AWS Lambda Layer for KeyBERT Extraction

Project: Create Lightweight AWS Lambda Layer for KeyBERT & Keyphrase Extraction Overview: We need a lightweight AWS Lambda Layer that packages KeyBERT and keyphrase-vectorizers for extracting keyphrases from text. The layer should be optimized for size, deployment speed, and Lambda compatibility (Python 3.9+ runtime). Requirements: 1. Dependencies: • keybert • keyphrase-vectorizers • Any minimal additional dependencies required (scikit-learn, numpy, torch, etc.). • Exclude unnecessary files to keep the layer lightweight (avoid heavy ML libraries not used). 2. Lambda Layer: • Build a deployable .zip file for AWS Lambda layer. • Ensure the size stays under AWS Lambda’s 250MB unzipped limit. • Should support Python 3.9 and 3.10 runtimes. • Provide instructions for publishing and attaching the layer. 3. Testing Code: Provide a sample Lambda function that imports the layer and runs the following function: from keybert import KeyBERT from keyphrase_vectorizers import KeyphraseCountVectorizer kw_model = KeyBERT() def patternrank_keywords(text: str): keywords = kw_model.extract_keywords(text, vectorizer=KeyphraseCountVectorizer(), top_n=15) return [words[0].replace("-", " ") for words in keywords] Example input: title + abstract text Example output: list of 15 keyphrases. 4. Deliverables: • Lambda Layer .zip file. • Deployment instructions (Terraform/CLI preferred but not mandatory). • Sample Lambda function for verification. Bonus (Optional): • Optimize dependencies to reduce cold start times. • Provide Dockerfile (for future re-builds). Skills Required: • AWS Lambda & Layers • Python packaging • Experience with ML/NLP libraries (KeyBERT, scikit-learn) ⸻ Goal: A ready-to-use Lambda Layer that allows us to run the provided keyphrase extraction code inside Lambda without errors.

Project ID: 39749887