Update README.md

README.md

@@ -1,13 +1,21 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- gpt2
+- tokenizer
+- code
+- python
+- code-search-net
+datasets:
+- code_search_net
+base_model: gpt2
 ---
 
-# Model Card for Model ID
+# 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
 
@@ -15,21 +23,23 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+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:** [More Information Needed]
+- **Developed by:** [farid678](https://huggingface.co/farid678)
 - **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [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 [optional]:** [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:** [More Information Needed]
+- **Repository:** https://huggingface.co/farid678
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
@@ -41,37 +51,54 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+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 -->
 
-[More Information Needed]
+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. -->
 
-[More Information Needed]
+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. -->
 
-[More Information Needed]
+- 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 model. More information needed for further recommendations.
+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 model.
+Use the code below to get started with the tokenizer.
 
-[More Information Needed]
+```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
 
@@ -79,20 +106,31 @@ Use the code below to get started with the model.
 
 <!-- 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. -->
 
-[More Information Needed]
+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. -->
 
-#### Preprocessing [optional]
+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.
 
-[More Information Needed]
+#### 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]
 
@@ -122,7 +160,7 @@ Use the code below to get started with the model.
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+[More Information Needed] (e.g. average tokens per line of code, compression ratio vs. original GPT-2 tokenizer, out-of-vocabulary rate)
 
 ### Results
 
@@ -130,7 +168,7 @@ Use the code below to get started with the model.
 
 #### 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]
 
@@ -154,7 +192,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+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
 
@@ -166,7 +204,9 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Software
 
-[More Information Needed]
+- 🤗 `transformers`
+- 🤗 `datasets`
+- 🤗 `tokenizers`
 
 ## Citation [optional]
 
@@ -174,17 +214,26 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-[More Information Needed]
+```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:**
 
-[More Information Needed]
+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. -->
 
-[More Information Needed]
+- **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]
 
@@ -192,8 +241,8 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+farid678
 
 ## Model Card Contact
 
-[More Information Needed]
+https://huggingface.co/farid678