README.md

metadata

language: en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - text-embeddings-inference
datasets:
  - s2orc
  - flax-sentence-embeddings/stackexchange_xml
  - ms_marco
  - gooaq
  - yahoo_answers_topics
  - code_search_net
  - search_qa
  - eli5
  - snli
  - multi_nli
  - wikihow
  - natural_questions
  - trivia_qa
  - embedding-data/sentence-compression
  - embedding-data/flickr30k-captions
  - embedding-data/altlex
  - embedding-data/simple-wiki
  - embedding-data/QQP
  - embedding-data/SPECTER
  - embedding-data/PAQ_pairs
  - embedding-data/WikiAnswers
pipeline_tag: text-classification
metrics:
  - f1
base_model:
  - sentence-transformers/all-mpnet-base-v2

My Fine-Tuned Sentence Transformer

This model is fine-tuned from sentence-transformers/all-mpnet-base-v2. It was trained in pair with a custom classifier to predict scam/not scam scenarios.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("cuteo23/scam_finetuned")
embeddings = model.encode(["Hello world"])