MNLP_M3_document_encoder_120tok huggingface.co api & zacbrld MNLP_M3_document_encoder_120tok github AI Model

Introduction of MNLP_M3_document_encoder_120tok

Model Details of MNLP_M3_document_encoder_120tok

STEM Embedding Model

🧬 Embedding model optimized for STEM content (Math, Physics, CS, Biology).

Performance

Separation Score : 0.6767 (Excellent!)
Accuracy : 97.18%
Training : 75k+ STEM chunks from Wikipedia + Semantic Scholar

Usage

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("zacbrld/MNLP_M3_document_encoder_120tok")
tokenizer = AutoTokenizer.from_pretrained("zacbrld/MNLP_M3_document_encoder_120tok")

# Encode text
inputs = tokenizer("Neural networks use backpropagation", return_tensors="pt", truncation=True, padding=True)
embeddings = model(**inputs).last_hidden_state.mean(dim=1)

Training Details

Base: sentence-transformers/all-MiniLM-L6-v2
Method: Contrastive learning with triplet loss
Specialized for scientific and technical content

Runs of zacbrld MNLP_M3_document_encoder_120tok on huggingface.co

Total runs

24-hour runs

-1

3-day runs

7-day runs

30-day runs

More Information About MNLP_M3_document_encoder_120tok huggingface.co Model

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MNLP_M3_document_encoder_120tok huggingface.co Url

https://huggingface.co/zacbrld/MNLP_M3_document_encoder_120tok

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MNLP_M3_document_encoder_120tok install

MNLP_M3_document_encoder_120tok is an open source model from GitHub that offers a free installation service, and any user can find MNLP_M3_document_encoder_120tok on GitHub to install. At the same time, huggingface.co provides the effect of MNLP_M3_document_encoder_120tok install, users can directly use MNLP_M3_document_encoder_120tok installed effect in huggingface.co for debugging and trial. It also supports api for free installation.