Instructions to use SparseCL/BGE-SparseCL-hotpotqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseCL/BGE-SparseCL-hotpotqa with Transformers:
# Load model directly from transformers import AutoTokenizer, our_BertForCL tokenizer = AutoTokenizer.from_pretrained("SparseCL/BGE-SparseCL-hotpotqa") model = our_BertForCL.from_pretrained("SparseCL/BGE-SparseCL-hotpotqa") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 2.0, | |
| "eval_steps": 500, | |
| "global_step": 2772, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [], | |
| "logging_steps": 500, | |
| "max_steps": 4158, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "total_flos": 0, | |
| "train_batch_size": null, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |