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 +10 -8
- pytorch_model.bin +1 -1
- tokenizer.json +2 -16
- 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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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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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: 0.5976
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- F1: 0.1404
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- Roc Auc: 0.525
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- Accuracy: 0.08
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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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 | 0.6350 | 0.0 | 0.5 | 0.0 |
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| 0.6342 | 2.0 | 516 | 0.6221 | 0.1391 | 0.5225 | 0.08 |
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| 0.6342 | 3.0 | 774 | 0.5976 | 0.1404 | 0.525 | 0.08 |
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### Framework versions
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pytorch_model.bin
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tokenizer.json
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"max_length": 512,
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"strategy": "LongestFirst",
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"direction": "Right",
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"pad_type_id": 0,
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"pad_token": "[PAD]"
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"added_tokens": [
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training_args.bin
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