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@ -24,16 +24,18 @@ Training data was classified as follow:
Abbreviation|Description Abbreviation|Description
-|- -|-
O| Outside of a named entity O |Outside of a named entity
MISC | Miscellaneous entity MISC |Miscellaneous entity
PER | Person’s name PER |Person’s name
ORG | Organization ORG |Organization
LOC | Location LOC |Location
In order to simplify, the prefix B- or I- from original conll2003 was removed. In order to simplify, the prefix B- or I- from original conll2003 was removed.
I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size: I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size:
Train | 17494
Validation | 3250 Train | Validation
-|-
17494 | 3250
## How to use camembert-ner with HuggingFace ## How to use camembert-ner with HuggingFace
@ -90,31 +92,31 @@ nlp("Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne to
## Model performances ## Model performances
Model performances computed on conll2003 validation dataset (computed on the tokens predictions) Model performances computed on conll2003 validation dataset (computed on the tokens predictions)
```
entity | precision | recall | f1 entity|precision|recall|f1
- | - | - | - -|-|-|-
PER | 0.9914 | 0.9927 | 0.9920 PER|0.9914|0.9927|0.9920
ORG | 0.9627 | 0.9661 | 0.9644 PER|0.9914|0.9927|0.9920
LOC | 0.9795 | 0.9862 | 0.9828 ORG|0.9627|0.9661|0.9644
MISC | 0.9292 | 0.9262 | 0.9277 LOC|0.9795|0.9862|0.9828
Overall | 0.9740 | 0.9766 | 0.9753 MISC|0.9292|0.9262|0.9277
``` Overall|0.9740|0.9766|0.9753
On private dataset (email, chat, informal discussion), computed on word predictions: On private dataset (email, chat, informal discussion), computed on word predictions:
```
entity | precision | recall | f1
- | - | - | -
PER | 0.8823 | 0.9116 | 0.8967
ORG | 0.7694 | 0.7292 | 0.7487
LOC | 0.8619 | 0.7768 | 0.8171
```
Spacy (en_core_web_trf-3.2.0) on the same private dataset was giving: entity|precision|recall|f1
``` -|-|-|-
entity | precision | recall | f1 PER|0.8823|0.9116|0.8967
- | - | - | - ORG|0.7694|0.7292|0.7487
PER | 0.9146 | 0.8287 | 0.8695 LOC|0.8619|0.7768|0.8171
ORG | 0.7655 | 0.6437 | 0.6993
LOC | 0.8727 | 0.6180 | 0.7236 By comparison on the same private dataset, Spacy (en_core_web_trf-3.2.0) was giving:
```
entity|precision|recall|f1
-|-|-|-
PER|0.9146|0.8287|0.8695
ORG|0.7655|0.6437|0.6993
LOC|0.8727|0.6180|0.7236