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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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- code |
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model-index: |
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- name: codeparrot-ds |
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results: [] |
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datasets: |
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- huggingface-course/codeparrot-ds-valid |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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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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# GPT2-Codeparrot |
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Generative Pre-trained Transformer 2 (GPT-2) is a large language model from OpenAI that was first introduced in [gpt2](https://huggingface.co/gpt2). It is a decoder-only Transformer model trained using a masked language modeling (MLM) objective. This means the model is trained to predict the next word in a sequence, given the previous words. GPT-2 models are known for their ability to generate realistic and coherent text, making them useful for a variety of natural language processing tasks such as text generation, translation, and question answering. |
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## Model description |
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This model is a base GPT-2 architecture with [insert number] parameters. It was trained on the huggingface-course/codeparrot-ds-valid dataset, which is a small subset of the original WebText dataset used to train GPT-2. Due to the limited training data, this model may not perform as well as other pre-trained GPT-2 models available on Hugging Face. |
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## Intended uses & limitations |
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This model is intended for personal learning and exploration of the GPT-2 architecture. Due to its limited training data, it may not be suitable for real-world applications. |
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## Training and evaluation data |
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This model was trained using the Transformers library with the following specifications: |
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- Training Data: `huggingface-course/codeparrot-ds-valid` |
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- Training Script: [Training_a_causal_language_model_from_scratch](https://github.com/kailas711/HugginFace-NLP-Course/blob/af464abed3f79fe7434f3310ceb97bfb68cddcef/Training_a_causal_language_model_from_scratch.ipynb) |
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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: 1000 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
