
## Dataset 1: TheStack - Java - Cleaned

**Description**: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Java, a popular statically typed language.

**Target Language**: Java

**Dataset Size**:
- Training: 900,000 files
- Validation: 50,000 files
- Test: 50,000 files

**Preprocessing**:
1. Selected Java as the target language due to its popularity on GitHub.
2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%.
3. Split files into 90% training, 5% validation, and 5% test sets.

**Tokenizer**: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.

**Training Sequences**: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning).
task_categories:text-generation size_categories:1M<n<10M language:code license:openrail code region:us
{"language": ["code"], "license": "openrail", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "TheStack-Java", "dataset_info": {"features": [{"name": "hexsha", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "avg_line_length", "dtype": "float64"}, {"name": "max_line_length", "dtype": "int64"}, {"name": "alphanum_fraction", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 3582248477.9086223, "num_examples": 806789}, {"name": "test", "num_bytes": 394048264.9973618, "num_examples": 88747}, {"name": "valid", "num_bytes": 3982797.09401595, "num_examples": 897}], "download_size": 1323156008, "dataset_size": 3980279540}, "tags": ["code"]}
TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-code #license-openrail #code #region-us 


## Dataset 1: TheStack - Java - Cleaned

Description: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Java, a popular statically typed language.

Target Language: Java

Dataset Size:
- Training: 900,000 files
- Validation: 50,000 files
- Test: 50,000 files

Preprocessing:
1. Selected Java as the target language due to its popularity on GitHub.
2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%.
3. Split files into 90% training, 5% validation, and 5% test sets.

Tokenizer: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.

Training Sequences: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning).
## Dataset 1: TheStack - Java - Cleaned

Description: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Java, a popular statically typed language.

Target Language: Java

Dataset Size:
- Training: 900,000 files
- Validation: 50,000 files
- Test: 50,000 files

Preprocessing:
1. Selected Java as the target language due to its popularity on GitHub.
2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%.
3. Split files into 90% training, 5% validation, and 5% test sets.

Tokenizer: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.

Training Sequences: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning).
TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-code #license-openrail #code #region-us 
 ## Dataset 1: TheStack - Java - Cleaned

Description: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Java, a popular statically typed language.

Target Language: Java

Dataset Size:
- Training: 900,000 files
- Validation: 50,000 files
- Test: 50,000 files

Preprocessing:
1. Selected Java as the target language due to its popularity on GitHub.
2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%.
3. Split files into 90% training, 5% validation, and 5% test sets.

Tokenizer: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.

Training Sequences: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning).
passage: TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-code #license-openrail #code #region-us 
## Dataset 1: TheStack - Java - Cleaned

Description: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Java, a popular statically typed language.

Target Language: Java

Dataset Size:
- Training: 900,000 files
- Validation: 50,000 files
- Test: 50,000 files

Preprocessing:
1. Selected Java as the target language due to its popularity on GitHub.
2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%.
3. Split files into 90% training, 5% validation, and 5% test sets.

Tokenizer: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.

Training Sequences: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning).