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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: GPT-2_para3M |
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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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# GPT-2_para3M |
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3207 |
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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: 0.0005 |
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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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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 9.6976 | 0.01 | 100 | 7.7754 | |
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| 6.488 | 0.02 | 200 | 5.7795 | |
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| 5.3705 | 0.03 | 300 | 4.8609 | |
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| 4.5632 | 0.04 | 400 | 4.2544 | |
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| 4.141 | 0.05 | 500 | 3.9425 | |
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| 3.902 | 0.06 | 600 | 3.7189 | |
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| 3.7074 | 0.07 | 700 | 3.5514 | |
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| 3.5716 | 0.08 | 800 | 3.4291 | |
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| 3.4695 | 0.08 | 900 | 3.3253 | |
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| 3.3847 | 0.09 | 1000 | 3.2311 | |
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| 3.2974 | 0.1 | 1100 | 3.1595 | |
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| 3.2318 | 0.11 | 1200 | 3.0909 | |
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| 3.1698 | 0.12 | 1300 | 3.0329 | |
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| 3.1258 | 0.13 | 1400 | 2.9879 | |
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| 3.0802 | 0.14 | 1500 | 2.9396 | |
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| 3.046 | 0.15 | 1600 | 2.9017 | |
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| 3.0047 | 0.16 | 1700 | 2.8652 | |
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| 2.9701 | 0.17 | 1800 | 2.8320 | |
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| 2.9425 | 0.18 | 1900 | 2.8048 | |
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| 2.9141 | 0.19 | 2000 | 2.7757 | |
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| 2.8896 | 0.2 | 2100 | 2.7515 | |
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| 2.8667 | 0.21 | 2200 | 2.7263 | |
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| 2.8443 | 0.22 | 2300 | 2.7066 | |
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| 2.8288 | 0.23 | 2400 | 2.6815 | |
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| 2.8044 | 0.24 | 2500 | 2.6620 | |
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| 2.7886 | 0.25 | 2600 | 2.6471 | |
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| 2.7732 | 0.25 | 2700 | 2.6283 | |
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| 2.7576 | 0.26 | 2800 | 2.6101 | |
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| 2.7479 | 0.27 | 2900 | 2.5978 | |
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| 2.7256 | 0.28 | 3000 | 2.5819 | |
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| 2.7179 | 0.29 | 3100 | 2.5688 | |
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| 2.707 | 0.3 | 3200 | 2.5595 | |
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| 2.6921 | 0.31 | 3300 | 2.5471 | |
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| 2.6809 | 0.32 | 3400 | 2.5329 | |
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| 2.6779 | 0.33 | 3500 | 2.5232 | |
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| 2.663 | 0.34 | 3600 | 2.5154 | |
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| 2.6554 | 0.35 | 3700 | 2.5030 | |
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| 2.6437 | 0.36 | 3800 | 2.4967 | |
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| 2.6346 | 0.37 | 3900 | 2.4859 | |
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| 2.6293 | 0.38 | 4000 | 2.4768 | |
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| 2.6221 | 0.39 | 4100 | 2.4709 | |
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| 2.6178 | 0.4 | 4200 | 2.4623 | |
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| 2.6076 | 0.41 | 4300 | 2.4586 | |
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| 2.6025 | 0.41 | 4400 | 2.4492 | |
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| 2.5907 | 0.42 | 4500 | 2.4409 | |
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| 2.5896 | 0.43 | 4600 | 2.4369 | |
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| 2.5816 | 0.44 | 4700 | 2.4316 | |
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| 2.5783 | 0.45 | 4800 | 2.4256 | |
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| 2.577 | 0.46 | 4900 | 2.4204 | |
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| 2.5685 | 0.47 | 5000 | 2.4150 | |
