Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
trl
reward-trainer
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("MahmoudMohamed/Reward_Model")
model = AutoModelForSequenceClassification.from_pretrained("MahmoudMohamed/Reward_Model")Quick Links
Reward_model
This model is a fine-tuned version of OpenAssistant/reward-model-deberta-v3-base on Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:
- Loss: 0.6931
- Accuracy: 1.0
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6936 | 1.0 | 13400 | 0.6931 | 1.0 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for MahmoudMohamed/Reward_Model
Base model
OpenAssistant/reward-model-deberta-v3-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MahmoudMohamed/Reward_Model")