Zero-Shot Cross-Lingual POS Tagging for Filipino
Collection
Collection of models involve: XLM-R and RoBERTa Monolingual and Combination (Top 2 to 5)https://aclanthology.org/2024.fieldmatters-1.9/ • 30 items • Updated • 1
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("iceman2434/roberta-tagalog-base-ft-udpos213-en")
model = AutoModelForTokenClassification.from_pretrained("iceman2434/roberta-tagalog-base-ft-udpos213-en", device_map="auto")Quick Links
Model Specification
- Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz)
- Training Data:
- English corpora (Top 1 Language)
- Training Details:
- Base configurations
Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 71.63% Accuracy)
POS Tags
- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="iceman2434/roberta-tagalog-base-ft-udpos213-en")