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labse-malach-multilabel

Text Classification
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="ufal/labse-malach-multilabel")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("ufal/labse-malach-multilabel")
model = AutoModelForSequenceClassification.from_pretrained("ufal/labse-malach-multilabel", device_map="auto")
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LaBSE-Malach-Multilabel

A multilabel text classification model fine-tuned on a the Visual History Archive in 6 languages. Input text segments consisted of ~350 words on average.

Given an input string, the model predicts probablites for 2800 subject keyword IDs from the VHA ontology.

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F32
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