Image-to-Text
Transformers
PyTorch
Safetensors
vision-encoder-decoder
image-text-to-text
image-captioning
Instructions to use AIris-Channel/vit-gpt2-verifycode-caption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIris-Channel/vit-gpt2-verifycode-caption with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="AIris-Channel/vit-gpt2-verifycode-caption")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("AIris-Channel/vit-gpt2-verifycode-caption") model = AutoModelForImageTextToText.from_pretrained("AIris-Channel/vit-gpt2-verifycode-caption") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14d9c5ba47110c0ff63195422b9815316b16768eb68fff04c818206063b14323
- Size of remote file:
- 957 MB
- SHA256:
- 3e23318c753b9dbc2fc3e1691f10d3be0fe04e82037823272fe5165da924cdb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.