Instructions to use ogoshi2000/stance-longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ogoshi2000/stance-longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ogoshi2000/stance-longformer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ogoshi2000/stance-longformer") model = AutoModelForSequenceClassification.from_pretrained("ogoshi2000/stance-longformer") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_CHeeSEArguments": "CHeeSEArguments", | |
| "model_name_or_path": "/netscratch/schnitzler/longformer", | |
| "dataset_path": "./dataset.py", | |
| "metric_path": "./metric.py", | |
| "task": "stance_detection", | |
| "first_sentence_inputs": [ | |
| "question" | |
| ], | |
| "second_sentence_inputs": [ | |
| "title", | |
| "snippet", | |
| "paragraphs" | |
| ], | |
| "labels_to_predict": [ | |
| "stance" | |
| ], | |
| "_CHeeSETrainingArguments": "CHeeSETrainingArguments", | |
| "do_train": false, | |
| "do_eval": true, | |
| "do_predict": true, | |
| "do_cross_validation": false, | |
| "output_dir": "/netscratch/schnitzler/longformer", | |
| "logging_dir": "/netscratch/schnitzler/longformer", | |
| "save_total_limit": 3, | |
| "log_to_file": true, | |
| "logging_strategy": "steps", | |
| "logging_steps": 50, | |
| "cross_validation_folds": 5, | |
| "save_steps": 1000, | |
| "evaluation_strategy": "steps", | |
| "eval_steps": 500, | |
| "num_train_epochs": 12, | |
| "per_device_train_batch_size": 8, | |
| "per_device_eval_batch_size": 8, | |
| "learning_rate": 3e-05, | |
| "prediction_columns_to_include": [ | |
| "title", | |
| "snippet", | |
| "paragraphs", | |
| "question", | |
| "labels", | |
| "stance" | |
| ], | |
| "overwrite_output_dir": true | |
| } |