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--- |
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tags: |
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- generated_from_trainer |
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widget: |
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- text: "Sthewillswes emy hedrpi cepl ritie" |
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- text: "orel nol hammug antees sopa raus" |
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- text: "Gan nstho lanuat tharestlint erks" |
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- text: "Jel chatr thefl harewh wh's" |
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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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# fake-gpt-2-17m |
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This model is a GPTJ (with 17m parameters) trained from scratch on a synthetic dataset (1gb of documents created in 4 fake languages, each with a formal and informal writing style) for 1 epoch. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.5592 |
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## Intended uses & limitations |
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This model is to be used as a base model for fine-tuning any language/task to probe the effectiveness of both pre-training on an algorithmically generated corpus and effectiveness of extremely small language models (SLMs?). It can only generate text based on its training data (which will be uploaded as a huggingface dataset soon). |
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## Training and evaluation data |
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More information needed |
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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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- batch_size 64 |
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- seed: 42 |
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- optimizer: Adam |
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- lr_scheduler_type: linear |
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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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| 3.5175 | 1.0 | 46857 | 3.5592 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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