Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use zkava01/DEBATE_Support_Jan21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zkava01/DEBATE_Support_Jan21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zkava01/DEBATE_Support_Jan21")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zkava01/DEBATE_Support_Jan21") model = AutoModelForSequenceClassification.from_pretrained("zkava01/DEBATE_Support_Jan21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
few_shot
This model is a fine-tuned version of mlburnham/Political_DEBATE_DeBERTa_large_v1.1 on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 9e-06
- train_batch_size: 1
- eval_batch_size: 16
- seed: 1
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 5
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.1+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
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