| | --- |
| | 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](https://huggingface.co/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](https://www.SBERT.net) installed: |
| |
|
| | ``` |
| | pip install -U sentence-transformers |
| | ``` |
| |
|
| | Then you can use the model like this: |
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| | |
| | model = SentenceTransformer("cuteo23/scam_finetuned") |
| | embeddings = model.encode(["Hello world"]) |
