Text Classification
Transformers
Safetensors
longformer
reward-model
reranking
literary-style
faithfulness
Instructions to use 3rd-Degree-Burn/LongformerRM-Unison with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3rd-Degree-Burn/LongformerRM-Unison with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="3rd-Degree-Burn/LongformerRM-Unison")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("3rd-Degree-Burn/LongformerRM-Unison") model = AutoModelForSequenceClassification.from_pretrained("3rd-Degree-Burn/LongformerRM-Unison") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "absolute_multihead_reward_model", | |
| "base_model": "./absolute-multitask-reward-model", | |
| "num_labels": 3, | |
| "label_order": [ | |
| "style", | |
| "faith", | |
| "identifier" | |
| ], | |
| "max_length": 1024, | |
| "recommended_overall_formula": "style_score * (0.5 * faith_score + 0.5 * identifier_score) * (identifier_score ** 1.5)", | |
| "notes": [ | |
| "Apply sigmoid to each logit before using scores.", | |
| "Do not use style_score alone for reranking.", | |
| "Keep input formatting consistent with training.", | |
| "For Longformer inference, set global_attention_mask[:, 0] = 1." | |
| ] | |
| } |