📊
Bengali Language Ner dataset by Suchandra
⭐ 26.7
📊
Dataset

Bengali Language Ner

by Suchandra hf-model--suchandra--bengali_language_ner
Nexus Index
26.7 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 38
Tech Context
Vital Performance
0 DL / 30D
0.0%
Data Integrity 26.7 FNI Score
- Size
- Rows
Parquet Format
- Tokens
Dataset Information Summary
Entity Passport
Registry ID hf-model--suchandra--bengali_language_ner
Provider huggingface
📜

Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_model__suchandra__bengali_language_ner,
  author = {Suchandra},
  title = {Bengali Language Ner Dataset},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/dataset/hf-model--suchandra--bengali_language_ner}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Suchandra. (2026). Bengali Language Ner [Dataset]. Free2AITools. https://free2aitools.com/dataset/hf-model--suchandra--bengali_language_ner

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Nexus Index V2.0

Methodology Index Protocol
26.7
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 38

💬 Index Insight

FNI V2.0 for Bengali Language Ner: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:38).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live

👁️ Data Preview

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Row-level preview not available for this dataset.

Schema structure is shown in the Field Logic panel when available.

🧬 Field Logic

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Schema not yet indexed for this dataset.

Dataset Specification

Bengali Named Entity Recognition

Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Bengali language.

Label ID and its corresponding label name

Label ID Label Name
0 O
1 B-PER
2 I-PER
3 B-ORG
4 I-ORG
5 B-LOC
6 I-LOC

Results

Name Overall F1 LOC F1 ORG F1 PER F1
Train set 0.997927 0.998246 0.996613 0.998769
Validation set 0.970187 0.969212 0.956831 0.982079
Test set 0.9673011 0.967120 0.963614 0.970938

Example

py 
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Suchandra/bengali_language_NER")
model = AutoModelForTokenClassification.from_pretrained("Suchandra/bengali_language_NER")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "মারভিন দি মারসিয়ান"

ner_results = nlp(example)
ner_results
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Dataset Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--suchandra--bengali_language_ner
slug
suchandra--bengali_language_ner
source
huggingface
author
Suchandra
license
tags

⚙️ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

📊 Engagement & Metrics

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