YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
NL2Py v2: English-to-Python Neural Code Translator
Parent GitHub Repository: https://github.com/abdullah00ashraf/abdullah00ashraf
Ecosystem Documentation: Axiyon / Producttolaunch Catalog
Part of the Axiyon Research Ecosystem.
Author: Abdullah Ashraf (@abdullahashraf122)
GitHub Profile: https://github.com/abdullah00ashraf
Model Overview
NL2Py v2 is a specialized seq2seq transformer fine-tuned on the iamthedan/code-instructions-25k-python dataset with structural prompt prefixes (translate English to Python: ). It generates syntax-valid Python code snippets directly from natural language specifications.
Architecture Highlights
- Base Architecture: T5-small (60M parameters)
- Tokenizer: SentencePiece with custom prefix masking
- Max Length: 128 input tokens $\to$ 512 target tokens
- Loss Function: Cross-Entropy with label smoothing
Interactive Demo
Run the included app.py or execute the training pipeline via NL2Py_v2_Colab_Pipeline.ipynb.