Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use TimmyOuO/TestModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TimmyOuO/TestModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TimmyOuO/TestModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TimmyOuO/TestModel") model = AutoModelForSequenceClassification.from_pretrained("TimmyOuO/TestModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
TestModel
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: 0.8087
- Matthews Correlation: 0.5481
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 |
|---|---|---|---|---|
| 0.5176 | 1.0 | 535 | 0.4607 | 0.4501 |
| 0.3476 | 2.0 | 1070 | 0.4784 | 0.5344 |
| 0.2342 | 3.0 | 1605 | 0.6180 | 0.5253 |
| 0.1706 | 4.0 | 2140 | 0.7754 | 0.5402 |
| 0.1251 | 5.0 | 2675 | 0.8087 | 0.5481 |
Framework versions
- Transformers 5.5.4
- Pytorch 2.10.0+cu128
- Datasets 2.21.0
- Tokenizers 0.22.2
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Model tree for TimmyOuO/TestModel
Base model
distilbert/distilbert-base-uncased