Instructions to use Una713/fine-tuned-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Una713/fine-tuned-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Una713/fine-tuned-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Una713/fine-tuned-encoder") model = AutoModel.from_pretrained("Una713/fine-tuned-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c18593216768a70eebd6c22a7540ced824d4526a98e7e67fbcb3251f8c433e21
- Size of remote file:
- 492 MB
- SHA256:
- f8d8d019732376dd9d146cfe322a9f433b8df09d912da64b2ba7c90b330695b2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.