Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use Gladiator/microsoft-deberta-v3-large_cls_subj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gladiator/microsoft-deberta-v3-large_cls_subj with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gladiator/microsoft-deberta-v3-large_cls_subj")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gladiator/microsoft-deberta-v3-large_cls_subj") model = AutoModelForSequenceClassification.from_pretrained("Gladiator/microsoft-deberta-v3-large_cls_subj") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Update dataset YAML metadata for model
#1
by librarian-bot - opened
This is a pull request to add a dataset, SetFit/subj, to the metadata for your model (defined in the YAML block of your model's README.md).
The pull request was made by librarian-bot and used a combination of rules and/or machine learning to suggest this additional metadata.
If this suggestion is incorrect, feel free to close this pull request.
Librarian Bot was made by @davanstrien; feel free to get in touch with feedback.
Gladiator changed pull request status to merged