Instructions to use thangvip/vi-t5-reward-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thangvip/vi-t5-reward-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thangvip/vi-t5-reward-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("thangvip/vi-t5-reward-model") model = AutoModelForSequenceClassification.from_pretrained("thangvip/vi-t5-reward-model") - Notebooks
- Google Colab
- Kaggle
vi-t5-reward-model
This model is a fine-tuned version of thangvip/vi-t5-base-finetune-rewriter-5-epochs on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5841
- Accuracy: 0.7225
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: 1.41e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for thangvip/vi-t5-reward-model
Base model
VietAI/vit5-base