Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified") model = AutoModelForSequenceClassification.from_pretrained("SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 475 Bytes
0f01265 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": "roberta-large",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layers_pattern": null,
"layers_to_transform": null,
"lora_alpha": 16,
"lora_dropout": 0.1,
"modules_to_save": null,
"peft_type": "LORA",
"r": 2,
"rank_pattern": {},
"revision": null,
"target_modules": [
"query",
"value"
],
"task_type": "SEQ_CLS"
} |