Instructions to use RajuEEE/RewardModelSmallerQuestionWithTwoLabels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajuEEE/RewardModelSmallerQuestionWithTwoLabels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RajuEEE/RewardModelSmallerQuestionWithTwoLabels")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RajuEEE/RewardModelSmallerQuestionWithTwoLabels") model = AutoModelForSequenceClassification.from_pretrained("RajuEEE/RewardModelSmallerQuestionWithTwoLabels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix broken dataset reference and remove auto-generated placeholder comment
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by BananaMindBot - opened
README.md
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# RewardModelSmallerQuestionWithTwoLabels
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.6213
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- F1: 0.6909
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results: []
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# RewardModelSmallerQuestionWithTwoLabels
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unspecified dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6213
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- F1: 0.6909
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