Instructions to use EN3S/NLP_assignment_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EN3S/NLP_assignment_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EN3S/NLP_assignment_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EN3S/NLP_assignment_2") model = AutoModelForSequenceClassification.from_pretrained("EN3S/NLP_assignment_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: bert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: NLP_Assignment_2 | |
| 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. --> | |
| # NLP_Assignment_2 | |
| 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: 1.0313 | |
| - Accuracy: 0.7256 | |
| ## 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: 0.0001 | |
| - train_batch_size: 64 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.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: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.6499 | 1.0 | 39 | 0.6318 | 0.6245 | | |
| | 0.5297 | 2.0 | 78 | 0.6423 | 0.6895 | | |
| | 0.3409 | 3.0 | 117 | 0.7184 | 0.6931 | | |
| | 0.1415 | 4.0 | 156 | 0.9218 | 0.7148 | | |
| | 0.0985 | 5.0 | 195 | 1.0313 | 0.7256 | | |
| ### Framework versions | |
| - Transformers 4.50.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.1 | |