Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use queueingnctu/250822kaibin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use queueingnctu/250822kaibin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="queueingnctu/250822kaibin")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("queueingnctu/250822kaibin") model = AutoModelForSequenceClassification.from_pretrained("queueingnctu/250822kaibin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
250822kaibin
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: 0.4769
- Matthews Correlation: 0.5539
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.5173 | 1.0 | 535 | 0.4574 | 0.4594 |
| 0.3446 | 2.0 | 1070 | 0.4769 | 0.5539 |
| 0.2296 | 3.0 | 1605 | 0.6469 | 0.5222 |
| 0.1716 | 4.0 | 2140 | 0.7548 | 0.5387 |
| 0.1189 | 5.0 | 2675 | 0.8114 | 0.5377 |
Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 2.21.0
- Tokenizers 0.21.4
- Downloads last month
- 4
Model tree for queueingnctu/250822kaibin
Base model
distilbert/distilbert-base-uncased