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--- |
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license: apache-2.0 |
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base_model: distilgpt2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: distilgpt2-finetuned-stories |
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results: [] |
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language: |
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- en |
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metrics: |
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- perplexity |
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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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# distilgpt2-finetuned-stories |
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the [demelin/understanding_fables](https://huggingface.co/datasets/demelin/understanding_fables) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.3089 |
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## Autoregressive and Prefix Language Modelling |
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Language Modelling, especially text generation works on the principle of generating the next token based on its previous antecedents. |
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This is what Autoregressive modelling are based on, it predicts the next token i.e. word here on the basis of token preceding it. Here, we take P(wi|wi-1), where wi is next word and wi-1 is token preceeding it, and P is the probbaility pf generating wi wrt wi-1 |
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But for Prefix Language modelling, we consider input into function and consider it in generation of our next word, i.e. the input is used as a context for generation of next tokens, calculating the conditional probability of next work wrt context. P(w|x), where w is next token and x is context and P is probability of getting w wrt x context. |
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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: 8 |
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- eval_batch_size: 8 |
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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: 3.0 |
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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 | 1.0 | 20 | 3.4065 | |
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| No log | 2.0 | 40 | 3.3288 | |
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| No log | 3.0 | 60 | 3.3089 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
