Update README.md

README.md

@@ -1,199 +1,82 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- tokenizer
+- gpt-2
+license: apache-2.0
+datasets:
+- code-search-net/code_search_net
+base_model:
+- openai-community/gpt2
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Code-Net Tokenizer Trained on GPT-2
 
+This model card describes a custom tokenizer trained on the existing GPT-2 tokenizer using the CodeSearchNet dataset. The tokenizer was adapted to better handle code-specific tokenization, leveraging the large scale and fine-grained vocabulary of the GPT-2 model.
 
 ## Model Details
 
 ### Model Description
 
-<!-- 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 tokenizer was fine-tuned on the CodeSearchNet dataset, which contains millions of code snippets in multiple programming languages. The tokenizer was initialized with the GPT-2 tokenizer and then adapted to better handle the unique characteristics of programming language syntax and semantics.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** [Your Name or Organization]
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Model type:** Tokenizer
+- **Language(s) (NLP):** Python, Java, JavaScript, Go, Ruby, etc.
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** openai-community/gpt2
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [More Information Needed]
 - **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. -->
-
-[More Information Needed]
+The tokenizer can be used directly in any NLP tasks that involve source code, such as code generation, code summarization, or bug detection, by replacing the original GPT-2 tokenizer with this newly trained version.
 
 ### 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]
+When plugged into a code-generation or code-understanding pipeline, this tokenizer can help improve the model’s understanding of programming languages and code structure.
 
 ### 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 specifically designed for tokenizing programming code. It is not suited for general text-based NLP tasks like natural language processing, sentiment analysis, or text generation outside the context of source code.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This model may introduce bias based on the dataset it was trained on. For example, the tokenizer might have difficulty with edge cases or rare programming language constructs that were underrepresented in the training data.
 
 ### 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 should be aware of potential limitations when applying this tokenizer to specific, less-common programming languages. Additionally, it may not handle malformed code or highly unconventional syntaxes well.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+You can use the tokenizer by loading it via the Hugging Face `transformers` library:
 
-## Training Details
+```python
+from transformers import GPT2Tokenizer
 
-### Training Data
+# Load the custom tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("your-model-name")
 
-<!-- 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. -->
+# Tokenize a code snippet
+code_snippet = "def hello_world(): print('Hello, world!')"
+tokens = tokenizer.encode(code_snippet)
+print(tokens)
+```
+### Training Details
+Training Data
+The tokenizer was trained using the CodeSearchNet dataset, which contains millions of code snippets from various programming languages. This dataset is diverse in terms of programming languages and code style, helping to create a more versatile tokenizer.
 
-[More Information Needed]
+Dataset: CodeSearchNet Dataset
+Languages Covered: Python
 
 ### 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]
-
-[More Information Needed]
-
-
-#### 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 -->
-
-#### 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]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## 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
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+The tokenizer was trained using the original GPT-2 tokenizer as a base and fine-tuned on the CodeSearchNet dataset. This involved segmenting code into subword units to ensure that tokenization respects common syntax and identifiers in code.