Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use heado/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heado/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="heado/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("heado/model_output") model = AutoModelForSequenceClassification.from_pretrained("heado/model_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b93dbc93ddc34dc59e3f3e63fbd5fd4bb767bba4e065f1763983b61358d69819
- Size of remote file:
- 5.18 kB
- SHA256:
- a816fb7b48fe8ef85f2d4fe7c4b8018534810dab99e06b4512f19ae32f69dc77
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