Zero-Shot Classification
Transformers
PyTorch
English
deberta-v2
text-classification
classification
information-extraction
zero-shot
Instructions to use knowledgator/comprehend_it-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knowledgator/comprehend_it-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="knowledgator/comprehend_it-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("knowledgator/comprehend_it-base") model = AutoModelForSequenceClassification.from_pretrained("knowledgator/comprehend_it-base") - Inference
- Notebooks
- Google Colab
- Kaggle
add a remark about benchmarking
Browse files
README.md
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### Benchmarking
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| Model | IMDB | AG_NEWS | Emotions |
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| [Bart-large-mnli (407 M)](https://huggingface.co/facebook/bart-large-mnli) | 0.89 | 0.6887 | 0.3765 |
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### Benchmarking
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Below, you can see the F1 score on several text classification datasets. All tested models were not fine-tuned on those datasets and were tested in a zero-shot setting.
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| Model | IMDB | AG_NEWS | Emotions |
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| [Bart-large-mnli (407 M)](https://huggingface.co/facebook/bart-large-mnli) | 0.89 | 0.6887 | 0.3765 |
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