Instructions to use TheNewPing/mega-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheNewPing/mega-base with Transformers:
# Load model directly from transformers import AutoModelForMultipleChoice model = AutoModelForMultipleChoice.from_pretrained("TheNewPing/mega-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: mnaylor/mega-base-wikitext | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| - f1 | |
| model-index: | |
| - name: mega-base | |
| 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. --> | |
| # mega-base | |
| This model is a fine-tuned version of [mnaylor/mega-base-wikitext](https://huggingface.co/mnaylor/mega-base-wikitext) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6932 | |
| - Accuracy: 0.4934 | |
| - Precision: 0.4940 | |
| - Recall: 0.5422 | |
| - F1: 0.5170 | |
| ## 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: 1e-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: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | |
| | 0.6936 | 1.0 | 1074 | 0.6931 | 0.4987 | 0.4988 | 0.5442 | 0.5205 | | |
| | 0.6934 | 2.0 | 2148 | 0.6932 | 0.4927 | 0.4934 | 0.5422 | 0.5167 | | |
| | 0.6934 | 3.0 | 3222 | 0.6932 | 0.4934 | 0.4940 | 0.5422 | 0.5170 | | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 | |