Instructions to use dgalik/emoBank_test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dgalik/emoBank_test2 with Transformers:
# Load model directly from transformers import AutoTokenizer, DistilBertForMultiOutputRegression tokenizer = AutoTokenizer.from_pretrained("dgalik/emoBank_test2") model = DistilBertForMultiOutputRegression.from_pretrained("dgalik/emoBank_test2") - Notebooks
- Google Colab
- Kaggle
| base_model: '' | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: emoBank_test2 | |
| 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. --> | |
| # emoBank_test2 | |
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0791 | |
| - Mse V: 0.1307 | |
| - Mse A: 0.0598 | |
| - Mse D: 0.0469 | |
| ## 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: 2e-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: 10 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.31.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.3 | |
| - Tokenizers 0.13.3 | |