Instructions to use dgalik/emoBank_test2_epoch20_batch16_lr1e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dgalik/emoBank_test2_epoch20_batch16_lr1e-5 with Transformers:
# Load model directly from transformers import AutoTokenizer, DistilBertForMultiOutputRegression tokenizer = AutoTokenizer.from_pretrained("dgalik/emoBank_test2_epoch20_batch16_lr1e-5") model = DistilBertForMultiOutputRegression.from_pretrained("dgalik/emoBank_test2_epoch20_batch16_lr1e-5") - Notebooks
- Google Colab
- Kaggle
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emoBank_test2_epoch20_batch16_lr1e-5
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0858
- Mse V: 0.1301
- Mse A: 0.0693
- Mse D: 0.0580
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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