Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use YujiK/deberta-v3-small-test_ver1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YujiK/deberta-v3-small-test_ver1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YujiK/deberta-v3-small-test_ver1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YujiK/deberta-v3-small-test_ver1") model = AutoModelForSequenceClassification.from_pretrained("YujiK/deberta-v3-small-test_ver1") - Notebooks
- Google Colab
- Kaggle
deberta-v3-small-test_ver1
This model is a fine-tuned version of microsoft/deberta-v3-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7818
- Pearson: 0.8125
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: 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson |
|---|---|---|---|---|
| 0.8486 | 1.0 | 2052 | 0.7806 | 0.7692 |
| 0.6951 | 2.0 | 4104 | 0.7546 | 0.7934 |
| 0.5971 | 3.0 | 6156 | 0.7366 | 0.8085 |
| 0.4998 | 4.0 | 8208 | 0.7407 | 0.8136 |
| 0.4407 | 5.0 | 10260 | 0.7818 | 0.8125 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
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