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--- |
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language: |
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- en |
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tags: |
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- text-classification |
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- zero-shot-classification |
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pipeline_tag: zero-shot-classification |
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library_name: transformers |
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--- |
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# deberta-v3-base-zeroshot-v1 |
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## Model description |
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The model is designed for zero-shot classification with the Hugging Face pipeline. |
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The model should be substantially better at zero-shot classification than my other zero-shot models on the |
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Hugging Face hub: https://huggingface.co/MoritzLaurer. |
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The model can do one universal task: determine whether a hypothesis is `true` or `not_true` given a text. |
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This task format is based on the Natural Language Inference task (NLI). |
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This task is so universal that any classification task can be reformulated into this true vs. false task. |
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The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format. |
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1. 26 classification tasks with ~400k texts: |
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['amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes', |
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'emotiondair', 'emocontext', 'empathetic', |
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'financialphrasebank', 'banking77', 'massive', |
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'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate', |
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'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent', |
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'agnews', 'yahootopics', |
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'trueteacher', 'spam', 'wellformedquery',] |
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2. Five NLI datasets with ~885k texts: ["mnli", "anli", "fever", "wanli", "ling"] |
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Note that compared to other NLI models, this model predicts two classes (true vs. not_true) as opposed to three classes (entailment/neutral/contradiction) |
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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/deberta-v3-base-zeroshot-v1") |
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sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the 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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### Details on data and training |
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The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main |
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## Limitations and bias |
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The model can only do text classification tasks. |
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Please consult the original DeBERTa paper and the papers for the different datasets for potential biases. |
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## Citation |
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If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k. |
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### Ideas for cooperation or questions? |
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If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) |
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### Debugging and issues |
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Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues. |
