Instructions to use Saran09577/saran-002-ss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Saran09577/saran-002-ss with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Saran09577/saran-002-ss", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download training_params.json from Saran09577/saran-002-ss: direct link, hf CLI and curl.
- Browser
- Download file 871 Bytes
-
https://huggingface.co/Saran09577/saran-002-ss/resolve/main/training_params.json
- Command line
-
hf download hf://Saran09577/saran-002-ss/training_params.json
-
curl -L -o training_params.json https://huggingface.co/Saran09577/saran-002-ss/resolve/main/training_params.json
871 Bytes
| { | |
| "data_path": "autotrain-7uitm-fa785/autotrain-data", | |
| "model": "cardiffnlp/twitter-roberta-base-sentiment-latest", | |
| "lr": 5e-05, | |
| "epochs": 3, | |
| "max_seq_length": 128, | |
| "batch_size": 8, | |
| "warmup_ratio": 0.1, | |
| "gradient_accumulation": 1, | |
| "optimizer": "adamw_torch", | |
| "scheduler": "linear", | |
| "weight_decay": 0.0, | |
| "max_grad_norm": 1.0, | |
| "seed": 42, | |
| "train_split": "train", | |
| "valid_split": "validation", | |
| "text_column": "autotrain_text", | |
| "target_column": "autotrain_label", | |
| "logging_steps": -1, | |
| "project_name": "autotrain-7uitm-fa785", | |
| "auto_find_batch_size": false, | |
| "mixed_precision": null, | |
| "save_total_limit": 1, | |
| "push_to_hub": true, | |
| "eval_strategy": "epoch", | |
| "username": "Saran09577", | |
| "log": "tensorboard", | |
| "early_stopping_patience": 5, | |
| "early_stopping_threshold": 0.01 | |
| } |