comprehend it base EU AI Act Compliance Profile

comprehend_it-base

This is a model based on DeBERTaV3-base that was trained on natural language inference datasets as well as on multiple text classification datasets.

It demonstrates better quality on the diverse set of text classification datasets in a zero-shot setting than Bart-large-mnli while being almost 3 times smaller.

Moreover, the model can be used for multiple information extraction tasks in zero-shot setting.

Possible use cases of the model:

• Text classification
• Reranking of search results;
• Named-entity recognition;
• Relation extraction;
• Entity linking;
• Question-answering;

With the zero-shot classification pipeline

The model can be loaded with the zero-shot-classification pipeline like so:

from transformers import pipeline
classifier = pipeline("zero-shot-classification",
                      model="knowledgator/comprehend_it-base")

You can then use this pipeline to classify sequences into any of the class names you specify.

sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
#{'labels': ['travel', 'dancing', 'cooking'],
# 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],
# 'sequence': 'one day I will see the world'}

If more than one candidate label can be correct, pass multi_label=True to calculate each class independently:

candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_label=True)
#{'labels': ['travel', 'exploration', 'dancing', 'cooking'],
# 'scores': [0.9945111274719238,
#  0.9383890628814697,
#  0.0057061901316046715,
#  0.0018193122232332826],
# 'sequence': 'one day I will see the world'}

With manual PyTorch

# pose sequence as a NLI premise and la

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