Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use LastProgrammerStanding/tutorial_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LastProgrammerStanding/tutorial_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LastProgrammerStanding/tutorial_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LastProgrammerStanding/tutorial_model") model = AutoModelForSequenceClassification.from_pretrained("LastProgrammerStanding/tutorial_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- deb0abbb9d4a2026d2c9ad9d44ba9c02444cf730f3c5f6de6ad427c716d06fd1
- Size of remote file:
- 268 MB
- SHA256:
- 631547028130644394fb88fc75cdef0a416f56391e787823d485bbea7df3bc0b
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