Feature Extraction
sentence-transformers
Safetensors
bert
embeddings
mplads
lok-sabha
indian-government
multilingual
text-embeddings-inference
Instructions to use ZeroiJ/mplads-ls-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ZeroiJ/mplads-ls-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ZeroiJ/mplads-ls-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] - Notebooks
- Google Colab
- Kaggle
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Download README.md from ZeroiJ/mplads-ls-encoder: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/ZeroiJ/mplads-ls-encoder/resolve/main/README.md
- Command line
-
hf download hf://ZeroiJ/mplads-ls-encoder/README.md
-
curl -L -o README.md https://huggingface.co/ZeroiJ/mplads-ls-encoder/resolve/main/README.md
1.33 kB
metadata
tags:
- sentence-transformers
- feature-extraction
- embeddings
- mplads
- lok-sabha
- indian-government
- multilingual
license: apache-2.0
language:
- en
- hi
- ta
- te
- bn
- mr
- gu
- kn
- ml
- pa
- ur
- or
- as
- sd
- ne
- si
datasets:
- ZeroiJ/mplads-works
MPLADS Lok Sabha Embedding Model
Fine-tuned paraphrase-multilingual-MiniLM-L12-v2 for Lok Sabha MPLADS work descriptions.
Use with sentence-transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ZeroiJ/mplads-ls-encoder")
emb = model.encode("construction of pucca road in Gram Panchayat")
Use with the Hugging Face Inference API
curl https://api-inference.huggingface.co/models/ZeroiJ/mplads-ls-encoder \
-X POST \
-H "Authorization: Bearer ${HF_TOKEN}" \
-H "Content-Type: application/json" \
-d '{"inputs": "construction of pucca road in Gram Panchayat"}'
Model details
- Base:
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 - Fine-tuning objective: cosine-similarity contrastive loss on curated MPLADS work pairs
- Output: 384-dimensional L2-normalized embeddings
- Validation cosine accuracy: ~96.3%
- Built for the MPLADS (SIH26102) fraud-detection demo.