---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
- code
model-index:
- name: codeparrot-ds
  results: []
datasets:
- huggingface-course/codeparrot-ds-valid
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-generation
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# GPT2-Codeparrot

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.

## Model description

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.
## Intended uses & limitations

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.
## Training and evaluation data

This model was trained using the Transformers library with the following specifications:

- Training Data: `huggingface-course/codeparrot-ds-valid`
- 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)
- 

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1