Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use cerrano/myFirstModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cerrano/myFirstModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cerrano/myFirstModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cerrano/myFirstModel") model = AutoModelForSequenceClassification.from_pretrained("cerrano/myFirstModel") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - matthews_correlation | |
| model-index: | |
| - name: myFirstModel | |
| results: [] | |
| <!-- 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. --> | |
| # myFirstModel | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.8408 | |
| - Matthews Correlation: 0.5440 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | |
| | 0.5181 | 1.0 | 535 | 0.4517 | 0.4731 | | |
| | 0.3490 | 2.0 | 1070 | 0.4767 | 0.5459 | | |
| | 0.2327 | 3.0 | 1605 | 0.6519 | 0.5210 | | |
| | 0.1699 | 4.0 | 2140 | 0.7830 | 0.5423 | | |
| | 0.1243 | 5.0 | 2675 | 0.8408 | 0.5440 | | |
| ### Framework versions | |
| - Transformers 5.5.4 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.22.2 | |