An Alternate Implementation for Zero Shot Text Classification

What is Alt-ZSC?

• Intentionally super simple yet useful if you are $ concious.
• Instead of reframing NLI/XNLI, Alt-ZSC reframes the text backbone of CLIP models to do ZSC. Perks can be, lightweight + supports more languages without trading-off accuracy, especially if you are using ZSC in a Low code or Auto* libraries or simply looking to do weak labelling.
• In some cases CLIP based Alt-ZSC models gives better accuracy than NLI/XNLI based ZSC. (Not a benchmark but from casual tests)
• Standing on the shoulder of gaints - OpenAI CLIP, Sentence-Transformers, HuggingFace Transformers,

Why Alt-ZSC can be attractive?

Installation

!pip install git+https://github.com/PrithivirajDamodaran/Alt-ZSC.git

Usage

English

from AltZSC import ZeroShotTextClassification

zstc = ZeroShotTextClassification()

preds = zstc(text="Do dogs really make better pets than cats or hamsters?",
            candidate_labels=["kittens", "hamsters", "cats", "dogs"], 
            )
            
print(preds)

'''
prints the following
Loading OpenAI CLIP model ViT-B/32 ...
Label language en ...

{'text': 'Do dogs really make better pets than cats or hamsters?', 
'scores': (0.988218, 0.011007968, 0.0007573191, 1.6704575e-05), 
'labels': ('dogs', 'cats', 'hamsters', 'kittens')}
'''

Spanish

zstc = ZeroShotTextClassification(lang="multi")

preds = zstc(text="¿Los perros realmente son mejores mascotas que los gatos o los hámsters?",
            candidate_labels=["gatita", "perros", "gatas","leonas"],
            )
            
print(preds)


'''
prints the following
Loading sentence transformer model sentence-transformers/clip-ViT-B-32-multilingual-v1 ...
Label language multi ...

{'text': '¿Los perros realmente son mejores mascotas que los gatos o los hámsters?', 
'scores': (0.99424934, 0.002653174, 0.002569642, 0.0005278819),
'labels': ('perros', 'gatas', 'gatita', 'leonas')}
'''

You can use it with over 50 languages

#View Supported lang codes
zstc.available_languages()

#Prints the following

{'ar',
 'bg',
 'ca',
 'cs',
 'da',
 'de',
 'el',
 'en',
 'es',
 'et',
 'fa',
 'fi',
 'fr',
 'fr-ca',
 'gl',
 'gu',
 'he',
 'hi',
 'hr',
 'hu',
 'hy',
 'id',
 'it',
 'ja',
 'ka',
 'ko',
 'ku',
 'lt',
 'lv',
 'mk',
 'mn',
 'mr',
 'ms',
 'my',
 'nb',
 'nl',
 'pl',
 'pt',
 'pt-br',
 'ro',
 'ru',
 'sk',
 'sl',
 'sq',
 'sr',
 'sv',
 'th',
 'tr',
 'uk',
 'ur',
 'vi',
 'zh-cn',
 'zh-tw'}

FAQ

• CLIP Text backbone has a token limit of 77, how to use Alt-ZSC with longer text ?
• The text param can be string or list of strings, for longer texts, you can split the text and pass it as a list of strings.
• Multi-lingual option covers english why use OpenAI CLIP for "en" ?
• OpenCLIP uses a Custom base Transformer with some ideas from GPT (as per the paper, see blow), but sentence-transformers/clip-ViT-B-32-multilingual-v1 uses distilbert-base-multilingual-cased. It means in practice OpenCLIP text backbone gives a better score than distilbert. For instance, trying the same english example shows the difference in scores (check the scores and label order below). While nothing stops you from using english text/labels under multi, Alt-ZSC uses OpenAI CLIP as default for the better scores it can give for english text.

The text encoder is a Transformer (Vaswani et al., 2017) with the architecture modifications described in Radford et al. (2019). As a base size we use a 63M-parameter 12- layer 512-wide model with 8 attention heads. The transformer operates on a lower-cased byte pair encoding (BPE) representation of the text with a 49,152 vocab size (Sennrich et al., 2015). For computational efficiency, the max sequence length was capped at 76. The text sequence is bracketed with [SOS] and [EOS] tokens and the activations of the highest layer of the transformer at the [EOS] token are treated as the feature representation of the text which is layer normalized and then linearly projected into the multi-modal embedding space. Masked self-attention was used in the text encoder to preserve the ability to initialize with a pre-trained language model or add language modeling as an auxiliary objective, though exploration of this is left as future work.

zstc = ZeroShotTextClassification(lang="multi")

preds = zstc(text="Do dogs really make better pets than cats or hamsters?",
            candidate_labels=["kittens", "hamsters", "cats", "dogs"], 
            )
            
print(preds)


'''
prints the following
Loading sentence transformer model sentence-transformers/clip-ViT-B-32-multilingual-v1 ...
Label language multi ...

{'text': 'Do dogs really make better pets than cats or hamsters?', 
'scores': (0.93635553, 0.06061751, 0.0016885924, 0.0013383164), 
'labels': ('dogs', 'cats', 'kittens', 'hamsters')}
'''

• If Alt-ZSC is not based on NLI/XNLI (unlike ZSC) why does it support hypothesis templates?
• Longer fluent sentences with context offer a nice prefix to the labels, it gives developers an option to squeeze better scores if needed (as per the CLIP paper, see below). So feel free to use when you see fit.

preds = zstc(text="Do dogs really make better pets than cats or hamsters?",
            candidate_labels=["kittens", "hamsters", "cats", "dogs"], 
            hypothesis_template = "This is {}"
            )

Another issue we encountered is that it’s relatively rare in our pre-training dataset for the text paired with the image to be just a single word. Usually the text is a full sentence describing the image in some way. To help bridge this distribution gap, we found that using the prompt template “A photo of a {label}.” to be a good default that helps specify the text is about the content of the image. This often improves performance over the baseline of using only the label text. For instance, just using this prompt improves accuracy on ImageNet by 1.3%.