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--- |
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license: mit |
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base_model: gpt2-large |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: GPTL-APPS |
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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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# GPTL-APPS |
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This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7539 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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- training_steps: 5000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.0941 | 0.04 | 200 | 0.9988 | |
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| 0.8014 | 0.08 | 400 | 0.9315 | |
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| 0.8452 | 0.12 | 600 | 0.8909 | |
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| 0.9507 | 0.16 | 800 | 0.8903 | |
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| 0.6988 | 0.2 | 1000 | 0.8632 | |
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| 0.6965 | 0.24 | 1200 | 0.8553 | |
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| 0.7256 | 0.28 | 1400 | 0.8222 | |
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| 0.7109 | 0.32 | 1600 | 0.8162 | |
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| 0.6418 | 0.36 | 1800 | 0.8086 | |
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| 0.649 | 0.4 | 2000 | 0.8051 | |
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| 0.7378 | 0.44 | 2200 | 0.7974 | |
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| 0.7202 | 0.48 | 2400 | 0.7933 | |
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| 0.6896 | 0.52 | 2600 | 0.7817 | |
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| 0.5561 | 0.56 | 2800 | 0.7945 | |
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| 0.6497 | 0.6 | 3000 | 0.7774 | |
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| 0.735 | 0.64 | 3200 | 0.7758 | |
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| 0.5507 | 0.68 | 3400 | 0.7741 | |
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| 0.5615 | 0.72 | 3600 | 0.7677 | |
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| 0.6098 | 0.76 | 3800 | 0.7605 | |
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| 0.6038 | 0.8 | 4000 | 0.7653 | |
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| 0.5356 | 0.84 | 4200 | 0.7562 | |
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| 0.5699 | 0.88 | 4400 | 0.7586 | |
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| 0.6348 | 0.92 | 4600 | 0.7547 | |
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| 0.6458 | 0.96 | 4800 | 0.7539 | |
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| 0.6236 | 1.0 | 5000 | 0.7539 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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