Instructions to use aakothari/DeepBERTa_zinc_base_100k_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aakothari/DeepBERTa_zinc_base_100k_v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aakothari/DeepBERTa_zinc_base_100k_v4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aakothari/DeepBERTa_zinc_base_100k_v4") model = AutoModelForMaskedLM.from_pretrained("aakothari/DeepBERTa_zinc_base_100k_v4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 053ae2dc7a5b147f681eb4044f18267e2f727b2d430e18c25887769f1a0f5062
- Size of remote file:
- 5.24 kB
- SHA256:
- 84913d8d7afa83d5fb703f868416a6372b0b64a0461be2307af7c4fb915b9a8b
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