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--- |
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language: no |
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license: CC-BY 4.0 |
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thumbnail: https://raw.githubusercontent.com/NBAiLab/notram/master/images/nblogo_2.png |
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tags: |
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- nb-bert |
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- text-classification |
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- pytorch |
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- tensorflow |
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- norwegian |
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- bert |
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datasets: |
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- mnli |
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- multi_nli |
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- xnli |
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pipeline_tag: zero-shot-classification |
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widget: |
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- text: "Folkehelseinstituttets mest optimistiske anslag er at alle over 18 år er ferdigvaksinert innen midten av september." |
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candidate_labels: "politikk, helse, sport, religion" |
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hypothesis_template: "Denne teksten handler om {}." |
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multi_class: true |
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--- |
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**Release 1.0** (March 11, 2021) |
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# NB-Bert base model finetuned on Norwegian machine translated MNLI |
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## Description |
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The most effective way of creating a good classifier is to finetune it for this specific task. However, in many cases this is simply impossible. |
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[Yin et al.](https://arxiv.org/abs/1909.00161) has proposed a very clever way of using pre-trained MNLI model as a zero-shot sequence classifiers. The methods works by reformulating the question to an MNLI hypothesis. If we want to figure out if a text is about "sport", we simply state that "This text is about sport" ("Denne teksten handler om sport"). |
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When the model is finetuned on the 400k large MNLI task, it is in many cases able to solve this classification tasks. There are no MNLI-set of this size in Norwegian but we have trained it on a machine translated version of the original MNLI-set. |
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## Hugging Face zero-shot-classification pipeline |
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The easiest way to try this out is using the Hugging Face pipeline. Please note that you will improve the results by overriding the English hypothesis template. |
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```python |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", model="NBAiLab/nb-bert-base-mnli") |
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``` |
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You can then use this pipeline to classify sequences into any of the class names you specify. |
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```python |
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sequence_to_classify = 'Folkehelseinstituttets mest optimistiske anslag er at alle over 18 år er ferdigvaksinert innen midten av september.' |
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candidate_labels = ['politikk, helse, sport, religion'] |
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hypothesis_template = 'Denne teksten handler om {}.' |
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classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True) |
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# {'labels': ['helse', 'politikk', 'sport', 'religion'], |
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# 'scores': [0.4210019111633301, 0.0674605593085289, 0.000840459018945694, 0.0007541406666859984], |
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# 'sequence': 'Folkehelseinstituttets mest optimistiske anslag er at alle over 18 år er ferdigvaksinert innen midten av september.'} |
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``` |
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## More information |
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For more information on the model, see |
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https://github.com/NBAiLab/notram |
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Here you will also find a Colab explaining more in details how to use the zero-shot-classification pipeline. |
