Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use xoyeop/deberta-v3-base-DIALOCONAN-CLS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xoyeop/deberta-v3-base-DIALOCONAN-CLS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xoyeop/deberta-v3-base-DIALOCONAN-CLS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xoyeop/deberta-v3-base-DIALOCONAN-CLS") model = AutoModelForSequenceClassification.from_pretrained("xoyeop/deberta-v3-base-DIALOCONAN-CLS") - Notebooks
- Google Colab
- Kaggle
deberta-v3-base-DIALOCONAN-CLS
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3523
- Precision: 0.7050
- Recall: 0.7068
- F1: 0.7057
- Accuracy: 0.9395
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4495 | 1.0 | 2500 | 0.4334 | 0.6818 | 0.6802 | 0.6797 | 0.9043 |
| 0.2925 | 2.0 | 5000 | 0.3642 | 0.6944 | 0.6959 | 0.6943 | 0.9243 |
| 0.173 | 3.0 | 7500 | 0.3523 | 0.7050 | 0.7068 | 0.7057 | 0.9395 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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