Instructions to use RajuEEE/RewardModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajuEEE/RewardModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RajuEEE/RewardModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RajuEEE/RewardModel") model = AutoModelForSequenceClassification.from_pretrained("RajuEEE/RewardModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
RajuEEE/RewardModel
Browse files- README.md +7 -7
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- F1: 0.
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- Roc Auc: 0.
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- Accuracy: 0.
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1
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|:-------------:|:-----:|:----:|:---------------:|:---:|:-------:|:--------:|
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| No log | 1.0 | 258 | 0.
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### Framework versions
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6265
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- F1: 0.0207
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- Roc Auc: 0.5019
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- Accuracy: 0.0106
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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| No log | 1.0 | 258 | 0.6265 | 0.0207 | 0.5019 | 0.0106 |
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### Framework versions
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pytorch_model.bin
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training_args.bin
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