Instructions to use Delphia/twitter-spam-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Delphia/twitter-spam-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Delphia/twitter-spam-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Delphia/twitter-spam-classifier") model = AutoModelForSequenceClassification.from_pretrained("Delphia/twitter-spam-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_params.json from Delphia/twitter-spam-classifier: direct link, hf CLI and curl.
- Browser
- Download file 867 Bytes
-
https://huggingface.co/Delphia/twitter-spam-classifier/resolve/main/training_params.json
- Command line
-
hf download hf://Delphia/twitter-spam-classifier/training_params.json
-
curl -L -o training_params.json https://huggingface.co/Delphia/twitter-spam-classifier/resolve/main/training_params.json
867 Bytes
| { | |
| "data_path": "autotrain-57208-4mv8z/autotrain-data", | |
| "model": "h-e-l-l-o/email-spam-classification-merged", | |
| "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-57208-4mv8z", | |
| "auto_find_batch_size": false, | |
| "mixed_precision": "fp16", | |
| "save_total_limit": 1, | |
| "save_strategy": "epoch", | |
| "push_to_hub": true, | |
| "repo_id": "rizdel/autotrain-57208-4mv8z", | |
| "evaluation_strategy": "epoch", | |
| "username": "rizdel", | |
| "log": "none" | |
| } |