Instructions to use OliverHeine/google_mobilebert-uncased_fold_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/google_mobilebert-uncased_fold_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/google_mobilebert-uncased_fold_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/google_mobilebert-uncased_fold_3") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/google_mobilebert-uncased_fold_3") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/mobilebert-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: google_mobilebert-uncased_fold_3 | |
| 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. --> | |
| # google_mobilebert-uncased_fold_3 | |
| This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1173 | |
| - Accuracy: 0.9583 | |
| - F1: 0.9542 | |
| - Precision: 0.9601 | |
| - Recall: 0.9485 | |
| ## 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: 40 | |
| - eval_batch_size: 40 | |
| - 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 | Precision | Recall | | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | |
| | 0.1216 | 1.0 | 15481 | 0.1380 | 0.9483 | 0.9423 | 0.9645 | 0.9211 | | |
| | 0.1359 | 2.0 | 30962 | 0.1113 | 0.9568 | 0.9528 | 0.9546 | 0.9510 | | |
| | 0.0927 | 3.0 | 46443 | 0.1173 | 0.9583 | 0.9542 | 0.9601 | 0.9485 | | |
| ### Framework versions | |
| - Transformers 5.3.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.6.1 | |
| - Tokenizers 0.22.2 | |