Instructions to use Yanhao/simcse-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yanhao/simcse-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Yanhao/simcse-roberta-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Yanhao/simcse-roberta-large") model = AutoModel.from_pretrained("Yanhao/simcse-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from Yanhao/simcse-roberta-large: direct link, hf CLI and curl.
- Browser
- Download file 588 Bytes
-
https://huggingface.co/Yanhao/simcse-roberta-large/resolve/main/trainer_state.json
- Command line
-
hf download hf://Yanhao/simcse-roberta-large/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/Yanhao/simcse-roberta-large/resolve/main/trainer_state.json
588 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.0, | |
| "global_step": 990, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 2.53, | |
| "learning_rate": 2.474747474747475e-05, | |
| "loss": 0.861, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 5.0, | |
| "step": 990, | |
| "train_runtime": 467.303, | |
| "train_samples_per_second": 2.119 | |
| } | |
| ], | |
| "max_steps": 990, | |
| "num_train_epochs": 5, | |
| "total_flos": 4316063138500608, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |