Instructions to use ancientnames/bert-fine-tuned-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ancientnames/bert-fine-tuned-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ancientnames/bert-fine-tuned-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ancientnames/bert-fine-tuned-cola") model = AutoModelForSequenceClassification.from_pretrained("ancientnames/bert-fine-tuned-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-fine-tuned-cola
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8243
- Matthews Correlation: 0.5645
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: 8
- eval_batch_size: 8
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.4633 | 1.0 | 1069 | 0.4671 | 0.4913 |
| 0.3315 | 2.0 | 2138 | 0.6088 | 0.5748 |
| 0.2079 | 3.0 | 3207 | 0.8243 | 0.5645 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2
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Model tree for ancientnames/bert-fine-tuned-cola
Base model
google-bert/bert-base-cased