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embeddinggemma-300m

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📋 Documentation Enhancement Suggestion

This observation was generated by Crovia — the AI transparency observation layer.

Crovia does not accuse or judge. It observes publicly available information and suggests improvements.

📊 Quick Stats

💻 Ready-to-Use Code

from transformers import AutoModel, AutoTokenizer

model_id = "google/embeddinggemma-300m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id)

# Example usage
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)

📚 Citation

If you use this model, please cite:

 @misc {google_embeddinggemma_300m_2026,
  author = {google},
  title = {google/embeddinggemma-300m},
  year = {2026},
  url = {https://huggingface.co/google/embeddinggemma-300m},
  note = {Accessed via CROVIA transparency registry}
}

⚖️ EU AI Act Compliance Checklist

• Training data disclosed
• License clearly stated
• Intended use documented
• Model limitations documented
• Evaluation metrics provided
• Bias/fairness analysis

🔍 Training Data Transparency

Training Data Status: Documentation not found

No training data section was observed in the public model card.

This is an observation, not an accusation. Many valid reasons exist for this status.

If you'd like to improve documentation, consider adding:

• Dataset names and versions used
• Data collection methodology
• Preprocessing steps applied
• Known limitations

This may help users understand your model better and prepare for upcoming transparency requirements (e.g., EU AI Act).

Enhancement generated by CROVIA · Package ID: 928c04ec5665
Generated at: 2026-02-09T19:31:55.837624Z

This suggestion was generated by Crovia — the AI transparency observation layer.

Crovia does not accuse or judge. It observes publicly available information and suggests documentation improvements.

If this suggestion is helpful, consider adding the recommended sections to your model card.
If not applicable, feel free to close this discussion.

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