Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use titangmz/PNC_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use titangmz/PNC_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="titangmz/PNC_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("titangmz/PNC_test") model = AutoModelForSequenceClassification.from_pretrained("titangmz/PNC_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 112201e0c360915eefa70e397883f3a62e8d59dd1e89aaabab340492ab7b2ab0
- Size of remote file:
- 5.3 kB
- SHA256:
- de8049e2da67cc4c19a37c76a2059d5a7323aa7bf40c4e1e85736e357785a766
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