Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
multi-label text classification
Generated from Trainer
text-embeddings-inference
Instructions to use Jessica666/deberta_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jessica666/deberta_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jessica666/deberta_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jessica666/deberta_classifier") model = AutoModelForSequenceClassification.from_pretrained("Jessica666/deberta_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 323206153f588dc44755cdf10493a19ef2914f53aa863f9d6fd5721c6cddd0d3
- Size of remote file:
- 738 MB
- SHA256:
- fd1ee18c28c057ad39ceb3714e87f786a78d48b009e61396e5ccb1bd2c30d9e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.