attention-visualiser

v2.0.3 safe
3.0
Low Risk

a module to visualise attention layer activations from transformer based models from huggingface

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks with no network or shell execution detected, and low likelihood of obfuscation or credential harvesting. However, it shows signs of lower maintenance efforts indicated by missing metadata.

  • No network or shell execution detected
  • Low risk of obfuscation or credential harvesting
  • Lacks author details and PyPI classifiers
Per-check LLM notes
  • Network: No network calls detected, which is normal for a visualisation tool unless it requires external data sources.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands that could pose a risk.
  • Obfuscation: The observed patterns appear to be related to standard operations for loading and evaluating a pre-trained model using Hugging Face's transformers library, not typical obfuscation.
  • Credentials: No suspicious patterns indicative of credential harvesting were found.
  • Metadata: Low risk, but lacks author details and PyPI classifiers indicating low maintenance effort.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_visualiser.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1339 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 8 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 96 commits in rashomon-gh/attention-visualiser
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • trained(model_name) model.eval() tokenizer = AutoTokenizer.from_pretrained(model_name)
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository rashomon-gh/attention-visualiser appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with attention-visualiser 
Create a mini-application named 'TransformerAttentionExplorer' using Python and the 'attention-visualiser' package. This application will allow users to input any text and select a pre-trained Hugging Face Transformer model to analyze the attention weights of the given text. Here’s a detailed plan for building this application:

1. **Setup**: Begin by installing necessary packages including 'transformers', 'torch', 'attention-visualiser', and 'streamlit'. Use Streamlit for the web interface.
2. **Model Selection**: Provide a dropdown menu in the app where users can choose from a variety of pre-trained models such as BERT, GPT-2, RoBERTa, etc.
3. **Text Input**: Include a text area for users to enter their text. Ensure the text is tokenized appropriately for the selected model.
4. **Visualization**: Utilize 'attention-visualiser' to visualize the attention weights of the chosen model on the input text. Display these visualizations in the app.
5. **Interactive Features**: Allow users to hover over different parts of the visualization to see which words are most influential in each attention head.
6. **Customization Options**: Offer options to adjust parameters like number of layers to visualize, number of heads, etc.
7. **Documentation**: Write clear documentation explaining how to use the app, its limitations, and the significance of the attention weights.

The goal is to provide an intuitive tool for understanding how transformers process text through interactive visualizations, making complex concepts more accessible.

💬 Discussion Feed

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