B

Bert Base Uncased Finetuned Rte Run Trial3

Developed by BoranIsmet
A model fine-tuned based on bert-base-uncased for textual entailment recognition tasks, with an accuracy of 66.43%
Downloads 59
Release Time : 4/7/2025

Model Overview

This model is a fine-tuned version of the BERT base (uncased) model for the Recognizing Textual Entailment (RTE) task, primarily used to determine the logical relationship between two sentences

Model Features

BERT Architecture Advantages
Bidirectional encoder representations based on Transformer, effectively capturing contextual semantic information
Task-specific Fine-tuning
Specially optimized for textual entailment recognition tasks, suitable for determining logical relationships between sentences
Efficient Training
Fine-tuned with relatively small batch size (128) and learning rate (2e-05)

Model Capabilities

Textual Entailment Recognition
Sentence Relationship Judgment
Natural Language Understanding

Use Cases

Natural Language Processing
Text Logical Relationship Judgment
Determine whether an entailment relationship exists between two sentences
Validation set accuracy 66.43%
Question Answering System Support
Assist QA systems in determining whether answers are entailed in given texts
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase