README.md

---
library_name: transformers
tags:
- gpt2
- tokenizer
- code
- python
- code-search-net
datasets:
- code_search_net
base_model: gpt2
---

# Model Card for farid678/gpt2-python-tokenizer

<!-- Provide a quick summary of what the model is/does. -->

A new byte-level BPE tokenizer trained from scratch for GPT-2, specialized for Python source code, using the `code_search_net` (Python subset) dataset.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This repository contains a custom tokenizer trained from the base GPT-2 tokenizer architecture, re-trained on the Python portion of the `code_search_net` dataset. The goal of this tokenizer is to better capture Python-specific syntax, keywords, identifiers, and code patterns (e.g. indentation, operators, common function/variable naming conventions) compared to the original GPT-2 tokenizer, which was trained primarily on natural language web text.

This tokenizer can be paired with a GPT-2 model (either the original pretrained weights with an extended/adapted embedding layer, or a model trained from scratch) for downstream tasks involving Python code, such as code completion, code summarization, or code generation.

- **Developed by:** [farid678](https://huggingface.co/farid678)
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** farid678
- **Model type:** Byte-level BPE tokenizer (GPT-2 architecture)
- **Language(s) (NLP):** Python (programming language); tokenizer vocabulary derived from source code rather than natural language
- **License:** [More Information Needed]
- **Finetuned from model:** `gpt2` (tokenizer re-trained from scratch on new data, using GPT-2's tokenizer architecture as the base)

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/farid678
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

This tokenizer can be used directly to tokenize Python source code for input into a GPT-2-style language model. It is intended for use in code-related NLP pipelines such as tokenizing datasets before training/fine-tuning a language model on Python code.

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

Intended to be paired with a GPT-2 (or GPT-2-style) causal language model for tasks such as:
- Python code completion
- Python code generation
- Code summarization / docstring generation
- Code-to-text or text-to-code tasks

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

This tokenizer is optimized for Python code and is not expected to perform well on natural language text or other programming languages (e.g. Java, C++, JavaScript) since its vocabulary was derived specifically from Python source code in `code_search_net`. It should not be used as a general-purpose natural language tokenizer.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

- The tokenizer's vocabulary reflects patterns present in the `code_search_net` Python subset, which is sourced from public open-source GitHub repositories. As such, it may inherit biases present in that codebase (e.g. naming conventions, coding styles, or underrepresentation of certain coding domains).
- Performance on code written in significantly different styles, older Python versions, or non-English identifiers/comments may be degraded.
- This tokenizer alone does not generate code; it must be paired with a trained language model to be useful for downstream tasks.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the tokenizer. It is recommended to evaluate tokenization quality (e.g. compression rate, out-of-vocabulary handling) on your own target dataset before relying on it for production use.

## How to Get Started with the Model

Use the code below to get started with the tokenizer.

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("farid678/gpt2-python-tokenizer")

code_sample = "def hello_world():\n    print('Hello, world!')"
tokens = tokenizer.tokenize(code_sample)
print(tokens)

encoded = tokenizer(code_sample)
print(encoded["input_ids"])
```

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

The tokenizer was trained on the **Python subset** of the [`code_search_net`](https://huggingface.co/datasets/code_search_net) dataset:

```python
from datasets import load_dataset

raw_dataset = load_dataset("code_search_net", "python")
```

`code_search_net` contains functions and methods collected from open-source GitHub repositories, along with their associated docstrings/comments. The Python configuration used here consists of Python source code specifically.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

A new byte-level BPE tokenizer was trained from scratch using the GPT-2 tokenizer architecture as a template (i.e. `tokenizer.train_new_from_iterator` from the 🤗 Tokenizers/Transformers library), using the raw code text from `code_search_net` (Python) as the training corpus.

#### Preprocessing [optional]

Python code and associated documentation strings from `code_search_net` were used as raw text input for tokenizer training. [More Information Needed] (exact preprocessing steps, e.g. whether docstrings/comments were included or code-only)

#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
- **Vocabulary size:** [More Information Needed]
- **Base tokenizer:** `gpt2` (byte-level BPE)

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed] (e.g. average tokens per line of code, compression ratio vs. original GPT-2 tokenizer, out-of-vocabulary rate)

### Results

[More Information Needed]

#### Summary

This tokenizer is expected to produce more efficient, code-aware tokenization for Python source code compared to the original GPT-2 tokenizer, though formal benchmark results have not yet been recorded.

## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

Byte-level BPE tokenizer following the GPT-2 tokenizer architecture, retrained on a new corpus (Python code from `code_search_net`) rather than the original GPT-2 training data.

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

- 🤗 `transformers`
- 🤗 `datasets`
- 🤗 `tokenizers`

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
@misc{husain2019codesearchnet,
  title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
  author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
  year={2019},
  eprint={1909.09436},
  archivePrefix={arXiv}
}
```

**APA:**

Husain, H., Wu, H.-H., Gazit, T., Allamanis, M., & Brockschmidt, M. (2019). CodeSearchNet Challenge: Evaluating the State of Semantic Code Search. arXiv preprint arXiv:1909.09436.

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

- **BPE (Byte-Pair Encoding):** A subword tokenization algorithm that iteratively merges the most frequent pairs of bytes/characters to build a vocabulary.
- **`train_new_from_iterator`:** A 🤗 Transformers method that allows retraining an existing tokenizer's vocabulary on a new corpus while keeping the same tokenization algorithm/architecture.

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

farid678

## Model Card Contact

https://huggingface.co/farid678