Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use DipanAI/test_bug_temporary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DipanAI/test_bug_temporary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DipanAI/test_bug_temporary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DipanAI/test_bug_temporary") model = AutoModelForSequenceClassification.from_pretrained("DipanAI/test_bug_temporary") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| metrics: | |
| - matthews_correlation | |
| model-index: | |
| - name: test_bug_temporary | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: glue | |
| type: glue | |
| config: cola | |
| split: validation | |
| args: cola | |
| metrics: | |
| - name: Matthews Correlation | |
| type: matthews_correlation | |
| value: 0.5914107788502262 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # test_bug_temporary | |
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.8210 | |
| - Matthews Correlation: 0.5914 | |
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | |
| | 0.4811 | 1.0 | 1069 | 0.5683 | 0.4612 | | |
| | 0.3499 | 2.0 | 2138 | 0.5654 | 0.6041 | | |
| | 0.1871 | 3.0 | 3207 | 0.8210 | 0.5914 | | |
| ### Framework versions | |
| - Transformers 4.30.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.13.3 | |