Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use lengocquangLAB/Taste-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lengocquangLAB/Taste-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lengocquangLAB/Taste-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lengocquangLAB/Taste-classifier") model = AutoModelForSequenceClassification.from_pretrained("lengocquangLAB/Taste-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c2e4c3892145b401958876e9e3188f852aa8e4d558a28cce211b302a0df2ca4
- Size of remote file:
- 4.92 kB
- SHA256:
- 251cc5abdc1e6fcf9a5356d52deacd5fbc0f9e89302acaa75bd8b58c095783e6
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