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An Approach to Evaluating Subjective Answers using BERT model paper by Independent / Community
⭐ 64.7
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Paper

An Approach to Evaluating Subjective Answers using BERT model

by Independent / Community 0051ed2a0e219c1ceabfd76d9993ab46297d115d
Free2AITools Nexus Index
64.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 74
P: Popularity 49
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

The state of art model for language translation, conversion from hand written to digital text, transcription are succeeded in wide range of fields using Natural Language Processing, Artificial Intelligence and Machine Learning (AIML) applications. In present, evaluation of subjective answers are not exercised systematically and graded using computer system. In this work, a mathematical method is proposed for evaluating subjective answers using Bidirectional Encoder Representation Transformers...

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Semantic Scholar 7 Citations
Paper Information Summary
Entity Passport
Registry ID 0051ed2a0e219c1ceabfd76d9993ab46297d115d
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{0051ed2a0e219c1ceabfd76d9993ab46297d115d,
  author = {Unknown},
  title = {An Approach to Evaluating Subjective Answers using BERT model Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0051ed2a0e219c1ceabfd76d9993ab46297d115d}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). An Approach to Evaluating Subjective Answers using BERT model [Paper]. Free2AITools. https://api.semanticscholar.org/0051ed2a0e219c1ceabfd76d9993ab46297d115d

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Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 74
Popularity (P) 49
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for An Approach to Evaluating Subjective Answers using BERT model: Authority (A:74), Popularity (P:49), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
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πŸ“ Executive Summary

"The state of art model for language translation, conversion from hand written to digital text, transcription are succeeded in wide range of fields using Natural Language Processing, Artificial Intelligence and Machine Learning (AIML) applications. In present, evaluation of subjective answers are not exercised systematically and graded using computer system. In this work, a mathematical method is proposed for evaluating subjective answers using Bidirectional Encoder Representation Transformers..."

❝ Cite Node 

@article{Unknown2026An,
  title={An Approach to Evaluating Subjective Answers using BERT model},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ7CitationsSemantic Scholar
πŸ›οΈ74AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈknowledge retrievalField

🏷️ Research Topics

transformer architecture
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πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
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citations
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