Text Classification
sentence-transformers
Safetensors
Transformers
English
sentence-similarity
feature-extraction
text-embeddings-inference
information-retrieval
knowledge-distillation
Instructions to use MongoDB/mdbr-leaf-ir-asym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MongoDB/mdbr-leaf-ir-asym with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MongoDB/mdbr-leaf-ir-asym") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use MongoDB/mdbr-leaf-ir-asym with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MongoDB/mdbr-leaf-ir-asym")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MongoDB/mdbr-leaf-ir-asym", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download document_1_Pooling/config.json from MongoDB/mdbr-leaf-ir-asym: direct link, hf CLI and curl.
- Browser
- Download file 312 Bytes
-
https://huggingface.co/MongoDB/mdbr-leaf-ir-asym/resolve/main/document_1_Pooling/config.json
- Command line
-
hf download hf://MongoDB/mdbr-leaf-ir-asym/document_1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/MongoDB/mdbr-leaf-ir-asym/resolve/main/document_1_Pooling/config.json
312 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |