Instructions to use JAWCF/spladeX-ZS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JAWCF/spladeX-ZS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JAWCF/spladeX-ZS")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JAWCF/spladeX-ZS") model = AutoModelForMaskedLM.from_pretrained("JAWCF/spladeX-ZS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b068bdd86839884bcaeb8c88438a1cfbe970ad92cdb509a579018791f2fb9583
- Size of remote file:
- 542 MB
- SHA256:
- ac5a0879c10d2185a96405677280946d4c8830010e80530ea1334596666e4f4e
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