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--- |
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license: mit |
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base_model: gpt2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: gpt2-10var |
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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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# gpt2-10var |
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1102 |
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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: 5e-05 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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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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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| No log | 0.04 | 200 | 0.2493 | |
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| No log | 0.08 | 400 | 0.3971 | |
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| 0.4919 | 0.12 | 600 | 0.6197 | |
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| 0.4919 | 0.16 | 800 | 0.5482 | |
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| 0.9307 | 0.2 | 1000 | 0.8619 | |
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| 0.9307 | 0.24 | 1200 | 0.5619 | |
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| 0.9307 | 0.28 | 1400 | 0.7757 | |
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| 1.6552 | 0.32 | 1600 | 0.5050 | |
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| 1.6552 | 0.36 | 1800 | 1.1518 | |
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| 1.1387 | 0.4 | 2000 | 1.0939 | |
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| 1.1387 | 0.44 | 2200 | 9.2829 | |
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| 1.1387 | 0.48 | 2400 | 0.2714 | |
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| 8.5966 | 0.52 | 2600 | 0.1263 | |
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| 8.5966 | 0.56 | 2800 | 0.1191 | |
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| 0.1233 | 0.6 | 3000 | 0.1161 | |
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| 0.1233 | 0.64 | 3200 | 0.1150 | |
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| 0.1233 | 0.67 | 3400 | 0.1145 | |
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| 0.1166 | 0.71 | 3600 | 0.1138 | |
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| 0.1166 | 0.75 | 3800 | 0.1135 | |
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| 0.1151 | 0.79 | 4000 | 0.1132 | |
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| 0.1151 | 0.83 | 4200 | 0.1130 | |
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| 0.1151 | 0.87 | 4400 | 0.1125 | |
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| 0.1131 | 0.91 | 4600 | 0.1122 | |
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| 0.1131 | 0.95 | 4800 | 0.1119 | |
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| 0.1132 | 0.99 | 5000 | 0.1116 | |
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| 0.1132 | 1.03 | 5200 | 0.1115 | |
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| 0.1132 | 1.07 | 5400 | 0.1115 | |
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| 0.1123 | 1.11 | 5600 | 0.1112 | |
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| 0.1123 | 1.15 | 5800 | 0.1111 | |
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| 0.1116 | 1.19 | 6000 | 0.1110 | |
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| 0.1116 | 1.23 | 6200 | 0.1110 | |
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| 0.1116 | 1.27 | 6400 | 0.1108 | |
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| 0.1132 | 1.31 | 6600 | 0.1107 | |
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| 0.1132 | 1.35 | 6800 | 0.1122 | |
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| 0.2039 | 1.39 | 7000 | 0.1110 | |
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| 0.2039 | 1.43 | 7200 | 0.1108 | |
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| 0.2039 | 1.47 | 7400 | 0.1106 | |
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| 0.1107 | 1.51 | 7600 | 0.1106 | |
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| 0.1107 | 1.55 | 7800 | 0.1105 | |
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| 0.1115 | 1.59 | 8000 | 0.1104 | |
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| 0.1115 | 1.63 | 8200 | 0.1104 | |
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| 0.1115 | 1.67 | 8400 | 0.1104 | |
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| 0.1106 | 1.71 | 8600 | 0.1104 | |
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| 0.1106 | 1.75 | 8800 | 0.1103 | |
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| 0.1092 | 1.79 | 9000 | 0.1103 | |
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| 0.1092 | 1.83 | 9200 | 0.1103 | |
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| 0.1092 | 1.87 | 9400 | 0.1102 | |
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| 0.111 | 1.91 | 9600 | 0.1102 | |
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| 0.111 | 1.94 | 9800 | 0.1102 | |
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| 0.1109 | 1.98 | 10000 | 0.1102 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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