roberta base ca v2 cased ner EU AI Act Compliance Profile

Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Named Entity Recognition.

Table of Contents

• Model description
• Intended uses and limitations
• How to use
• Limitations and bias
• Training
  • Training data
  • Training procedure
• Evaluation
  • Variable and metrics
  • Evaluation results
• Additional information
  • Author
  • Contact information
  • Copyright
  • Licensing information
  • Funding
  • Citing information
  • Disclaimer

Model description

The roberta-base-ca-v2-cased-ner is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the roberta-base-ca-v2 model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details).

Intended uses and limitations

roberta-base-ca-v2-cased-ner model can be used to recognize Named Entities in the provided text. The model is limited by its training dataset and may not generalize well for all use cases.

How to use

Here is how to use this model:

from transformers import pipeline
from pprint import pprint

nlp = pipeline("ner", model="projecte-aina/roberta-base-ca-v2-cased-ner")
example = "Em dic Lluïsa i visc a Santa Maria del Camí."

ner_results = nlp(example)
pprint(ner_results)

[{'entity_group': 'person-other', 'score': 0.90749526, 'word': ' George Smith Patton', 'start': 0, 'end': 19}, {'entity_group': 'organization-other', 'score': 0.9260813, 'w

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