README.md

---
language: multilingual
tags:
- text-classification
- pytorch
- nli
- xnli
- de
datasets:
- xnli

pipeline_tag: zero-shot-classification

---

# German Zeroshot

## Model Description

This model has [GBERT Large](https://huggingface.co/deepset/gbert-large) as base model and fine-tuned it on xnli de dataset

#### Zero-shot classification pipeline

```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
                      model="Sahajtomar/German_Zeroshot")

# we will classify the Russian translation of, "Who are you voting for in 2020?"
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': ['politics', 'Europe', 'public health'],
#  'scores': [0.9048484563827515, 0.05722189322113991, 0.03792969882488251],
#  'sequence': 'За кого вы голосуете в 2020 году?'}
```

The default hypothesis template is the English, `This text is {}`. If you are working strictly within one language, it
may be worthwhile to translate this to the language you are working with:

```python
sequence_to_classify = "¿A quién vas a votar en 2020?"
candidate_labels = ["Europa", "salud pública", "política"]
hypothesis_template = "Este ejemplo es {}."
classifier(sequence_to_classify, 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'}"""
```