Text Classification
Transformers
Safetensors
bert
multilabel
history
holocaust
heritage
text-embeddings-inference
Instructions to use ufal/labse-malach-multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ufal/labse-malach-multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ufal/labse-malach-multilabel", device_map="auto")# 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") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
language:
- cs
- de
- en
- hu
- pl
metrics:
- f1
- precision
- recall
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- multilabel
- history
- holocaust
- heritage
base_model:
- sentence-transformers/LaBSE
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.