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

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

Downloads last month: 1,454

Safetensors

Model size

277M params

Tensor type

I64

F32

Inference Examples

Token Classification

This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

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

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

Evaluation results

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

76.600
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

73.700
German Test accuracy on Universal Dependencies v2.8
self-reported

70.500
Italian Test accuracy on Universal Dependencies v2.8
self-reported

75.100
French Test accuracy on Universal Dependencies v2.8
self-reported

71.300
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

74.500
Russian Test accuracy on Universal Dependencies v2.8
self-reported

80.300
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

79.300
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

75.700
Danish Test accuracy on Universal Dependencies v2.8
self-reported

80.400

View on Papers With Code