Image-to-Text
Transformers
PyTorch
TensorBoard
English
vision-encoder-decoder
image-text-to-text
Generated from Trainer
Instructions to use DunnBC22/trocr-base-printed_captcha_ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/trocr-base-printed_captcha_ocr 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="DunnBC22/trocr-base-printed_captcha_ocr")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("DunnBC22/trocr-base-printed_captcha_ocr") model = AutoModelForMultimodalLM.from_pretrained("DunnBC22/trocr-base-printed_captcha_ocr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e6ca651e5905126d12eec96124a2422d32b9ef3d4daad82629047a21853689a
- Size of remote file:
- 1.34 GB
- SHA256:
- 973b4138a73be4853c7e89097ec3268bb979061cc288d718cf0fc758b3522118
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.