update model card README.md
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README.md
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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-64-42
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results: []
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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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# best_model-yelp_polarity-64-42
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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: 0.8838
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- Accuracy: 0.9141
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 1.0 | 4 | 1.0394 | 0.9141 |
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| No log | 2.0 | 8 | 1.0413 | 0.9141 |
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| 0.5047 | 3.0 | 12 | 1.0408 | 0.9141 |
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| 0.5047 | 4.0 | 16 | 1.0386 | 0.9141 |
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| 0.4566 | 5.0 | 20 | 1.0336 | 0.9141 |
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| 0.4566 | 6.0 | 24 | 1.0248 | 0.9141 |
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| 0.4566 | 7.0 | 28 | 1.0128 | 0.9141 |
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| 0.4026 | 8.0 | 32 | 1.0000 | 0.9141 |
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| 0.4026 | 9.0 | 36 | 0.9823 | 0.9141 |
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| 0.3103 | 10.0 | 40 | 0.9632 | 0.9141 |
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| 0.3103 | 11.0 | 44 | 0.9553 | 0.9219 |
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| 0.3103 | 12.0 | 48 | 0.9610 | 0.9141 |
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| 0.2537 | 13.0 | 52 | 0.9575 | 0.9141 |
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| 0.2537 | 14.0 | 56 | 0.9497 | 0.9141 |
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| 0.1335 | 15.0 | 60 | 0.9510 | 0.9141 |
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| 0.1335 | 16.0 | 64 | 0.9465 | 0.9141 |
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| 0.1335 | 17.0 | 68 | 0.9379 | 0.9141 |
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| 0.0655 | 18.0 | 72 | 0.9312 | 0.9141 |
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| 0.0655 | 19.0 | 76 | 0.9317 | 0.9141 |
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| 0.051 | 20.0 | 80 | 0.9246 | 0.9141 |
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| 0.051 | 21.0 | 84 | 0.9026 | 0.9141 |
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| 0.051 | 22.0 | 88 | 0.8836 | 0.9141 |
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| 0.0012 | 23.0 | 92 | 0.8697 | 0.9141 |
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| 0.0012 | 24.0 | 96 | 0.8588 | 0.9141 |
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| 0.0003 | 25.0 | 100 | 0.8458 | 0.9141 |
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| 0.0003 | 26.0 | 104 | 0.8323 | 0.9141 |
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| 0.0003 | 27.0 | 108 | 0.8499 | 0.9141 |
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| 0.0019 | 28.0 | 112 | 0.8750 | 0.9219 |
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| 0.0019 | 29.0 | 116 | 0.8897 | 0.9219 |
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| 0.0 | 30.0 | 120 | 0.8943 | 0.9219 |
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| 0.0 | 31.0 | 124 | 0.8570 | 0.9219 |
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| 0.0 | 32.0 | 128 | 0.8162 | 0.9219 |
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| 0.0065 | 33.0 | 132 | 0.8156 | 0.9141 |
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| 0.0065 | 34.0 | 136 | 0.8147 | 0.9141 |
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| 0.0137 | 35.0 | 140 | 0.8191 | 0.9219 |
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| 0.0137 | 36.0 | 144 | 0.8258 | 0.9219 |
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| 0.0137 | 37.0 | 148 | 0.8316 | 0.9141 |
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| 0.0 | 38.0 | 152 | 0.8362 | 0.9219 |
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| 0.0 | 39.0 | 156 | 0.8188 | 0.9141 |
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| 0.0001 | 40.0 | 160 | 0.8255 | 0.9141 |
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| 0.0001 | 41.0 | 164 | 0.8535 | 0.9062 |
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| 0.0001 | 42.0 | 168 | 0.8499 | 0.9062 |
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| 0.0017 | 43.0 | 172 | 0.8184 | 0.9141 |
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| 0.0017 | 44.0 | 176 | 0.8120 | 0.9297 |
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| 0.0 | 45.0 | 180 | 0.8277 | 0.9219 |
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| 0.0 | 46.0 | 184 | 0.8434 | 0.9219 |
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| 0.0 | 47.0 | 188 | 0.8535 | 0.9219 |
