XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Urdu

This model is part of our paper called:

Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages

Check the Space for more details.

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ur")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ur")

Downloads last month: 108

Safetensors

Model size

277M params

Tensor type

I64

F32

Inference Providers NEW

Token Classification

This model is not currently available via any of the supported third-party Inference Providers, and the model is not deployed on the HF Inference API.

Dataset used to train wietsedv/xlm-roberta-base-ft-udpos28-ur

Space using wietsedv/xlm-roberta-base-ft-udpos28-ur 1

Evaluation results

English Test accuracy on Universal Dependencies v2.8
self-reported

76.900
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

74.300
German Test accuracy on Universal Dependencies v2.8
self-reported

73.500
Italian Test accuracy on Universal Dependencies v2.8
self-reported

71.000
French Test accuracy on Universal Dependencies v2.8
self-reported

68.200
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

72.700
Russian Test accuracy on Universal Dependencies v2.8
self-reported

85.900
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

80.000
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

74.900
Danish Test accuracy on Universal Dependencies v2.8
self-reported

77.400

View on Papers With Code