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
Download adapter_config.json from SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified: direct link, hf CLI and curl.
- Browser
- Download file 475 Bytes
-
https://huggingface.co/SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified/resolve/main/adapter_config.json
- Command line
-
hf download hf://SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified/resolve/main/adapter_config.json
475 Bytes
| { | |
| "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" | |
| } |