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
File size: 586 Bytes
978c674 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"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."
]
} |