Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use juwon6157/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juwon6157/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="juwon6157/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("juwon6157/model_output") model = AutoModelForSequenceClassification.from_pretrained("juwon6157/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4e56f700ab95bc24c6dd369740bc1f866fd8ea7ce0babe0cf2bd014d836b967
- Size of remote file:
- 5.18 kB
- SHA256:
- 472da36d903ab0f476dde5b03153b5c292911a8e00ab870d6898e037f5f7ff13
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.