Instructions to use dgalik/emoBank_LaBSE_test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dgalik/emoBank_LaBSE_test1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dgalik/emoBank_LaBSE_test1") model = AutoModel.from_pretrained("dgalik/emoBank_LaBSE_test1") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Error:"base_model" with value "old_models/LaBSE/0_Transformer" is not valid. Use a model id from https://hf.co/models.
emoBank_LaBSE_test1
This model is a fine-tuned version of old_models/LaBSE/0_Transformer on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0464
- Mse V: 0.0518
- Mse A: 0.0486
- Mse D: 0.0387
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse V | Mse A | Mse D |
|---|---|---|---|---|---|---|
| No log | 1.0 | 252 | 0.0639 | 0.0783 | 0.0770 | 0.0364 |
| 0.1799 | 2.0 | 504 | 0.0502 | 0.0610 | 0.0545 | 0.0350 |
| 0.1799 | 3.0 | 756 | 0.0495 | 0.0497 | 0.0465 | 0.0524 |
| 0.0606 | 4.0 | 1008 | 0.0504 | 0.0655 | 0.0449 | 0.0409 |
| 0.0606 | 5.0 | 1260 | 0.0495 | 0.0649 | 0.0467 | 0.0369 |
| 0.0473 | 6.0 | 1512 | 0.0461 | 0.0520 | 0.0470 | 0.0394 |
| 0.0473 | 7.0 | 1764 | 0.0473 | 0.0517 | 0.0508 | 0.0395 |
| 0.041 | 8.0 | 2016 | 0.0464 | 0.0518 | 0.0486 | 0.0387 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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