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| 0.0 | 48.0 | 192 | 0.8597 | 0.9219 |
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| 0.0 | 49.0 | 196 | 0.8633 | 0.9219 |
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| 0.0 | 50.0 | 200 | 0.8651 | 0.9219 |
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| 0.0 | 51.0 | 204 | 0.8617 | 0.9219 |
|
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| 0.0 | 52.0 | 208 | 0.8571 | 0.9219 |
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| 0.0 | 53.0 | 212 | 0.8538 | 0.9219 |
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| 0.0 | 54.0 | 216 | 0.8514 | 0.9219 |
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| 0.0 | 55.0 | 220 | 0.8346 | 0.9219 |
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| 0.0 | 56.0 | 224 | 0.8153 | 0.9219 |
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| 0.0 | 57.0 | 228 | 0.8087 | 0.9219 |
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| 0.0 | 58.0 | 232 | 0.8083 | 0.9141 |
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| 0.0 | 59.0 | 236 | 0.8168 | 0.9141 |
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| 0.0002 | 60.0 | 240 | 0.8424 | 0.9141 |
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| 0.0002 | 61.0 | 244 | 0.8614 | 0.9141 |
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| 0.0002 | 62.0 | 248 | 0.8736 | 0.9141 |
|
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| 0.0 | 63.0 | 252 | 0.8817 | 0.9141 |
|
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| 0.0 | 64.0 | 256 | 0.8848 | 0.9141 |
|
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| 0.0 | 65.0 | 260 | 0.8876 | 0.9141 |
|
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| 0.0 | 66.0 | 264 | 0.8896 | 0.9141 |
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| 0.0 | 67.0 | 268 | 0.8868 | 0.9141 |
|
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| 0.0 | 68.0 | 272 | 0.8831 | 0.9141 |
|
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| 0.0 | 69.0 | 276 | 0.8792 | 0.9141 |
|
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| 0.0001 | 70.0 | 280 | 0.8107 | 0.9141 |
|
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| 0.0001 | 71.0 | 284 | 0.9166 | 0.9219 |
|
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| 0.0001 | 72.0 | 288 | 0.8786 | 0.9219 |
|
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| 0.0232 | 73.0 | 292 | 0.8429 | 0.9219 |
|
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| 0.0232 | 74.0 | 296 | 0.8228 | 0.9297 |
|
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| 0.0 | 75.0 | 300 | 0.8332 | 0.9219 |
|
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| 0.0 | 76.0 | 304 | 0.8651 | 0.9062 |
|
129 |
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| 0.0 | 77.0 | 308 | 0.8879 | 0.9062 |
|
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| 0.0 | 78.0 | 312 | 0.9017 | 0.9062 |
|
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| 0.0 | 79.0 | 316 | 0.9093 | 0.9062 |
|
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+
| 0.0 | 80.0 | 320 | 0.9133 | 0.9062 |
|
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| 0.0 | 81.0 | 324 | 0.9160 | 0.9062 |
|
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| 0.0 | 82.0 | 328 | 0.9180 | 0.9062 |
|
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| 0.0 | 83.0 | 332 | 0.9192 | 0.9062 |
|
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| 0.0 | 84.0 | 336 | 0.9196 | 0.9062 |
|
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| 0.0 | 85.0 | 340 | 0.9209 | 0.9062 |
|
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| 0.0 | 86.0 | 344 | 0.9250 | 0.9062 |
|
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| 0.0 | 87.0 | 348 | 0.9289 | 0.9062 |
|
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| 0.0 | 88.0 | 352 | 0.9314 | 0.9062 |
|
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| 0.0 | 89.0 | 356 | 0.9330 | 0.9062 |
|
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| 0.0 | 90.0 | 360 | 0.9340 | 0.9062 |
|
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| 0.0 | 91.0 | 364 | 0.9346 | 0.9062 |
|
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| 0.0 | 92.0 | 368 | 0.9348 | 0.9062 |
|
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| 0.0 | 93.0 | 372 | 0.9351 | 0.9062 |
|
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| 0.0 | 94.0 | 376 | 0.9354 | 0.9062 |
|
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| 0.0 | 95.0 | 380 | 0.9355 | 0.9062 |
|
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| 0.0 | 96.0 | 384 | 0.9354 | 0.9062 |
|
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| 0.0 | 97.0 | 388 | 0.9339 | 0.9062 |
|
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| 0.0 | 98.0 | 392 | 0.9310 | 0.9062 |
|
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| 0.0 | 99.0 | 396 | 0.9290 | 0.9062 |
|
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| 0.0 | 100.0 | 400 | 0.9276 | 0.9062 |
