Text Classification
Transformers
PyTorch
Safetensors
Portuguese
bert
reward model
alignment
preference model
RLHF
text-embeddings-inference
Instructions to use nicholasKluge/RewardModelPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicholasKluge/RewardModelPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nicholasKluge/RewardModelPT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/RewardModelPT") model = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/RewardModelPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- **Optimizer:** `torch.optim.AdamW`
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- **Learning Rate:** 5e-5
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- **GPU:** 1 NVIDIA A100-SXM4-40GB
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- **Emissions:** 0.23 KgCO2
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- **Total Energy Consumption:** 0.48 kWh
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| Step|Training Loss|Validation Loss|Accuracy|
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- **Optimizer:** `torch.optim.AdamW`
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- **Learning Rate:** 5e-5
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- **GPU:** 1 NVIDIA A100-SXM4-40GB
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- **Emissions:** 0.23 KgCO2 (Singapore)
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- **Total Energy Consumption:** 0.48 kWh
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| Step|Training Loss|Validation Loss|Accuracy|
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