Instructions to use razzaghi/tuning_intfloat_E5_small_encode_3m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use razzaghi/tuning_intfloat_E5_small_encode_3m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("razzaghi/tuning_intfloat_E5_small_encode_3m") 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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---
language: fa
tags:
- persian
- job-embedding
- ISCO
datasets:
- text-classification
- text-similarity
- triplet
model_name: tuning_E5_embedding_3m
library_name: sentence-transformers
license: mit
widget:
- text: "Your sample text here"
---
# Persian Job Title Embedding
This model predicts ISCO codes for Persian job titles using embeddings. It includes three datasets:
- Text Classification
- Text Similarity
- Positive and Negative Texts
## Model Details
- **Language:** Persian (Farsi)
- **Model Type:** STS (Semantic Textual Similarity)
- **Framework:** Hugging Face Sentence Transformers
- **License:** MIT
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