Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use GhifSmile/textClass-finetuned-coba-coba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GhifSmile/textClass-finetuned-coba-coba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GhifSmile/textClass-finetuned-coba-coba")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GhifSmile/textClass-finetuned-coba-coba") model = AutoModelForSequenceClassification.from_pretrained("GhifSmile/textClass-finetuned-coba-coba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
textClass-finetuned-coba-coba
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4974
- Accuracy: 0.7831
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5094 | 1.0 | 2757 | 0.4658 | 0.7746 |
| 0.4474 | 2.0 | 5514 | 0.4490 | 0.7851 |
| 0.402 | 3.0 | 8271 | 0.4619 | 0.7841 |
| 0.3618 | 4.0 | 11028 | 0.4822 | 0.7831 |
| 0.334 | 5.0 | 13785 | 0.4974 | 0.7831 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Downloads last month
- 2