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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: '20230826105641' |
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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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# 20230826105641 |
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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: 0.6024 |
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- Accuracy: 0.64 |
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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 | 0.6078 | 0.65 | |
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| No log | 2.0 | 50 | 0.5963 | 0.66 | |
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| No log | 3.0 | 75 | 0.6125 | 0.65 | |
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| No log | 4.0 | 100 | 0.6042 | 0.66 | |
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| No log | 5.0 | 125 | 0.6065 | 0.66 | |
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| No log | 6.0 | 150 | 0.6020 | 0.65 | |
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| No log | 7.0 | 175 | 0.5987 | 0.65 | |
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| No log | 8.0 | 200 | 0.6016 | 0.66 | |
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| No log | 9.0 | 225 | 0.6066 | 0.66 | |
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| No log | 10.0 | 250 | 0.6112 | 0.66 | |
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| No log | 11.0 | 275 | 0.6085 | 0.66 | |
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| No log | 12.0 | 300 | 0.5976 | 0.66 | |
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| No log | 13.0 | 325 | 0.6074 | 0.66 | |
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| No log | 14.0 | 350 | 0.6060 | 0.65 | |
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| No log | 15.0 | 375 | 0.6254 | 0.65 | |
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| No log | 16.0 | 400 | 0.6031 | 0.66 | |
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| No log | 17.0 | 425 | 0.6011 | 0.67 | |
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| No log | 18.0 | 450 | 0.6063 | 0.66 | |
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| No log | 19.0 | 475 | 0.6031 | 0.65 | |
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| 0.6484 | 20.0 | 500 | 0.6013 | 0.65 | |
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| 0.6484 | 21.0 | 525 | 0.6041 | 0.65 | |
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| 0.6484 | 22.0 | 550 | 0.6037 | 0.65 | |
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| 0.6484 | 23.0 | 575 | 0.6046 | 0.65 | |
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| 0.6484 | 24.0 | 600 | 0.6072 | 0.66 | |
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| 0.6484 | 25.0 | 625 | 0.5980 | 0.66 | |
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| 0.6484 | 26.0 | 650 | 0.6039 | 0.64 | |
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| 0.6484 | 27.0 | 675 | 0.6025 | 0.65 | |
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| 0.6484 | 28.0 | 700 | 0.6062 | 0.65 | |
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| 0.6484 | 29.0 | 725 | 0.6056 | 0.64 | |
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| 0.6484 | 30.0 | 750 | 0.6091 | 0.61 | |
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| 0.6484 | 31.0 | 775 | 0.6037 | 0.65 | |
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| 0.6484 | 32.0 | 800 | 0.6037 | 0.63 | |
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| 0.6484 | 33.0 | 825 | 0.6175 | 0.64 | |
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| 0.6484 | 34.0 | 850 | 0.6089 | 0.62 | |
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| 0.6484 | 35.0 | 875 | 0.6076 | 0.64 | |
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| 0.6484 | 36.0 | 900 | 0.6073 | 0.64 | |
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| 0.6484 | 37.0 | 925 | 0.6059 | 0.64 | |
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| 0.6484 | 38.0 | 950 | 0.6109 | 0.63 | |
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| 0.6484 | 39.0 | 975 | 0.6090 | 0.64 | |
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| 0.6362 | 40.0 | 1000 | 0.6080 | 0.64 | |
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| 0.6362 | 41.0 | 1025 | 0.5994 | 0.64 | |
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| 0.6362 | 42.0 | 1050 | 0.6034 | 0.64 | |
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| 0.6362 | 43.0 | 1075 | 0.6113 | 0.6 | |
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| 0.6362 | 44.0 | 1100 | 0.6131 | 0.64 | |
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| 0.6362 | 45.0 | 1125 | 0.6150 | 0.61 | |
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| 0.6362 | 46.0 | 1150 | 0.6115 | 0.63 | |
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| 0.6362 | 47.0 | 1175 | 0.6055 | 0.64 | |
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| 0.6362 | 48.0 | 1200 | 0.6033 | 0.64 | |
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| 0.6362 | 49.0 | 1225 | 0.6047 | 0.64 | |
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| 0.6362 | 50.0 | 1250 | 0.6037 | 0.64 | |
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| 0.6362 | 51.0 | 1275 | 0.6010 | 0.63 | |
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| 0.6362 | 52.0 | 1300 | 0.5988 | 0.64 | |
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| 0.6362 | 53.0 | 1325 | 0.5991 | 0.64 | |
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| 0.6362 | 54.0 | 1350 | 0.6019 | 0.64 | |
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| 0.6362 | 55.0 | 1375 | 0.6002 | 0.64 | |
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| 0.6362 | 56.0 | 1400 | 0.6006 | 0.64 | |
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| 0.6362 | 57.0 | 1425 | 0.5992 | 0.63 | |
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| 0.6362 | 58.0 | 1450 | 0.5992 | 0.63 | |
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| 0.6362 | 59.0 | 1475 | 0.5992 | 0.64 | |
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| 0.6341 | 60.0 | 1500 | 0.6026 | 0.64 | |
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| 0.6341 | 61.0 | 1525 | 0.6022 | 0.64 | |
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| 0.6341 | 62.0 | 1550 | 0.6026 | 0.64 | |
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| 0.6341 | 63.0 | 1575 | 0.6036 | 0.64 | |
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| 0.6341 | 64.0 | 1600 | 0.6039 | 0.64 | |
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| 0.6341 | 65.0 | 1625 | 0.6041 | 0.64 | |
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| 0.6341 | 66.0 | 1650 | 0.6034 | 0.64 | |
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| 0.6341 | 67.0 | 1675 | 0.6049 | 0.64 | |
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| 0.6341 | 68.0 | 1700 | 0.6027 | 0.64 | |
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| 0.6341 | 69.0 | 1725 | 0.6057 | 0.64 | |
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| 0.6341 | 70.0 | 1750 | 0.6056 | 0.64 | |
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| 0.6341 | 71.0 | 1775 | 0.6048 | 0.64 | |
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| 0.6341 | 72.0 | 1800 | 0.6019 | 0.64 | |
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| 0.6341 | 73.0 | 1825 | 0.6021 | 0.64 | |
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| 0.6341 | 74.0 | 1850 | 0.6018 | 0.64 | |
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| 0.6341 | 75.0 | 1875 | 0.6027 | 0.64 | |
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| 0.6341 | 76.0 | 1900 | 0.6025 | 0.64 | |
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| 0.6341 | 77.0 | 1925 | 0.6021 | 0.64 | |
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| 0.6341 | 78.0 | 1950 | 0.6023 | 0.64 | |
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| 0.6341 | 79.0 | 1975 | 0.6024 | 0.64 | |
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| 0.626 | 80.0 | 2000 | 0.6024 | 0.64 | |
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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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