Instructions to use Ryanliii/test-trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ryanliii/test-trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ryanliii/test-trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ryanliii/test-trainer") model = AutoModelForSequenceClassification.from_pretrained("Ryanliii/test-trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ryanliii/test-trainer: direct link, hf CLI and curl.
- Browser
- Download file 1.64 kB
-
https://huggingface.co/Ryanliii/test-trainer/resolve/main/README.md
- Command line
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hf download hf://Ryanliii/test-trainer/README.md
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curl -L -o README.md https://huggingface.co/Ryanliii/test-trainer/resolve/main/README.md
1.64 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: bert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: test-trainer | |
| 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. --> | |
| # test-trainer | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6045 | |
| - Accuracy: 0.88 | |
| - F1: 0.8947 | |
| ## 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: 5e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - 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: 3.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 0.4129 | 1.0 | 500 | 0.4443 | 0.86 | 0.8704 | | |
| | 0.1950 | 2.0 | 1000 | 0.4386 | 0.91 | 0.9204 | | |
| | 0.0672 | 3.0 | 1500 | 0.6045 | 0.88 | 0.8947 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |