Instructions to use Hemg/Multiple-choice-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/Multiple-choice-qa with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("Hemg/Multiple-choice-qa") model = AutoModelForMultipleChoice.from_pretrained("Hemg/Multiple-choice-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Hemg/Multiple-choice-qa: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
-
https://huggingface.co/Hemg/Multiple-choice-qa/resolve/main/README.md
- Command line
-
hf download hf://Hemg/Multiple-choice-qa/README.md
-
curl -L -o README.md https://huggingface.co/Hemg/Multiple-choice-qa/resolve/main/README.md
1.35 kB
| license: apache-2.0 | |
| base_model: bert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Multiple-choice-qa | |
| 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. --> | |
| # Multiple-choice-qa | |
| 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: 0.5306 | |
| - Accuracy: 0.7961 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.7347 | 1.0 | 2299 | 0.5306 | 0.7961 | | |
| ### Framework versions | |
| - Transformers 4.39.3 | |
| - Pytorch 2.1.2 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |