Instructions to use johnny8808/test-trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johnny8808/test-trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="johnny8808/test-trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("johnny8808/test-trainer") model = AutoModelForSequenceClassification.from_pretrained("johnny8808/test-trainer") - Notebooks
- Google Colab
- Kaggle
| 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: 0.6436 | |
| - Accuracy: 0.8382 | |
| - F1: 0.8846 | |
| ## 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 adamw_torch 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | No log | 1.0 | 459 | 0.4523 | 0.8309 | 0.8761 | | |
| | 0.5648 | 2.0 | 918 | 0.4340 | 0.8284 | 0.8754 | | |
| | 0.3674 | 3.0 | 1377 | 0.6436 | 0.8382 | 0.8846 | | |
| ### Framework versions | |
| - Transformers 4.52.4 | |
| - Pytorch 2.9.0.dev20250812+cu128 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 | |