Text Classification
Transformers
TensorBoard
Safetensors
bert
hyundo
categorical
multi_laebl
10_class
Generated from Trainer
text-embeddings-inference
Instructions to use Kanggo/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kanggo/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kanggo/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kanggo/model_output") model = AutoModelForSequenceClassification.from_pretrained("Kanggo/model_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d454e779384fa4aa38b6555b5ccc227db5e2beea68bb1131388e6237a3fbe2b
- Size of remote file:
- 5.18 kB
- SHA256:
- d1f99c7325f82a2ec3756d6eedb81febc072e5a0121985bdc6b8de42ae564240
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