Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
Generated from Trainer
dataset_size:9741
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Matjac5/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matjac5/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/MNLP_M2_document_encoder") sentences = [ "His anger reached a boiling point, the therapist said they should take a break and what?", "chicken coop", "cool off", "bark" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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| "additional_special_tokens": [ | |
| "<|im_start|>", | |
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| "<|quad_start|>", | |
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| "<|vision_start|>", | |
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| "<|vision_pad|>", | |
| "<|image_pad|>", | |
| "<|video_pad|>" | |
| ], | |
| "eos_token": { | |
| "content": "<|im_end|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |