Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository. Note: this model only includes the language modeling head.
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
Intended uses & limitations
You can use the raw model for masked language modeling given an image and a piece of text with [MASK] tokens.
How to use
Here is how to use this model in PyTorch:
from transformers import ViltProcessor, ViltForMaskedLM
import requests
from PIL import Image
import re
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "a bunch of [MASK] laying on a [MASK]."
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
outputs = model(**encoding)
tl = len(re.findall("\[MASK\]", text))
inferred_token = [text]
# gradually fill in the MASK tokens, one by one
with torch.no_grad():
for i in range(tl):
encoded = processor.tokenizer(inferred_token)
input_ids = torch.tensor(encoded.input_ids).to(device)
encoded = encoded["input_ids"][0][1:-1]
outputs = model(input_ids=input_ids, pixel_values=pixel_values)
mlm_logits = outputs.logits[0] # shape (seq_len, vocab_size)
# only take into account text features (minus CLS and SEP token)
mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :]
mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1)
# only take into account text
mlm_values[torch.tensor(encoded) != 103] = 0
select = mlm_values.argmax().item()
encoded[select] = mlm_ids[select].item()
inferred_token = [processor.decode(encoded)]
selected_token = ""
encoded = processor.tokenizer(inferred_token)
processor.decode(encoded.input_ids[0], skip_special_tokens=True)
Training data
(to do)
Training procedure
Preprocessing
(to do)
Pretraining
(to do)
Evaluation results
(to do)
BibTeX entry and citation info
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
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