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Sankalp-Bahad
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Multilingual-NER-Model

Token Classification
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xlm-roberta
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("token-classification", model="Sankalp-Bahad/Multilingual-NER-Model")
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Sankalp-Bahad/Multilingual-NER-Model")
model = AutoModelForTokenClassification.from_pretrained("Sankalp-Bahad/Multilingual-NER-Model")
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Multilingual-NER-Model

This model is developed by fine-tuning XLM-Roberta-Base on the NER-Dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • optimizer: Adam
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Framework versions

  • Transformers 4.38.2
  • Pytorch 1.9.1
  • Datasets 2.14.6
  • Tokenizers 0.15.0
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Dataset used to train Sankalp-Bahad/Multilingual-NER-Model