Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use bdpc/DeBERT_50K_steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/DeBERT_50K_steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/DeBERT_50K_steps")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/DeBERT_50K_steps") model = AutoModelForSequenceClassification.from_pretrained("bdpc/DeBERT_50K_steps") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0e75cff36e4a34a35fdea1e2f5bd3bdfb20b06d73019886652244ae757a5033f
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size 738220684
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