Instructions to use taskydata/deberta-v3-base_10xp3_10xc4_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taskydata/deberta-v3-base_10xp3_10xc4_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="taskydata/deberta-v3-base_10xp3_10xc4_512")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("taskydata/deberta-v3-base_10xp3_10xc4_512") model = AutoModelForSequenceClassification.from_pretrained("taskydata/deberta-v3-base_10xp3_10xc4_512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bc3a41489ae207442453b913669bbe23ad5fec374e73dd1cf5b0887274dc057
- Size of remote file:
- 738 MB
- SHA256:
- 6bb4d854c8bd1fab03c8f4c2ca6d6a8f3104169934c898748c30af3d06611c63
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