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
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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
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curl -L -o README.md https://huggingface.co/Hemg/Multiple-choice-qa/resolve/main/README.md
1.35 kB
metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Multiple-choice-qa
results: []
Multiple-choice-qa
This model is a fine-tuned version of 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