Text Classification
Transformers
TensorBoard
Safetensors
bert
sjy
categorical
multi_label
10_class
Generated from Trainer
text-embeddings-inference
Instructions to use son1791/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use son1791/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="son1791/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("son1791/model_output") model = AutoModelForSequenceClassification.from_pretrained("son1791/model_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9fa55549b8dd89fa6bea7d5700e08e78434802209de5fb96ed29644ab3eb7a6
- Size of remote file:
- 5.18 kB
- SHA256:
- b9625437c101a60186cf3885e9a2396c62eba8825148f51a6b398276f96af3a1
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