ | 0.0001 | 49.0 | 196 | 0.6948 | 0.9219 |
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+ | 0.0001 | 50.0 | 200 | 0.8408 | 0.9062 |
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+ | 0.0001 | 51.0 | 204 | 0.7876 | 0.9141 |
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+ | 0.0001 | 52.0 | 208 | 0.7271 | 0.9219 |
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+ | 0.0001 | 53.0 | 212 | 0.8016 | 0.9141 |
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+ | 0.0001 | 54.0 | 216 | 0.8336 | 0.9062 |
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+ | 0.0148 | 55.0 | 220 | 0.7701 | 0.9219 |
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+ | 0.0148 | 56.0 | 224 | 0.8717 | 0.9062 |
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+ | 0.0148 | 57.0 | 228 | 0.8018 | 0.9141 |
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+ | 0.0001 | 58.0 | 232 | 0.8777 | 0.9062 |
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+ | 0.0001 | 59.0 | 236 | 0.9158 | 0.9062 |
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+ | 0.0001 | 60.0 | 240 | 0.9356 | 0.8984 |
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+ | 0.0001 | 61.0 | 244 | 0.7494 | 0.9062 |
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+ | 0.0001 | 62.0 | 248 | 0.6708 | 0.9219 |
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+ | 0.0298 | 63.0 | 252 | 0.6649 | 0.9141 |
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+ | 0.0298 | 64.0 | 256 | 0.7463 | 0.9062 |
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+ | 0.0285 | 65.0 | 260 | 0.8065 | 0.8984 |
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+ | 0.0285 | 66.0 | 264 | 0.8267 | 0.9062 |
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+ | 0.0285 | 67.0 | 268 | 0.8447 | 0.8984 |
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+ | 0.0001 | 68.0 | 272 | 0.8409 | 0.8984 |
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+ | 0.0001 | 69.0 | 276 | 0.6652 | 0.9219 |
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+ | 0.0005 | 70.0 | 280 | 0.6507 | 0.9219 |
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+ | 0.0005 | 71.0 | 284 | 0.6889 | 0.9062 |
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+ | 0.0005 | 72.0 | 288 | 0.6652 | 0.9062 |
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+ | 0.0296 | 73.0 | 292 | 0.6454 | 0.9062 |
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+ | 0.0296 | 74.0 | 296 | 0.6368 | 0.9062 |
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+ | 0.0002 | 75.0 | 300 | 0.6396 | 0.9062 |
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+ | 0.0002 | 76.0 | 304 | 0.6505 | 0.9062 |
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+ | 0.0002 | 77.0 | 308 | 0.6620 | 0.9062 |
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+ | 0.0002 | 78.0 | 312 | 0.6734 | 0.9062 |
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+ | 0.0002 | 79.0 | 316 | 0.6846 | 0.9062 |
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+ | 0.0002 | 80.0 | 320 | 0.6951 | 0.9062 |
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+ | 0.0002 | 81.0 | 324 | 0.7038 | 0.9062 |
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+ | 0.0002 | 82.0 | 328 | 0.7116 | 0.9062 |
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+ | 0.0002 | 83.0 | 332 | 0.7187 | 0.9062 |
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+ | 0.0002 | 84.0 | 336 | 0.7250 | 0.9062 |
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+ | 0.0002 | 85.0 | 340 | 0.6930 | 0.9141 |
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+ | 0.0002 | 86.0 | 344 | 0.6856 | 0.9219 |
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+ | 0.0002 | 87.0 | 348 | 0.7474 | 0.9141 |
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+ | 0.0227 | 88.0 | 352 | 0.6506 | 0.9219 |
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+ | 0.0227 | 89.0 | 356 | 0.6457 | 0.9219 |
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+ | 0.0001 | 90.0 | 360 | 0.7022 | 0.9141 |
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+ | 0.0001 | 91.0 | 364 | 0.7275 | 0.9062 |
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+ | 0.0001 | 92.0 | 368 | 0.7375 | 0.9141 |
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+ | 0.0001 | 93.0 | 372 | 0.8008 | 0.9062 |
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+ | 0.0001 | 94.0 | 376 | 0.6855 | 0.9141 |
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+ | 0.0053 | 95.0 | 380 | 0.5869 | 0.9375 |
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+ | 0.0053 | 96.0 | 384 | 0.6060 | 0.9297 |
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+ | 0.0053 | 97.0 | 388 | 0.5990 | 0.9297 |
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+ | 0.0001 | 98.0 | 392 | 0.6250 | 0.9141 |
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+ | 0.0001 | 99.0 | 396 | 0.6505 | 0.9141 |
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+ | 0.0001 | 100.0 | 400 | 0.6577 | 0.9141 |
