Text Classification
Transformers
Safetensors
multilingual
xlm-roberta
document-classification
doclaynet
e5
Eval Results (legacy)
text-embeddings-inference
Instructions to use kaixkhazaki/multilingual-e5-doclaynet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaixkhazaki/multilingual-e5-doclaynet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kaixkhazaki/multilingual-e5-doclaynet")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaixkhazaki/multilingual-e5-doclaynet") model = AutoModelForSequenceClassification.from_pretrained("kaixkhazaki/multilingual-e5-doclaynet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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