Text Classification
Transformers
TensorBoard
Safetensors
bert
gjg
categorical
multi_label
10_class
Generated from Trainer
text-embeddings-inference
Instructions to use pongDang/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pongDang/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pongDang/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pongDang/model_output") model = AutoModelForSequenceClassification.from_pretrained("pongDang/model_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ad24074fbccfd766aff4ff884764ec2e79887e05d0a503769bc61ddd8f915d1
- Size of remote file:
- 5.18 kB
- SHA256:
- 5e55c5e0ab49932e96cfdf1e6fd7b7587a3eec2bb68a92c416c6d794970fb4a6
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