ICDAR 2023 Historical Nested-NER experiences
Collection
ANR SoDUCo project, experiments published at ICDAR 2023 • 36 items • Updated
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("nlpso/m0_flat_ner_ocr_cmbert_io")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m0_flat_ner_ocr_cmbert_io")Quick Links
m0_flat_ner_ocr_cmbert_io
Introduction
This model is a fine-tuned verion from Jean-Baptiste/camembert-ner for nested NER task on a nested NER Paris trade directories dataset.
Dataset
| Abbreviation | Description |
|---|---|
| O | Outside of a named entity |
| PER | Person or company name |
| ACT | Person or company professional activity |
| TITRE | Distinction |
| LOC | Street name |
| CARDINAL | Street number |
| FT | Geographical feature |
Experiment parameter
- Pretrained-model : Jean-Baptiste/camembert-ner
- Dataset : noisy (Pero OCR)
- Tagging format : IO
- Recognised entities : All (flat entities)
Load model from the HuggingFace
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("nlpso/m0_flat_ner_ocr_cmbert_io")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m0_flat_ner_ocr_cmbert_io")
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nlpso/m0_flat_ner_ocr_cmbert_io")