metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: bert-fine-tuned-cola
results: []
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: 0.8013
- Matthews Correlation: 0.5941
Using pipeline
sentence = "Hi,Not me" #sentence you want to classify
classifier = pipeline("text-classification", model= "syedmubarish/bert-fine-tuned-cola")
classifier(sentence)
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 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 |
|---|---|---|---|---|
| 0.4624 | 1.0 | 1069 | 0.4426 | 0.5394 |
| 0.3086 | 2.0 | 2138 | 0.5806 | 0.5828 |
| 0.1825 | 3.0 | 3207 | 0.8013 | 0.5941 |
Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0
- Datasets 4.1.1
- Tokenizers 0.22.1