Instructions to use guyhadad01/E5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guyhadad01/E5-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="guyhadad01/E5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("guyhadad01/E5-small") model = AutoModelForMaskedLM.from_pretrained("guyhadad01/E5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ad8ecc4dd95822829b9498a3dc12e3ee267bbf229091ebd32b316d016653fd5
- Size of remote file:
- 472 MB
- SHA256:
- f7c9c6832d9b9ae653131500755efe8459dc2939032a1a72e8b52c63f0712a9c
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