Instructions to use leomaurodesenv/bert-base-uncased-answerable-or-not-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/bert-base-uncased-answerable-or-not-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leomaurodesenv/bert-base-uncased-answerable-or-not-augmented")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/bert-base-uncased-answerable-or-not-augmented") model = AutoModelForSequenceClassification.from_pretrained("leomaurodesenv/bert-base-uncased-answerable-or-not-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google-bert/bert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: bert-base-uncased-answerable-or-not-augmented | |
| 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. --> | |
| # bert-base-uncased-answerable-or-not-augmented | |
| This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2582 | |
| - Accuracy: 0.9188 | |
| ## 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: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 50 | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.2501 | 1.0 | 1367 | 0.3223 | 0.8836 | | |
| | 0.3057 | 2.0 | 2734 | 0.2581 | 0.9188 | | |
| | 0.2221 | 3.0 | 4101 | 0.3245 | 0.9202 | | |
| | 0.0712 | 4.0 | 5468 | 0.3585 | 0.9243 | | |
| | 0.0175 | 5.0 | 6835 | 0.3699 | 0.9319 | | |
| ### Framework versions | |
| - Transformers 5.2.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.5.0 | |
| - Tokenizers 0.22.2 | |