Instructions to use Decycle/simcse_longembed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Decycle/simcse_longembed with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Decycle/simcse_longembed") model = AutoModel.from_pretrained("Decycle/simcse_longembed") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| language: | |
| - en | |
| base_model: | |
| - princeton-nlp/unsup-simcse-roberta-base | |
| datasets: | |
| - dwzhu/LongEmbed | |
| pipeline_tag: sentence-similarity | |
| library_name: transformers | |
| Finetuned SimCSE with longer context size for LongEmbed tasks. |