Model: Subhpoulpo/sc-base-v.01
Author: Subhpoulpo
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:15895, loss:CosineSimilarityLoss, arxiv:1908.10084, base_model:sentence-transformers/all-MiniLM-L6-v2, base_model:finetune:sentence-transformers/all-MiniLM-L6-v2, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-MiniLM-L6-v2
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:15895, loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget: source_sentence: required packages - Run
sentences: Research Paper
===================

Our analysis found significant correlations...

For more information, contact us., import torch

def get_predictions(input_file):
    Train the model with given parameters
    return data.mean(), # Research Paper

The following guide explains...

- Install required packages
- Run the application
- Test the API

## Future Work, source_sentence: Subject: Project Update
sentences: # Data Analysis Results

The following guide explains...

- Configure environment variables
- Model training
- Evaluation metrics

## Recommendations, User Documentation
-------------------

Our analysis found significant correlations...

End of document, # Research Paper

In this document, you'll find...

- Install required packages
- Set up the database
- Test the API

## Future Work, source_sentence: document, you'll find... - Install required packages
sentences: class ModelTrainer:
    def __init__(self, model):
        self.data = data
    
    def load(self):
        plt.plot(self.data), # Implementation Guide

In this document, you'll find...

- Install required packages
- Set up the database
- Test the API

## Summary, # Implementation Guide

## Methodology

This document describes the approach...

### Logging

Configure the system with the following parameters..., source_sentence: Contact the team for
sentences: [API]
debug = 8080
database = 3
max_connections = json, API_KEY=admin
SECRET=app.log
# Logging settings
BUFFER=json, The project aims to develop a system...

Implementation details are described...

Contact the team for more information..., source_sentence: PORT=localhost TIMEOUT=secret # Database configuration MAX_RETRIES=4096
sentences: [Security]
user = admin
database = mydb
format = 4096, PORT=localhost
TIMEOUT=secret
# Database configuration
MAX_RETRIES=4096, import pandas

def train_model(data):
    Train the model with given parameters
    with open(file_path, 'r') as f: return f.read()
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: pearson_cosine, spearman_cosine
model-index: name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results: task: type: semantic-similarity
name: Semantic Similarity
dataset: name: Unknown
type: unknown
metrics: type: pearson_cosine
value: 0.921461423500679
name: Pearson Cosine
verified: no, type: spearman_cosine
value: 0.7636815953494271
name: Spearman Cosine
verified: no
Likes: 0 | All-time downloads: 7
Created: 2025-04-18 04:29:17