Instructions to use arbitropy/mt5-base-bcoqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbitropy/mt5-base-bcoqa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("arbitropy/mt5-base-bcoqa") model = AutoModelForMultimodalLM.from_pretrained("arbitropy/mt5-base-bcoqa") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google/mt5-base | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: mt5-base-bcoqa | |
| 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. --> | |
| # mt5-base-bcoqa | |
| This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0503 | |
| ## 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: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:-----:|:---------------:| | |
| | 1.7933 | 0.1 | 3500 | 1.4891 | | |
| | 1.594 | 0.2 | 7000 | 1.3193 | | |
| | 1.4983 | 0.3 | 10500 | 1.2550 | | |
| | 1.4311 | 0.4 | 14000 | 1.2163 | | |
| | 1.4343 | 0.5 | 17500 | 1.1723 | | |
| | 1.3635 | 0.61 | 21000 | 1.1518 | | |
| | 1.3782 | 0.71 | 24500 | 1.1331 | | |
| | 1.3782 | 0.81 | 28000 | 1.1126 | | |
| | 1.3091 | 0.91 | 31500 | 1.1197 | | |
| | 1.2328 | 1.01 | 35000 | 1.0967 | | |
| | 1.2605 | 1.11 | 38500 | 1.0892 | | |
| | 1.183 | 1.21 | 42000 | 1.0872 | | |
| | 1.1713 | 1.31 | 45500 | 1.0963 | | |
| | 1.2369 | 1.41 | 49000 | 1.0696 | | |
| | 1.2542 | 1.51 | 52500 | 1.0672 | | |
| | 1.2226 | 1.61 | 56000 | 1.0608 | | |
| | 1.2013 | 1.72 | 59500 | 1.0538 | | |
| | 1.1776 | 1.82 | 63000 | 1.0516 | | |
| | 1.191 | 1.92 | 66500 | 1.0503 | | |
| ### Framework versions | |
| - Transformers 4.39.0.dev0 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.1 | |