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m2_joint_label_ref_ptrn_cmbert_io
nlpso/m2_joint_label_ref_ptrn_cmbert_io
m2_joint_label_ref_ptrn_cmbert_io is a token classification model from nlpso. Use it when you need labels on individual words, such as names. It is set up for transformers.
This model is a fine-tuned verion from Jean-Baptiste/camembert-ner for nested NER task on a nested NER Paris trade directories dataset.
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- camembert
- Access
- Public
- Created
- Feb 9, 2023
- Updated
- Feb 16, 2023
- SHA
- 0219919c
Try a prompt
Model card
From the Hugging Face model README
m2_joint_label_ref_ptrn_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 | Entity group (level) | Description |
|---|---|---|
| O | 1 & 2 | Outside of a named entity |
| PER | 1 | Person or company name |
| ACT | 1 & 2 | Person or company professional activity |
| TITREH | 2 | Military or civil distinction |
| DESC | 1 | Entry full description |
| TITREP | 2 | Professionnal reward |
| SPAT | 1 | Address |
| LOC | 2 | Street name |
| CARDINAL | 2 | Street number |
| FT | 2 | Geographical feature |
Experiment parameter
- Pretrained-model : Jean-Baptiste/camembert-ner
- Dataset : ground-truth
- Tagging format : IO
- Recognised entities : 'All'
Load model from the Hugging Face
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("nlpso/m2_joint_label_ref_ptrn_cmbert_io")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m2_joint_label_ref_ptrn_cmbert_io")
- Task
- Token Classification
- Library
- transformers
- Type
- camembert
- Languages
- fr
- Created
- Feb 9, 2023
- Updated
- Feb 16, 2023