Instructions to use Bingsu/clip_text_model_for_masked_lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/clip_text_model_for_masked_lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Bingsu/clip_text_model_for_masked_lm", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Bingsu/clip_text_model_for_masked_lm", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("Bingsu/clip_text_model_for_masked_lm", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
vocab.json
#1
by mpennisi - opened
Thanks for the project, is pretty interesting. I have a question:
Where vocab.json is coming from? I was thinking it was the same of 'openai/clip-vit-base-patch32' or 'clip-vit-large-patch14' but is not
It's been a while, but I'm guessing it was newly trained for this model.
mpennisi changed discussion status to closed