Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use rcade/testing_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rcade/testing_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rcade/testing_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rcade/testing_model") model = AutoModelForSequenceClassification.from_pretrained("rcade/testing_model") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: bert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: testing_model | |
| 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. --> | |
| # testing_model | |
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4629 | |
| - Accuracy: 0.8358 | |
| - F1: 0.8874 | |
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 0.5682 | 1.0 | 230 | 0.4783 | 0.7990 | 0.8669 | | |
| | 0.3425 | 2.0 | 460 | 0.4264 | 0.8333 | 0.8863 | | |
| | 0.1752 | 3.0 | 690 | 0.4629 | 0.8358 | 0.8874 | | |
| ### Framework versions | |
| - Transformers 4.37.1 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.1 | |