Token Classification
Transformers
Safetensors
Vietnamese
roberta
ner
vietnamese
address-parsing
phobert
Eval Results (legacy)
Instructions to use open-thienhang-com/bert_all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use open-thienhang-com/bert_all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="open-thienhang-com/bert_all")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("open-thienhang-com/bert_all") model = AutoModelForTokenClassification.from_pretrained("open-thienhang-com/bert_all", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add model bert_all
Browse files- README.md +7 -7
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README.md
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name: Vietnamese Address NER
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metrics:
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---
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# bert_all
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**Source artefact**: `bert_all`
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**Base model**: `vinai/phobert-base`
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**Trained on**: Vietnamese address corpus (mixed sources)
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**Saved at**: `2026-06-
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## Metrics
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| Metric | Value |
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| Precision | **
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## Labels
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name: Vietnamese Address NER
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metrics:
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- type: f1
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value: 0.8212719298245614
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- type: precision
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value: 0.7741602067183463
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- type: recall
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value: 0.8744892002335085
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---
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# bert_all
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**Source artefact**: `bert_all`
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**Base model**: `vinai/phobert-base`
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**Trained on**: Vietnamese address corpus (mixed sources)
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**Saved at**: `2026-06-06T12:29:40.098669+00:00`
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## Metrics
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| Metric | Value |
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|-----------|-----------|
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| Precision | **0.7742** |
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| Recall | **0.8745** |
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| F1 | **0.8213** |
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## Labels
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model.safetensors
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