robertuito ner EU AI Act Compliance Profile

Named Entity Recognition model for Spanish/English

robertuito-ner

Repository: https://github.com/pysentimiento/pysentimiento/

Model trained with the Spanish/English split of the LinCE NER corpus, a code-switched benchmark . Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.

Usage

If you want to use this model, we suggest you use it directly from the pysentimiento library as it is not working properly with the pipeline due to tokenization issues

from pysentimiento import create_analyzer

ner_analyzer = create_analyzer("ner", lang="es")

ner_analyzer.predict(
  "rindanse ante el mejor, leonel andres messi cuccitini. serresiete no existis, segui en al-nassr"
)
 

# [{'type': 'PER',
#   'text': 'leonel andres messi cuccitini',
#   'start': 24,
#   'end': 53},
#  {'type': 'PER', 'text': 'serresiete', 'start': 55, 'end': 65},
#  {'type': 'LOC', 'text': 'al-nassr', 'start': 108, 'end': 116}]

Results

Results are taken from the LinCE leaderboard

| Model | Sentiment | NER | POS | |:-----------------------|:----------------|:-------------------|:--------| | RoBERTuito | 60.6 | 68.5 | 97.2 | | XLM Large | -- | 69.5 | 97.2 | | XLM Base | -- | 64.9 | 97.0 | | C2S mBERT | 59.1 | 64.6 | 96.9 | | mBERT | 56.4 | 64.0 | 97.1 | | BERT | 58.4 | 61.1 | 96.9 | | BETO | 56.5 | -- | -- |

Citation

If you use this model in your research, please cite pysentimiento, RoBERTuito and LinCE papers:

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