Instructions to use dgalik/emoBank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dgalik/emoBank with Transformers:
# Load model directly from transformers import AutoTokenizer, DistilBertForMultiOutputRegression tokenizer = AutoTokenizer.from_pretrained("dgalik/emoBank") model = DistilBertForMultiOutputRegression.from_pretrained("dgalik/emoBank") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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# emoBank
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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### Training results
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.
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- Tokenizers 0.13.3
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# emoBank
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0999
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- Mse V: 0.1507
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- Mse A: 0.1019
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- Mse D: 0.0471
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10
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### Training results
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.3
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- Tokenizers 0.13.3
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