Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Yeji-Seong/distilbert-base-uncased-textclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yeji-Seong/distilbert-base-uncased-textclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yeji-Seong/distilbert-base-uncased-textclassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Yeji-Seong/distilbert-base-uncased-textclassification") model = AutoModelForSequenceClassification.from_pretrained("Yeji-Seong/distilbert-base-uncased-textclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-textclassification
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2477
- Accuracy: 0.9286
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2267 | 1.0 | 1563 | 0.2099 | 0.9178 |
| 0.1517 | 2.0 | 3126 | 0.2477 | 0.9286 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for Yeji-Seong/distilbert-base-uncased-textclassification
Base model
distilbert/distilbert-base-uncased