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--- |
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datasets: |
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- gguichard/coref_dataset |
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language: |
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- fr |
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library_name: transformers |
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widget: |
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- text: "Un homme, qui parle à son collègue, <s'> avance vers moi." |
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--- |
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# Easter-Island/coref_classifier_ancor |
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## Table of Contents |
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- [Model Details](#model-details) |
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- [Uses](#uses) |
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- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [Citation Information](#citation-information) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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- ## Model Details |
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- **Model Description:** |
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This model is a state-of-the-art language model for French coreference resolution. |
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- **Developed by:** Grégory Guichard |
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- **Model Type:** Token Classification |
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- **Language(s):** French |
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- **License:** MIT |
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- **Parent Model:** See the [Camembert-large model](https://huggingface.co/camembert/camembert-large) for more information about the RoBERTa base model. |
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- **Resources for more information:** |
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## Uses |
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This model can be used for Coreference token classification tasks. |
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The model evaluates, for each token, if it is a reference of the expression between "<>". |
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### Example |
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```python |
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from transformers import pipeline |
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classifier = pipeline("ner", model=model, tokenizer=tokenizer) |
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text = "Un homme me parle. <Il> est beau." |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁Un', '▁homme'] |
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``` |
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This coreference resolver can perform many tasks |
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### Reprise pronominale |
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```python |
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from transformers import AutoModelForTokenClassification, AutoTokenizer |
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model = AutoModelForTokenClassification.from_pretrained("models/merged/ancor_classifier") |
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tokenizer = AutoTokenizer.from_pretrained("models/merged/ancor_classifier_tokenizer") |
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from transformers import pipeline |
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classifier = pipeline("ner", model=model, tokenizer=tokenizer) |
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text = "Platon est un philosophe antique de la Grèce classique... Il reprit le travail philosophique decertains de <ses> prédécesseurs" |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁Platon'] |
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text = "Platon est un philosophe antique de la Grèce classique... <Il> reprit le travail philosophique decertains de ses prédécesseurs" |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁Platon', '▁un', '▁philosophe', '▁antique', '▁de','▁la', '▁Grèce', '▁classique', '▁ses'] |
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``` |
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### Anaphores fidèles |
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```python |
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from transformers import pipeline |
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classifier = pipeline("ner", model=model, tokenizer=tokenizer) |
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text = "Le chat que j’ai adopté court partout... Mais j’aime beaucoup <ce chat> ." |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁Le', '▁chat'] |
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``` |
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### Anaphores infidèles |
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```python |
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from transformers import pipeline |
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classifier = pipeline("ner", model=model, tokenizer=tokenizer) |
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text = "Le chat que j’ai adopté court partout... Mais j’aime beaucoup <cet animal> ." |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁Le', '▁chat'] |
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``` |
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### Paroles rapportées |
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```python |
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from transformers import pipeline |
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classifier = pipeline("ner", model=model, tokenizer=tokenizer) |
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text = """Lionel Jospin se livre en revanche à une longue analyse de son échec du 21 avril. “Ma part de responsabilité dans l’échec existe forcément. <Je> l’ai assumée en quittant la vie politique”""" |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁Lionel', '▁Jos', 'pin', '▁son', 'Ma'] |
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``` |
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### Entités nommées |
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```python |
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from transformers import pipeline |
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classifier = pipeline("ner", model=model, tokenizer=tokenizer) |
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text = "Paris est située sur la Seine. <La plus grande ville de France> compte plus de 10 millions d’habitants." |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁Paris'] |
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``` |
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### Les groupes |
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```python |
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from transformers import pipeline |
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classifier = pipeline("ner", model=model, tokenizer=tokenizer) |
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text = "Jack et Rose commencent à faire connaissance. Ils s’entendent bien. <Le couple> se marie et a des enfants." |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁Jack', '▁et', '▁Rose', '▁Ils'] |
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``` |
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### Groupes dispersés |
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```python |
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from transformers import pipeline |
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classifier = pipeline("ner", model=model, tokenizer=tokenizer) |
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text = "Pierre retrouva sa femme au restaurant. <Le couple> dina jusqu'à tard dans la nuit." |
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[elem['word'] for elem in classifier(text) if elem['entity'] == 'LABEL_1'] |
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# results |
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['▁sa', '▁femme'] # ici il y a une erreur, on devrait avoir "Pierre" également |
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``` |
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## Risks, Limitations and Biases |
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## Training |
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#### Training Data |
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#### Training Procedure |
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## Evaluation |
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## Citation Information |
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## How to Get Started With the Model |
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