---

language: German

tags:

- text-classification

- pytorch

- nli

- de


pipeline_tag: zero-shot-classification

widget:

- text: "Ich habe ein Problem mit meinem Iphone das so schnell wie möglich gelöst werden muss."

  candidate_labels: "Computer, Handy, Tablet, dringend, nicht dringend"

  hypothesis_template: "In diesem Satz geht es um das Thema {}."

---

# SVALabs - Gbert Large Zeroshot Nli  

In this repository, we present our german zeroshot model. 

This model was trained on the basis of the German BERT large model from [deepset.ai](https://huggingface.co/deepset/gbert-large) and finetuned for natural language inference based on 847.862 machine-translated nli sentence pairs, using the [mnli](https://huggingface.co/datasets/multi_nli), [anli](https://huggingface.co/datasets/anli) and [snli](https://huggingface.co/datasets/snli) datasets.

For this purpose, we translated the sentence pairs in these dataset to German.

### Model Details

| | Description or Link  |
|---|---|
|**Base model**   | [```gbert-large```](https://huggingface.co/deepset/gbert-large) |
|**Finetuning task**| Text Pair Classification / Natural Language Inference  |
|**Source dataset**| [```mnli```](https://huggingface.co/datasets/multi_nli) ; [```anli```](https://huggingface.co/datasets/anli) ; [```snli```](https://huggingface.co/datasets/snli)   |

### Performance

We evaluated our model for the nli task using the TEST set of the German part of the [xnli](https://huggingface.co/datasets/xnli dataset).

TEST-Set Accuracy: 86% 


## Zeroshot Text Classification Task Benchmark 

We further tested our model for a zeroshot text classification task using a part of the [10kGNAD Dataset](https://tblock.github.io/10kGNAD/).
Specifically, we used all articles that were labeled "Kultur", "Sport", "Web", "Wirtschaft" und "Wissenschaft". 

The next table shows the results as well as a comparison with other German language zeroshot options performing the same task: 

| Model               | NDCG@1 | NDCG@5 | NDCG@10 | Recall@1 | Recall@5 | Recall@10 |

|:-------------------:|:------:|:------:|:-------:|:--------:|:--------:|:---------:|

| BM25                | 0.1463 | 0.3451 | 0.4097  | 0.1463   | 0.5424   | 0.7415    |

| BM25(Top 100) +Ours | 0.6410 | 0.7885 | 0.7943  | 0.6410   | 0.8576   | 0.9024    |

## Other Applications



DESCRIPTION GOES HERE: 
Satz 1:
"Ich habe ein Problem mit meinem Iphone das so schnell wie möglich gelöst werden muss" 
Satz 2: 
"Ich hab ein kleines Problem mit meinem Macbook, und auch wenn die Reparatur nicht eilt, würde ich es gerne addressieren."
Label: 
["Computer", "Handy", "Tablet", "dringend", "nicht dringend"] 

EMOTION EXAMPLE:
"Ich bin entäuscht, dass ich kein Ticket für das Konzert meiner Lieblingsband bekommen habe."
label: "Furcht, Freude, Wut , Überraschung,  Traurigkeit, Ekel, Verachtung"

 
   - text: "Wer ist die reichste Person der Welt" 

  candidate_labels: "Frage, Schlagwörter"

  hypothesis_template: "Hierbei handelt es sich um {}."

""""""""



```python

from transformers import pipeline

classifier = pipeline("zero-shot-classification",

                      model="Dehnes/zeroshot_gbert")

sequence = "Ich habe ein Problem mit meinem Iphone das so schnell wie möglich gelöst werden muss" 

candidate_labels = ["Computer", "Handy", "Tablet", "dringend", "nicht dringend"] 

#hypothesis_template = "In diesem Satz geht es um das Thema {}."     ## Since monolingual model,its sensitive to hypothesis template. This can be experimented
#hypothesis_template = "Dieser Satz drückt ein Gefühl von {} aus."

classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)

```