Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use ushakov15/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ushakov15/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ushakov15/MNLP_M3_document_encoder") 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 ushakov15/MNLP_M3_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ushakov15/MNLP_M3_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ushakov15/MNLP_M3_document_encoder") model = AutoModelForCausalLM.from_pretrained("ushakov15/MNLP_M3_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f7828f2f177a8fecc364286be9c2c09ea5c1890f3c6f6b4c0ef6aa5f57d44c5
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
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