|
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| 0.0 | 101.0 | 404 | 0.9271 | 0.9062 |
|
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| 0.0 | 102.0 | 408 | 0.9274 | 0.9062 |
|
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| 0.0 | 103.0 | 412 | 0.9277 | 0.9062 |
|
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| 0.0 | 104.0 | 416 | 0.9282 | 0.9062 |
|
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| 0.0 | 105.0 | 420 | 0.9285 | 0.9062 |
|
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| 0.0 | 106.0 | 424 | 0.9289 | 0.9062 |
|
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| 0.0 | 107.0 | 428 | 0.9293 | 0.9062 |
|
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| 0.0 | 108.0 | 432 | 0.9297 | 0.9062 |
|
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| 0.0 | 109.0 | 436 | 0.9296 | 0.9062 |
|
162 |
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| 0.0 | 110.0 | 440 | 0.9297 | 0.9062 |
|
163 |
+
| 0.0 | 111.0 | 444 | 0.9328 | 0.9062 |
|
164 |
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| 0.0 | 112.0 | 448 | 0.9376 | 0.9062 |
|
165 |
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| 0.0 | 113.0 | 452 | 0.9408 | 0.9062 |
|
166 |
+
| 0.0 | 114.0 | 456 | 0.9428 | 0.9062 |
|
167 |
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| 0.0 | 115.0 | 460 | 0.9442 | 0.9062 |
|
168 |
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| 0.0 | 116.0 | 464 | 0.9455 | 0.9062 |
|
169 |
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| 0.0 | 117.0 | 468 | 0.9464 | 0.9062 |
|
170 |
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| 0.0 | 118.0 | 472 | 0.9470 | 0.9062 |
|
171 |
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| 0.0 | 119.0 | 476 | 0.9478 | 0.9062 |
|
172 |
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| 0.0 | 120.0 | 480 | 0.9487 | 0.9062 |
|
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| 0.0 | 121.0 | 484 | 0.9492 | 0.9062 |
|
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| 0.0 | 122.0 | 488 | 0.9496 | 0.9062 |
|
175 |
+
| 0.0 | 123.0 | 492 | 0.9499 | 0.9062 |
|
176 |
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| 0.0 | 124.0 | 496 | 0.9504 | 0.9062 |
|
177 |
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| 0.0 | 125.0 | 500 | 0.9505 | 0.9062 |
|
178 |
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| 0.0 | 126.0 | 504 | 0.9507 | 0.9062 |
|
179 |
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| 0.0 | 127.0 | 508 | 0.9509 | 0.9062 |
|
180 |
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| 0.0 | 128.0 | 512 | 0.9504 | 0.9062 |
|
181 |
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| 0.0 | 129.0 | 516 | 0.9502 | 0.9062 |
|
182 |
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| 0.0 | 130.0 | 520 | 0.9500 | 0.9062 |
|
183 |
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| 0.0 | 131.0 | 524 | 0.9497 | 0.9062 |
|
184 |
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| 0.0 | 132.0 | 528 | 0.9496 | 0.9062 |
|
185 |
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| 0.0 | 133.0 | 532 | 0.9496 | 0.9062 |
|
186 |
+
| 0.0 | 134.0 | 536 | 0.9498 | 0.9062 |
|
187 |
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| 0.0 | 135.0 | 540 | 0.9502 | 0.9062 |
|
188 |
+
| 0.0 | 136.0 | 544 | 0.9398 | 0.9062 |
|
189 |
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| 0.0 | 137.0 | 548 | 0.9199 | 0.9062 |
|
190 |
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| 0.0 | 138.0 | 552 | 0.9047 | 0.9062 |
|
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| 0.0 | 139.0 | 556 | 0.8950 | 0.9141 |
|
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| 0.0 | 140.0 | 560 | 0.8894 | 0.9141 |
|
193 |
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| 0.0 | 141.0 | 564 | 0.8862 | 0.9141 |
|
194 |
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| 0.0 | 142.0 | 568 | 0.8846 | 0.9141 |
|
195 |
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| 0.0 | 143.0 | 572 | 0.8840 | 0.9141 |
|
196 |
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| 0.0 | 144.0 | 576 | 0.8837 | 0.9141 |
|
197 |
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| 0.0 | 145.0 | 580 | 0.8836 | 0.9141 |
|
198 |
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| 0.0 | 146.0 | 584 | 0.8836 | 0.9141 |
|
199 |
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| 0.0 | 147.0 | 588 | 0.8837 | 0.9141 |
|
200 |
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| 0.0 | 148.0 | 592 | 0.8838 | 0.9141 |
|
201 |
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| 0.0 | 149.0 | 596 | 0.8838 | 0.9141 |
|
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| 0.0 | 150.0 | 600 | 0.8838 | 0.9141 |
|
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|
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### Framework versions
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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
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