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| 2.567 | 0.48 | 5100 | 2.4093 | |
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| 2.5564 | 0.49 | 5200 | 2.4059 | |
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| 2.5556 | 0.5 | 5300 | 2.4012 | |
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| 2.5496 | 0.51 | 5400 | 2.3997 | |
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| 2.545 | 0.52 | 5500 | 2.3956 | |
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| 2.5473 | 0.53 | 5600 | 2.3905 | |
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| 2.5389 | 0.54 | 5700 | 2.3856 | |
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| 2.5373 | 0.55 | 5800 | 2.3818 | |
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| 2.5318 | 0.56 | 5900 | 2.3787 | |
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| 2.5313 | 0.57 | 6000 | 2.3751 | |
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| 2.5285 | 0.58 | 6100 | 2.3722 | |
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| 2.5318 | 0.58 | 6200 | 2.3687 | |
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| 2.5229 | 0.59 | 6300 | 2.3666 | |
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| 2.5194 | 0.6 | 6400 | 2.3632 | |
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| 2.5174 | 0.61 | 6500 | 2.3598 | |
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| 2.5169 | 0.62 | 6600 | 2.3567 | |
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| 2.511 | 0.63 | 6700 | 2.3552 | |
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| 2.5093 | 0.64 | 6800 | 2.3546 | |
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| 2.5114 | 0.65 | 6900 | 2.3528 | |
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| 2.5064 | 0.66 | 7000 | 2.3492 | |
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| 2.507 | 0.67 | 7100 | 2.3483 | |
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| 2.502 | 0.68 | 7200 | 2.3445 | |
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| 2.4964 | 0.69 | 7300 | 2.3448 | |
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| 2.4999 | 0.7 | 7400 | 2.3423 | |
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| 2.4961 | 0.71 | 7500 | 2.3407 | |
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| 2.489 | 0.72 | 7600 | 2.3386 | |
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| 2.4926 | 0.73 | 7700 | 2.3384 | |
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| 2.4919 | 0.74 | 7800 | 2.3365 | |
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| 2.491 | 0.74 | 7900 | 2.3349 | |
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| 2.4893 | 0.75 | 8000 | 2.3333 | |
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| 2.4909 | 0.76 | 8100 | 2.3318 | |
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| 2.4862 | 0.77 | 8200 | 2.3305 | |
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| 2.4884 | 0.78 | 8300 | 2.3299 | |
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| 2.49 | 0.79 | 8400 | 2.3280 | |
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| 2.4788 | 0.8 | 8500 | 2.3286 | |
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| 2.4865 | 0.81 | 8600 | 2.3272 | |
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| 2.4823 | 0.82 | 8700 | 2.3263 | |
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| 2.4844 | 0.83 | 8800 | 2.3255 | |
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| 2.4826 | 0.84 | 8900 | 2.3251 | |
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| 2.4844 | 0.85 | 9000 | 2.3243 | |
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| 2.4798 | 0.86 | 9100 | 2.3231 | |
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| 2.4864 | 0.87 | 9200 | 2.3231 | |
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| 2.4755 | 0.88 | 9300 | 2.3228 | |
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| 2.4735 | 0.89 | 9400 | 2.3228 | |
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| 2.4786 | 0.9 | 9500 | 2.3224 | |
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| 2.4791 | 0.91 | 9600 | 2.3222 | |
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| 2.4809 | 0.91 | 9700 | 2.3214 | |
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| 2.4778 | 0.92 | 9800 | 2.3213 | |
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| 2.4777 | 0.93 | 9900 | 2.3211 | |
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| 2.4798 | 0.94 | 10000 | 2.3209 | |
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| 2.4768 | 0.95 | 10100 | 2.3212 | |
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| 2.4808 | 0.96 | 10200 | 2.3209 | |
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| 2.4762 | 0.97 | 10300 | 2.3208 | |
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| 2.4778 | 0.98 | 10400 | 2.3208 | |
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| 2.4816 | 0.99 | 10500 | 2.3207 | |
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| 2.4728 | 1.0 | 10600 | 2.3207 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.2 |
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