update readme with easier zeroshot code
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README.md
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README.md
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@ -38,8 +38,18 @@ This multilingual model can perform natural language inference (NLI) on 100 lang
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As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model, introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf).
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As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model, introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf).
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## Intended uses & limitations
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### How to use the model
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#### How to use the model
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#### Simple zero-shot classification pipeline
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```python
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from transformers import pipeline
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classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
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sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
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candidate_labels = ["politics", "economy", "entertainment", "environment"]
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output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
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print(output)
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```
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#### NLI use-case
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```python
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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import torch
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