BERT-base-uncased-finetuned-RTE - Run Trial3 Open-source Model: Precise Recognition of Textual Entailment Relationships

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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%

Text Classification

Transformers

Open Source License:Apache-2.0 #Textual Entailment Recognition #BERT Fine-tuning #Low-resource Adaptation

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

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	20	0.6834	0.5271
No log	2.0	40	0.6524	0.6318
No log	3.0	60	0.6944	0.6065
No log	4.0	80	0.6385	0.6606
No log	5.0	100	0.6890	0.6570
No log	6.0	120	0.7396	0.6643
No log	7.0	140	0.8757	0.6318
No log	8.0	160	0.8490	0.6390
No log	9.0	180	0.8816	0.6498
No log	10.0	200	0.8988	0.6462

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Bert Base Uncased Finetuned Rte Run Trial3

Model Overview

Model Features

Model Capabilities

Use Cases

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