Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use LogischeIP/zero-shot_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LogischeIP/zero-shot_text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LogischeIP/zero-shot_text_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LogischeIP/zero-shot_text_classification") model = AutoModelForSequenceClassification.from_pretrained("LogischeIP/zero-shot_text_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a87c32eacf48a98d3460f24608014a3c040f691dd34a9c164b2e13068ac6924
- Size of remote file:
- 738 MB
- SHA256:
- 04c8fa36c49c55cea59378dba63fa4f9c06665027cb3cc8eca3e6a7b49316c8e
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