distilbert-base-nli-mean-tokens
| Property | Value |
|---|---|
| Parameter Count | 66.4M |
| License | Apache 2.0 |
| Paper | Sentence-BERT Paper |
| Downloads | 232,750 |
What is distilbert-base-nli-mean-tokens?
This is a Sentence-BERT model based on DistilBERT architecture that maps sentences and paragraphs to 768-dimensional dense vector space. However, it's important to note that this model is now deprecated due to producing lower quality sentence embeddings compared to newer alternatives.
Implementation Details
The model utilizes a DistilBERT base architecture combined with mean token pooling strategy. It's implemented using the sentence-transformers framework and can be easily used with both sentence-transformers and HuggingFace Transformers libraries.
- Supports max sequence length of 128 tokens
- Uses mean pooling over token embeddings
- Outputs 768-dimensional embeddings
- Implements F32 tensor type
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Text clustering applications
- Semantic search functionality
Frequently Asked Questions
Q: What makes this model unique?
This model combines DistilBERT's efficiency with mean token pooling, making it lightweight compared to BERT-based alternatives. However, its deprecated status means it's not recommended for new projects.
Q: What are the recommended use cases?
While historically used for semantic search and text similarity tasks, it's recommended to use newer sentence-transformer models listed on SBERT.net due to this model's known quality limitations.
