Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Jingni/transient_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jingni/transient_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jingni/transient_data")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jingni/transient_data") model = AutoModelForSequenceClassification.from_pretrained("Jingni/transient_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a7fdd896782f528599b8467fa786b28cbd1e9af82f9615b5bc13db95a420ded
- Size of remote file:
- 4.86 kB
- SHA256:
- 3a8e31921469b2145505ad445520f1a54c40d0a7faf19ace3abe170786d1b17c
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