
# m1_ind_layers_ref_ptrn_cmbert_iob2_level_1

## Introduction

This model is a model that was fine-tuned from [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) 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 : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Dataset : ground-truth
* Tagging format : IOB2
* Recognised entities : level 1

## Load model from the Hugging Face

**Warning 1 ** : this model only recognises level-1 entities of dataset. It has to be used with [m1_ind_layers_ref_ptrn_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2) to recognise nested entities level-2.

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1")


{"language": "fr", "datasets": ["nlpso/m1_fine_tuning_ref_ptrn_cmbert_iob2"], "tag": "token-classification", "widget": [{"text": "Duflot, loueur de carrosses, r. de Paradis-\u2029505\u2029Poissonni\u00e8re, 22.", "example_title": "Noisy entry #1"}, {"text": "Du\u017four el Besnard, march, de bois \u00e0 bruler,\u2029quai de la Tournelle, 17. etr. des Foss\u00e9s-\u2029SBernard. 11.\u2029D\u00ed", "example_title": "Noisy entry #2"}, {"text": "Dufour (Charles), \u00e9picier, r. St-Denis\u2029\u261e\u2029332", "example_title": "Ground-truth entry #1"}]}
transformers pytorch camembert token-classification fr dataset:nlpso/m1_fine_tuning_ref_ptrn_cmbert_iob2 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #token-classification #fr #dataset-nlpso/m1_fine_tuning_ref_ptrn_cmbert_iob2 #autotrain_compatible #endpoints_compatible #region-us 

m1\_ind\_layers\_ref\_ptrn\_cmbert\_iob2\_level\_1
==================================================


Introduction
------------


This model is a model that was fine-tuned from HueyNemud/das22-10-camembert\_pretrained for nested NER task on a nested NER Paris trade directories dataset.


Dataset
-------


Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature


Experiment parameter
--------------------


* Pretrained-model : HueyNemud/das22-10-camembert\_pretrained
* Dataset : ground-truth
* Tagging format : IOB2
* Recognised entities : level 1


Load model from the Hugging Face
--------------------------------


Warning 1 : this model only recognises level-1 entities of dataset. It has to be used with m1\_ind\_layers\_ref\_ptrn\_cmbert\_iob2\_level\_2 to recognise nested entities level-2.


'''python
from transformers import AutoTokenizer, AutoModelForTokenClassification


tokenizer = AutoTokenizer.from\_pretrained("nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_iob2\_level\_1")
model = AutoModelForTokenClassification.from\_pretrained("nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_iob2\_level\_1")



TAGS
#transformers #pytorch #camembert #token-classification #fr #dataset-nlpso/m1_fine_tuning_ref_ptrn_cmbert_iob2 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #camembert #token-classification #fr #dataset-nlpso/m1_fine_tuning_ref_ptrn_cmbert_iob2 #autotrain_compatible #endpoints_compatible #region-us 
