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--- |
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language: |
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- en |
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tags: |
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- text-generation |
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- gpt2 |
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- gpt |
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license: mit |
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widget: |
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- text: |+ |
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Do you like my new haircut? |
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person beta: |
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example_title: haircut |
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- text: |+ |
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I love to learn new things.. are you willing to teach me something? |
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person beta: |
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example_title: teaching |
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- text: |+ |
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What's your favorite animal? Mine is the dog? |
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person beta: |
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example_title: favorite |
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- text: |+ |
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how much does it cost? |
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person beta: |
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example_title: money |
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inference: |
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parameters: |
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min_length: 2 |
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max_length: 64 |
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length_penalty: 0.6 |
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no_repeat_ngram_size: 3 |
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do_sample: true |
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top_p: 0.85 |
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top_k: 10 |
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repetition_penalty: 2.1 |
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pipeline_tag: text-generation |
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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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# pszemraj/gpt2-medium-vaguely-human-dialogue |
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This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on a parsed version of Wizard of Wikipedia. Because the batch size was so large, it learned a general understanding of words that makes sense together but does not specifically respond to anything - sort of like an alien learning to imitate human words to convince others that it is human. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.3281 |
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## Model description |
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- a decent example of what happens when your batch size is too large and the global optima does not reflect specific prompts / use cases. |
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## Intended uses & limitations |
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- there are no intended uses |
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## Training and evaluation data |
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- a parsed version of the wizard of Wikipedia dataset |
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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: 2e-05 |
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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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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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_ratio: 0.05 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 34.991 | 1.0 | 837 | 14.8359 | |
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| 12.2881 | 2.0 | 1674 | 9.375 | |
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| 8.5071 | 3.0 | 2511 | 7.2148 | |
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| 7.6031 | 4.0 | 3348 | 6.1758 | |
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| 6.4808 | 5.0 | 4185 | 5.5820 | |
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| 5.8562 | 6.0 | 5022 | 5.0977 | |
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| 5.6094 | 7.0 | 5859 | 4.8203 | |
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| 5.2591 | 8.0 | 6696 | 4.5977 | |
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| 5.0031 | 9.0 | 7533 | 4.4219 | |
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| 4.8837 | 10.0 | 8370 | 4.3281 | |
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### Framework versions |
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- Transformers 4.16.1 |
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- Pytorch 1.10.0+cu111 |
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- Tokenizers 0.11.0 |
