Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use hesreallyhim/distilbert-base-uncased-finetuned-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hesreallyhim/distilbert-base-uncased-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hesreallyhim/distilbert-base-uncased-finetuned-cola")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hesreallyhim/distilbert-base-uncased-finetuned-cola") model = AutoModelForSequenceClassification.from_pretrained("hesreallyhim/distilbert-base-uncased-finetuned-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-finetuned-cola
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: 0.8257
- Matthews Correlation: 0.5388
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 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 |
|---|---|---|---|---|
| 0.5135 | 1.0 | 535 | 0.4733 | 0.4270 |
| 0.3373 | 2.0 | 1070 | 0.4803 | 0.5116 |
| 0.2339 | 3.0 | 1605 | 0.6351 | 0.5195 |
| 0.1784 | 4.0 | 2140 | 0.7703 | 0.5325 |
| 0.1262 | 5.0 | 2675 | 0.8257 | 0.5388 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for hesreallyhim/distilbert-base-uncased-finetuned-cola
Base model
distilbert/distilbert-base-uncased