Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use chiaying0608/demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chiaying0608/demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chiaying0608/demo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chiaying0608/demo") model = AutoModelForSequenceClassification.from_pretrained("chiaying0608/demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
demo
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: 0.4509
- Matthews Correlation: 0.5279
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.5259 | 1.0 | 535 | 0.4498 | 0.4628 |
| 0.3468 | 2.0 | 1070 | 0.4509 | 0.5279 |
| 0.2403 | 3.0 | 1605 | 0.5986 | 0.5220 |
| 0.1757 | 4.0 | 2140 | 0.7999 | 0.5111 |
| 0.1248 | 5.0 | 2675 | 0.8358 | 0.5181 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for chiaying0608/demo
Base model
distilbert/distilbert-base-uncased