Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
ml-intern
text-embeddings-inference
Instructions to use narcolepticchicken/patch-reward-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use narcolepticchicken/patch-reward-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="narcolepticchicken/patch-reward-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("narcolepticchicken/patch-reward-model") model = AutoModelForSequenceClassification.from_pretrained("narcolepticchicken/patch-reward-model") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| - ml-intern | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: patch-reward-model | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # patch-reward-model | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4385 | |
| - Accuracy: 1.0 | |
| - F1: 1.0 | |
| - Auc: 1.0 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:---:| | |
| | 0.6845 | 1.0 | 10 | 0.6395 | 0.6 | 0.75 | 1.0 | | |
| | 0.5908 | 2.0 | 20 | 0.4385 | 1.0 | 1.0 | 1.0 | | |
| | 0.4235 | 3.0 | 30 | 0.3320 | 1.0 | 1.0 | 1.0 | | |
| ### Framework versions | |
| - Transformers 5.8.0 | |
| - Pytorch 2.11.0+cu130 | |
| - Datasets 4.8.5 | |
| - Tokenizers 0.22.2 | |
| <!-- ml-intern-provenance --> | |
| ## Generated by ML Intern | |
| This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. | |
| - Try ML Intern: https://smolagents-ml-intern.hf.space | |
| - Source code: https://github.com/huggingface/ml-intern | |
| ## Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_id = 'narcolepticchicken/patch-reward-model' | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| ``` | |
| For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class. | |