facebook/xnli
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How to use Sahajtomar/German_Zeroshot with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("zero-shot-classification", model="Sahajtomar/German_Zeroshot") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Sahajtomar/German_Zeroshot")
model = AutoModelForSequenceClassification.from_pretrained("Sahajtomar/German_Zeroshot")This model has GBERT Large as base model and fine-tuned it on xnli de dataset.
The default hypothesis template is in English: This text is {}. While using this model , change it to "In deisem geht es um {}." or something different. While inferencing through huggingface api may give poor results as it uses by default english template. Since model is monolingual and not multilingual, hypothesis template needs to be changed accordingly.
Accuracy: 85.5
Accuracy: 83.6
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="Sahajtomar/German_Zeroshot")
sequence = "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie"
candidate_labels = ["Verbrechen","Tragödie","Stehlen"]
hypothesis_template = "In deisem geht es um {}." ## Since monolingual model,its sensitive to hypothesis template. This can be experimented
classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)
"""{'labels': ['Tragödie', 'Verbrechen', 'Stehlen'],
'scores': [0.8328856854438782, 0.10494536352157593, 0.06316883927583696],
'sequence': 'Letzte Woche gab es einen Selbstmord in einer nahe gelegenen Kolonie'}"""