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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f26bc1cc8f0165387549ed9f0c32730b050c188d06acd72375c2bb7c2ab1381d
- Size of remote file:
- 1.19 GB
- SHA256:
- 0437e45c94563b09e13cb7a64478fc406947a93cb34a7e05870fc8dcd48e23fd
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