Text Classification
Transformers
Safetensors
multilingual
xlm-roberta
document-ai
ocr
cross-page
table
text-embeddings-inference
Instructions to use lemoncoda/bertforocr-continuity-ep5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lemoncoda/bertforocr-continuity-ep5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lemoncoda/bertforocr-continuity-ep5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lemoncoda/bertforocr-continuity-ep5") model = AutoModelForSequenceClassification.from_pretrained("lemoncoda/bertforocr-continuity-ep5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a184b2b11046834b0cd331a6add8c60f316cd5fc20f545baf507182cfc22a21
- Size of remote file:
- 17.1 MB
- SHA256:
- 3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
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