Text Classification
Transformers
PyTorch
Safetensors
deberta-v2
Generated from Trainer
calibration
uncertainty
Instructions to use parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5") model = AutoModelForSequenceClassification.from_pretrained("parameterlab/apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3eb8b2c39f567d236653197ddc11f4cfa6b2a9cd4011253153cf79b9fdf4c8c0
- Size of remote file:
- 738 MB
- SHA256:
- 6fe391133c6f7afeaff79676c0f26eb1643e6fd5dca19af8e4441da169c296f8
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