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Bert Base Chinese Finetuned Ner dataset by leonadase
⭐ 25.6
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Dataset

Bert Base Chinese Finetuned Ner

by leonadase hf-model--leonadase--bert-base-chinese-finetuned-ner
Nexus Index
25.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 23
Tech Context
Vital Performance
0 DL / 30D
0.0%
Data Integrity 25.6 FNI Score
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- Rows
Parquet Format
- Tokens
Dataset Information Summary
Entity Passport
Registry ID hf-model--leonadase--bert-base-chinese-finetuned-ner
Provider huggingface
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Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_model__leonadase__bert_base_chinese_finetuned_ner,
  author = {leonadase},
  title = {Bert Base Chinese Finetuned Ner Dataset},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/dataset/hf-model--leonadase--bert-base-chinese-finetuned-ner}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
leonadase. (2026). Bert Base Chinese Finetuned Ner [Dataset]. Free2AITools. https://free2aitools.com/dataset/hf-model--leonadase--bert-base-chinese-finetuned-ner

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
25.6
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 23

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FNI V2.0 for Bert Base Chinese Finetuned Ner: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:23).

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Dataset Specification

bert-base-chinese-finetuned-ner

This model is a fine-tuned version of bert-base-chinese on the fdner dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1016
  • Precision: 0.9146
  • Recall: 0.9414
  • F1: 0.9278
  • Accuracy: 0.9751

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 10
  • eval_batch_size: 10
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 2 0.9181 0.1271 0.1255 0.1263 0.7170
No log 2.0 4 0.8048 0.1919 0.2385 0.2127 0.7669
No log 3.0 6 0.7079 0.2422 0.3264 0.2781 0.7980
No log 4.0 8 0.6201 0.3505 0.4854 0.4070 0.8338
No log 5.0 10 0.5462 0.3898 0.4812 0.4307 0.8611
No log 6.0 12 0.4851 0.4749 0.5941 0.5279 0.8802
No log 7.0 14 0.4338 0.5213 0.6151 0.5643 0.8936
No log 8.0 16 0.3843 0.5663 0.6611 0.6100 0.9076
No log 9.0 18 0.3451 0.6255 0.6987 0.6601 0.9214
No log 10.0 20 0.3058 0.6719 0.7197 0.6949 0.9293
No log 11.0 22 0.2783 0.6808 0.7406 0.7094 0.9344
No log 12.0 24 0.2497 0.7050 0.7699 0.7360 0.9427
No log 13.0 26 0.2235 0.7519 0.8117 0.7807 0.9506
No log 14.0 28 0.2031 0.7713 0.8326 0.8008 0.9552
No log 15.0 30 0.1861 0.7915 0.8577 0.8233 0.9593
No log 16.0 32 0.1726 0.8031 0.8703 0.8353 0.9613
No log 17.0 34 0.1619 0.8320 0.8912 0.8606 0.9641
No log 18.0 36 0.1521 0.8571 0.9038 0.8798 0.9674
No log 19.0 38 0.1420 0.8710 0.9038 0.8871 0.9695
No log 20.0 40 0.1352 0.8795 0.9163 0.8975 0.9700
No log 21.0 42 0.1281 0.8755 0.9121 0.8934 0.9712
No log 22.0 44 0.1209 0.8916 0.9289 0.9098 0.9728
No log 23.0 46 0.1155 0.8924 0.9372 0.9143 0.9733
No log 24.0 48 0.1115 0.904 0.9456 0.9243 0.9746
No log 25.0 50 0.1087 0.9116 0.9498 0.9303 0.9746
No log 26.0 52 0.1068 0.9146 0.9414 0.9278 0.9740
No log 27.0 54 0.1054 0.9146 0.9414 0.9278 0.9743
No log 28.0 56 0.1036 0.9146 0.9414 0.9278 0.9743
No log 29.0 58 0.1022 0.9146 0.9414 0.9278 0.9746
No log 30.0 60 0.1016 0.9146 0.9414 0.9278 0.9751

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0
🤗 Data Source: Hugging Face ↗
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AI Summary: Based on Hugging Face metadata. Not a recommendation.

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id
hf-model--leonadase--bert-base-chinese-finetuned-ner
slug
leonadase--bert-base-chinese-finetuned-ner
source
huggingface
author
leonadase
license
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