Instructions to use phosseini/atomic-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phosseini/atomic-bert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="phosseini/atomic-bert-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("phosseini/atomic-bert-large") model = AutoModel.from_pretrained("phosseini/atomic-bert-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 281a549d5616328faa849cb605e9a70e9042d58939d7d900ba0ab6b7ddef250c
- Size of remote file:
- 1.33 GB
- SHA256:
- cd27d9c45564c0d6ded307c6de67d0519fcdc03affc7f1252efd10733dd1abe7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.