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

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

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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-tr

Spaces using wietsedv/xlm-roberta-base-ft-udpos28-tr 2

Evaluation results

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

74.400
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

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

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

73.200
French Test accuracy on Universal Dependencies v2.8
self-reported

71.400
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

71.100
Russian Test accuracy on Universal Dependencies v2.8
self-reported

77.900
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

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

69.200
Danish Test accuracy on Universal Dependencies v2.8
self-reported

73.800

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