roberta-large-sts-b Open-source Model - Free Deployment for Accurate Text Similarity Calculation

Roberta Large Sts B

Developed by SparkBeyond

A text similarity calculation model fine-tuned on the STS-B dataset based on RoBERTa-large

Text Embedding #Text similarity calculation #RoBERTa fine-tuning #Semantic matching

Downloads 19

Release Time : 3/2/2022

Model Overview

This model is specifically designed to calculate semantic similarity between two sentences, outputting a similarity score ranging from 0 to 5. Suitable for natural language processing tasks requiring text similarity measurement.

Model Features

High-precision similarity calculation

Fine-tuned on the standard STS-B dataset, capable of accurately capturing semantic similarity between sentences

Large-scale pre-training foundation

Based on the RoBERTa-large model with powerful semantic understanding capabilities

Batch processing support

Provides batch processing functionality for efficient handling of multiple sentence pairs

Model Capabilities

Text similarity calculation

Semantic relevance evaluation

Sentence pair comparison

Use Cases

Information retrieval

Search result ranking

Re-rank search results based on semantic similarity between queries and documents

Improves relevance of search results

Text matching

Q&A systems

Match user questions with candidate answers in knowledge bases

Improves accuracy of Q&A systems

🚀 Roberta Large STS-B

This model is a fine-tuned RoBERTA model on the STS-B dataset, which can effectively handle semantic similarity tasks.

🚀 Quick Start

This model is a fine-tuned RoBERTA model over STS-B. It was trained with the following parameters:

!python /content/transformers/examples/text-classification/run_glue.py \
    --model_type roberta \
    --model_name_or_path roberta-large \
    --task_name STS-B \
    --do_train \
    --do_eval \
    --do_lower_case \
    --data_dir /content/glue_data/STS-B/ \
    --max_seq_length 128 \
    --per_gpu_eval_batch_size=8   \
    --per_gpu_train_batch_size=8   \
    --learning_rate 2e-5 \
    --num_train_epochs 3.0 \
    --output_dir /content/roberta-sts-b

💻 Usage Examples

Basic Usage

import toolz
import torch
batch_size = 6

def roberta_similarity_batches(to_predict):
  batches = toolz.partition(batch_size, to_predict)
  similarity_scores = []  
  for batch in batches: 
    sentences = [(sentence_similarity["sent1"], sentence_similarity["sent2"])  for sentence_similarity in batch]   
    batch_scores = similarity_roberta(model, tokenizer,sentences)
    similarity_scores = similarity_scores + batch_scores[0].cpu().squeeze(axis=1).tolist()
  return similarity_scores

def similarity_roberta(model, tokenizer, sent_pairs):
  batch_token = tokenizer(sent_pairs, padding='max_length', truncation=True, max_length=500)
  res = model(torch.tensor(batch_token['input_ids']).cuda(), attention_mask=torch.tensor(batch_token["attention_mask"]).cuda())  
  return res

similarity_roberta(model, tokenizer, [('NEW YORK--(BUSINESS WIRE)--Rosen Law Firm, a global investor rights law firm, announces it is investigating potential securities claims on behalf of shareholders of Vale S.A. ( VALE ) resulting from allegations that Vale may have issued materially misleading business information to the investing public',
                                       'EQUITY ALERT: Rosen Law Firm Announces Investigation of Securities Claims Against Vale S.A. – VALE')])

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