Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v1
deberta-mnli
Instructions to use NDugar/v3-Large-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NDugar/v3-Large-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="NDugar/v3-Large-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NDugar/v3-Large-mnli") model = AutoModelForSequenceClassification.from_pretrained("NDugar/v3-Large-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload train_results.json
Browse files- train_results.json +8 -0
train_results.json
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{
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"epoch": 2.0,
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"train_loss": 0.2868618026395198,
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"train_runtime": 21298.9707,
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"train_samples": 392702,
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"train_samples_per_second": 36.875,
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"train_steps_per_second": 4.609
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}
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