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claritylab/zero-shot-explicit-gpt2

sourceHugging Facemitupdated 3y agoView on Hugging Face

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Zero-shot Explicit GPT2

This is a modified GPT2 model. It was introduced in the Findings of ACL'23 Paper Label Agnostic Pre-training for Zero-shot Text Classification by *Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang and Jason Mars*. The code for training and evaluating this model can be found here.

Model description

This model is intended for zero-shot text classification. It was trained under the generative classification framework via explicit training with the aspect-normalized UTCD dataset.

—Finetuned from model: `gpt2-medium`

Usage

Install our python package:

bash

pip install zeroshot-classifier

Then, you can use the model like this:

python

>>> import torch
>>> from zeroshot_classifier.models import ZsGPT2Tokenizer, ZsGPT2LMHeadModel

>>> training_strategy = 'explicit'
>>> model_name = f'claritylab/zero-shot-{training_strategy}-gpt2'
>>> model = ZsGPT2LMHeadModel.from_pretrained(model_name)
>>> tokenizer = ZsGPT2Tokenizer.from_pretrained(model_name, form=training_strategy)

>>> text = "I'd like to have this track onto my Classical Relaxations playlist."
>>> labels = [
>>>     'Add To Playlist', 'Book Restaurant', 'Get Weather', 'Play Music', 'Rate Book', 'Search Creative Work',
>>>     'Search Screening Event'
>>> ]

>>> inputs = tokenizer(dict(text=text, label_options=labels), mode='inference-sample')
>>> inputs = {k: torch.tensor(v).unsqueeze(0) for k, v in inputs.items()}
>>> outputs = model.generate(**inputs, max_length=128)
>>> decoded = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
>>> print(decoded)

<|question|>Which of these choices best describes the following document? : " Play Music ", " Add To Playlist ", " Rate Book ", " Get Weather ", " Book Restaurant ", " Search Screening Event ", " Search Creative Work "<|endoftext|><|text|>I'd like to have this track onto my Classical Relaxations playlist.<|endoftext|><|answer|>Play Media<|endoftext|>