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+ | 0.0001 | 101.0 | 404 | 0.6594 | 0.9141 |
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+ | 0.0001 | 102.0 | 408 | 0.6602 | 0.9141 |
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+ | 0.0001 | 103.0 | 412 | 0.6610 | 0.9219 |
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+ | 0.0001 | 104.0 | 416 | 0.6622 | 0.9141 |
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+ | 0.037 | 105.0 | 420 | 0.6055 | 0.9297 |
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+ | 0.037 | 106.0 | 424 | 0.5915 | 0.9297 |
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+ | 0.037 | 107.0 | 428 | 0.6261 | 0.9297 |
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+ | 0.0001 | 108.0 | 432 | 0.6679 | 0.9219 |
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+ | 0.0001 | 109.0 | 436 | 0.7106 | 0.9219 |
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+ | 0.0001 | 110.0 | 440 | 0.7223 | 0.9219 |
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+ | 0.0001 | 111.0 | 444 | 0.7267 | 0.9141 |
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+ | 0.0001 | 112.0 | 448 | 0.7287 | 0.9141 |
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+ | 0.0001 | 113.0 | 452 | 0.7298 | 0.9141 |
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+ | 0.0001 | 114.0 | 456 | 0.7306 | 0.9141 |
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+ | 0.0001 | 115.0 | 460 | 0.7314 | 0.9141 |
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+ | 0.0001 | 116.0 | 464 | 0.7323 | 0.9141 |
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+ | 0.0001 | 117.0 | 468 | 0.7333 | 0.9141 |
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+ | 0.0001 | 118.0 | 472 | 0.7342 | 0.9141 |
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+ | 0.0001 | 119.0 | 476 | 0.7351 | 0.9141 |
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+ | 0.0001 | 120.0 | 480 | 0.7359 | 0.9141 |
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+ | 0.0001 | 121.0 | 484 | 0.7369 | 0.9141 |
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+ | 0.0001 | 122.0 | 488 | 0.7379 | 0.9141 |
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+ | 0.0001 | 123.0 | 492 | 0.7388 | 0.9141 |
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+ | 0.0001 | 124.0 | 496 | 0.7396 | 0.9141 |
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+ | 0.0001 | 125.0 | 500 | 0.7403 | 0.9141 |
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+ | 0.0001 | 126.0 | 504 | 0.7410 | 0.9141 |
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+ | 0.0001 | 127.0 | 508 | 0.7417 | 0.9141 |
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+ | 0.0001 | 128.0 | 512 | 0.7423 | 0.9141 |
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+ | 0.0001 | 129.0 | 516 | 0.7429 | 0.9141 |
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+ | 0.0001 | 130.0 | 520 | 0.7435 | 0.9141 |
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+ | 0.0001 | 131.0 | 524 | 0.7440 | 0.9141 |
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+ | 0.0001 | 132.0 | 528 | 0.7446 | 0.9141 |
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+ | 0.0001 | 133.0 | 532 | 0.7450 | 0.9141 |
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+ | 0.0001 | 134.0 | 536 | 0.7455 | 0.9141 |
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+ | 0.0001 | 135.0 | 540 | 0.7459 | 0.9141 |
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+ | 0.0001 | 136.0 | 544 | 0.7463 | 0.9141 |
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+ | 0.0001 | 137.0 | 548 | 0.7466 | 0.9141 |
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+ | 0.0001 | 138.0 | 552 | 0.7470 | 0.9141 |
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+ | 0.0001 | 139.0 | 556 | 0.7473 | 0.9141 |
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+ | 0.0001 | 140.0 | 560 | 0.7475 | 0.9141 |
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+ | 0.0001 | 141.0 | 564 | 0.7478 | 0.9141 |
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+ | 0.0001 | 142.0 | 568 | 0.7480 | 0.9141 |
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+ | 0.0001 | 143.0 | 572 | 0.7482 | 0.9141 |
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+ | 0.0001 | 144.0 | 576 | 0.7483 | 0.9141 |
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+ | 0.0001 | 145.0 | 580 | 0.7485 | 0.9141 |
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+ | 0.0001 | 146.0 | 584 | 0.7486 | 0.9141 |
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+ | 0.0001 | 147.0 | 588 | 0.7487 | 0.9141 |
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+ | 0.0001 | 148.0 | 592 | 0.7488 | 0.9141 |
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+ | 0.0001 | 149.0 | 596 | 0.7488 | 0.9141 |
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+ | 0.0001 | 150.0 | 600 | 0.7488 | 0.9141 |
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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