Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use XvKuoMing/ed_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XvKuoMing/ed_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="XvKuoMing/ed_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("XvKuoMing/ed_model") model = AutoModelForSequenceClassification.from_pretrained("XvKuoMing/ed_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 521d40ddbac5ce4ca79337e92d24c2a1a413dcccca630700548a1e0779d32a50
- Size of remote file:
- 711 MB
- SHA256:
- 7f3546915c9d0b000ba2dba60705a0795d677ab8109eeb1b41bae858a361f1de
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