Modelpublic

coastalcph/danish-legal-longformer-eurlex-sd

sourceHugging Faceupdated 4y agoView on Hugging Face

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Model Card

Model description

This model is a fine-tuned version of coastalcph/danish-legal-longformer-base on the Danish part of MultiEURLEX dataset using an additional Spectral Decoupling penalty (Pezeshki et al., 2020.

Training and evaluation data

The Danish part of MultiEURLEX dataset.

Use of Model

As a text classifier:

python

from transformers import pipeline
import numpy as np

# Init text classification pipeline
text_cls_pipe = pipeline(task="text-classification",
                         model="coastalcph/danish-legal-longformer-eurlex-sd",
                         use_auth_token='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')

# Encode and Classify document
predictions = text_cls_pipe("KOMMISSIONENS BESLUTNING\naf 6. marts 2006\nom klassificering af visse byggevarers "
                            "ydeevne med hensyn til reaktion ved brand for så vidt angår trægulve samt vægpaneler "
                            "og vægbeklædning i massivt træ\n(meddelt under nummer K(2006) 655")

# Print prediction
print(predictions)
# [{'label': 'building and public works', 'score': 0.9626012444496155}]

As a feature extractor (document embedder):

python

from transformers import pipeline
import numpy as np

# Init feature extraction pipeline
feature_extraction_pipe = pipeline(task="feature-extraction",
                                   model="coastalcph/danish-legal-longformer-eurlex-sd",
                                   use_auth_token='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')

# Encode document
predictions = feature_extraction_pipe("KOMMISSIONENS BESLUTNING\naf 6. marts 2006\nom klassificering af visse byggevarers "
                                      "ydeevne med hensyn til reaktion ved brand for så vidt angår trægulve samt vægpaneler "
                                      "og vægbeklædning i massivt træ\n(meddelt under nummer K(2006) 655")

# Use CLS token representation as document embedding
document_features = token_wise_features[0][0]

print(document_features.shape)
# (768,)

Framework versions

—Transformers 4.18.0
—Pytorch 1.12.0+cu113
—Datasets 2.0.0
—Tokenizers 0.12.1