es_trf_ner_cds_xlm-large

Introduction

This model is a fine-tuned version of xlm-roberta-large for Named-Entity Recognition, in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC).

Usage

You can use this model with Transformers pipeline for NER.

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("es_trf_ner_cds_xlm-large")
model = AutoModelForTokenClassification.from_pretrained("es_trf_ner_cds_xlm-large")

example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo."
ner_pipe = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")

for ent in ner_pipe(example):
    print(ent)

Dataset

ToDo

Model performance

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3