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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- super_glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: '20230826130948' |
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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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# 20230826130948 |
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This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5311 |
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- Accuracy: 0.65 |
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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: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 11 |
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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: 80.0 |
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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 | 25 | 0.5416 | 0.66 | |
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| No log | 2.0 | 50 | 0.5394 | 0.64 | |
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| No log | 3.0 | 75 | 0.5376 | 0.65 | |
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| No log | 4.0 | 100 | 0.5476 | 0.65 | |
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| No log | 5.0 | 125 | 0.5371 | 0.64 | |
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| No log | 6.0 | 150 | 0.5442 | 0.63 | |
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| No log | 7.0 | 175 | 0.5413 | 0.65 | |
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| No log | 8.0 | 200 | 0.5381 | 0.65 | |
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| No log | 9.0 | 225 | 0.5366 | 0.65 | |
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| No log | 10.0 | 250 | 0.5402 | 0.65 | |
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| No log | 11.0 | 275 | 0.5405 | 0.65 | |
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| No log | 12.0 | 300 | 0.5396 | 0.65 | |
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| No log | 13.0 | 325 | 0.5379 | 0.66 | |
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| No log | 14.0 | 350 | 0.5375 | 0.66 | |
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| No log | 15.0 | 375 | 0.5393 | 0.65 | |
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| No log | 16.0 | 400 | 0.5371 | 0.66 | |
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| No log | 17.0 | 425 | 0.5286 | 0.66 | |
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| No log | 18.0 | 450 | 0.5313 | 0.65 | |
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| No log | 19.0 | 475 | 0.5427 | 0.62 | |
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| 0.616 | 20.0 | 500 | 0.5469 | 0.63 | |
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| 0.616 | 21.0 | 525 | 0.5348 | 0.65 | |
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| 0.616 | 22.0 | 550 | 0.5352 | 0.64 | |
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| 0.616 | 23.0 | 575 | 0.5434 | 0.63 | |
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| 0.616 | 24.0 | 600 | 0.5437 | 0.62 | |
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| 0.616 | 25.0 | 625 | 0.5344 | 0.65 | |
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| 0.616 | 26.0 | 650 | 0.5344 | 0.66 | |
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| 0.616 | 27.0 | 675 | 0.5319 | 0.66 | |
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| 0.616 | 28.0 | 700 | 0.5329 | 0.66 | |
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| 0.616 | 29.0 | 725 | 0.5313 | 0.66 | |
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| 0.616 | 30.0 | 750 | 0.5321 | 0.66 | |
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| 0.616 | 31.0 | 775 | 0.5342 | 0.65 | |
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| 0.616 | 32.0 | 800 | 0.5364 | 0.66 | |
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| 0.616 | 33.0 | 825 | 0.5350 | 0.65 | |
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| 0.616 | 34.0 | 850 | 0.5382 | 0.65 | |
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| 0.616 | 35.0 | 875 | 0.5330 | 0.65 | |
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| 0.616 | 36.0 | 900 | 0.5361 | 0.64 | |
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| 0.616 | 37.0 | 925 | 0.5379 | 0.63 | |
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| 0.616 | 38.0 | 950 | 0.5314 | 0.64 | |
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| 0.616 | 39.0 | 975 | 0.5308 | 0.65 | |
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| 0.6054 | 40.0 | 1000 | 0.5348 | 0.65 | |
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| 0.6054 | 41.0 | 1025 | 0.5374 | 0.64 | |
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| 0.6054 | 42.0 | 1050 | 0.5363 | 0.64 | |
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| 0.6054 | 43.0 | 1075 | 0.5361 | 0.64 | |
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| 0.6054 | 44.0 | 1100 | 0.5333 | 0.65 | |
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| 0.6054 | 45.0 | 1125 | 0.5346 | 0.65 | |
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| 0.6054 | 46.0 | 1150 | 0.5354 | 0.65 | |
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| 0.6054 | 47.0 | 1175 | 0.5338 | 0.64 | |
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| 0.6054 | 48.0 | 1200 | 0.5332 | 0.65 | |
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| 0.6054 | 49.0 | 1225 | 0.5334 | 0.65 | |
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| 0.6054 | 50.0 | 1250 | 0.5361 | 0.65 | |
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| 0.6054 | 51.0 | 1275 | 0.5311 | 0.65 | |
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| 0.6054 | 52.0 | 1300 | 0.5332 | 0.66 | |
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| 0.6054 | 53.0 | 1325 | 0.5312 | 0.65 | |
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| 0.6054 | 54.0 | 1350 | 0.5334 | 0.65 | |
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| 0.6054 | 55.0 | 1375 | 0.5306 | 0.66 | |
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| 0.6054 | 56.0 | 1400 | 0.5326 | 0.65 | |
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| 0.6054 | 57.0 | 1425 | 0.5336 | 0.65 | |
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| 0.6054 | 58.0 | 1450 | 0.5361 | 0.65 | |
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| 0.6054 | 59.0 | 1475 | 0.5359 | 0.63 | |
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| 0.5996 | 60.0 | 1500 | 0.5342 | 0.65 | |
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| 0.5996 | 61.0 | 1525 | 0.5346 | 0.66 | |
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| 0.5996 | 62.0 | 1550 | 0.5333 | 0.64 | |
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| 0.5996 | 63.0 | 1575 | 0.5322 | 0.65 | |
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| 0.5996 | 64.0 | 1600 | 0.5307 | 0.65 | |
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| 0.5996 | 65.0 | 1625 | 0.5298 | 0.65 | |
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| 0.5996 | 66.0 | 1650 | 0.5300 | 0.65 | |
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| 0.5996 | 67.0 | 1675 | 0.5306 | 0.65 | |
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| 0.5996 | 68.0 | 1700 | 0.5311 | 0.65 | |
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| 0.5996 | 69.0 | 1725 | 0.5318 | 0.65 | |
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| 0.5996 | 70.0 | 1750 | 0.5320 | 0.65 | |
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| 0.5996 | 71.0 | 1775 | 0.5320 | 0.65 | |
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| 0.5996 | 72.0 | 1800 | 0.5309 | 0.65 | |
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| 0.5996 | 73.0 | 1825 | 0.5307 | 0.65 | |
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| 0.5996 | 74.0 | 1850 | 0.5306 | 0.65 | |
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| 0.5996 | 75.0 | 1875 | 0.5314 | 0.65 | |
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| 0.5996 | 76.0 | 1900 | 0.5311 | 0.65 | |
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| 0.5996 | 77.0 | 1925 | 0.5311 | 0.65 | |
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| 0.5996 | 78.0 | 1950 | 0.5311 | 0.65 | |
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| 0.5996 | 79.0 | 1975 | 0.5311 | 0.65 | |
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| 0.596 | 80.0 | 2000 | 0.5311 | 0.65 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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