Instructions to use BenjaminTT/NLPGroupProject-Finetune-Funnel-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenjaminTT/NLPGroupProject-Finetune-Funnel-Transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("BenjaminTT/NLPGroupProject-Finetune-Funnel-Transformer") model = AutoModelForMultipleChoice.from_pretrained("BenjaminTT/NLPGroupProject-Finetune-Funnel-Transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: funnel-transformer/intermediate-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: NLPGroupProject-Finetune-Funnel-Transformer | |
| 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. --> | |
| # NLPGroupProject-Finetune-Funnel-Transformer | |
| This model is a fine-tuned version of [funnel-transformer/intermediate-base](https://huggingface.co/funnel-transformer/intermediate-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.3863 | |
| - Accuracy: 0.263 | |
| ## 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: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 1.4073 | 0.25 | 500 | 1.3863 | 0.262 | | |
| | 1.403 | 0.5 | 1000 | 1.3863 | 0.275 | | |
| | 1.4031 | 0.75 | 1500 | 1.3863 | 0.263 | | |
| | 1.4035 | 1.0 | 2000 | 1.3863 | 0.259 | | |
| | 1.3984 | 1.25 | 2500 | 1.3863 | 0.283 | | |
| | 1.3904 | 1.5 | 3000 | 1.3863 | 0.263 | | |
| | 1.3977 | 1.75 | 3500 | 1.3863 | 0.252 | | |
| | 1.3949 | 2.0 | 4000 | 1.3863 | 0.272 | | |
| | 1.3979 | 2.25 | 4500 | 1.3863 | 0.258 | | |
| | 1.3965 | 2.5 | 5000 | 1.3863 | 0.225 | | |
| | 1.3944 | 2.75 | 5500 | 1.3863 | 0.246 | | |
| | 1.3999 | 3.0 | 6000 | 1.3863 | 0.263 | | |
| ### Framework versions | |
| - Transformers 4.40.0 | |
| - Pytorch 2.2.2+cu118 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 | |