Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Chi666/multiple_scores_reward_model_v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chi666/multiple_scores_reward_model_v7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chi666/multiple_scores_reward_model_v7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Chi666/multiple_scores_reward_model_v7") model = AutoModelForSequenceClassification.from_pretrained("Chi666/multiple_scores_reward_model_v7") - Notebooks
- Google Colab
- Kaggle
multiple_scores_reward_model_v7
This model is a fine-tuned version of roberta-base on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.4
- Tokenizers 0.20.3
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Model tree for Chi666/multiple_scores_reward_model_v7
Base model
FacebookAI/roberta-base