Instructions to use gramajo/nouns-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gramajo/nouns-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gramajo/nouns-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gramajo/nouns-model") model = AutoModelForSequenceClassification.from_pretrained("gramajo/nouns-model") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: nouns-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. --> | |
| # nouns-model | |
| 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: 0.6913 | |
| - Accuracy: 0.5076 | |
| - F1: 0.4327 | |
| - Precision: 0.3217 | |
| - Recall: 0.6607 | |
| ## 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: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | |
| | 0.6889 | 1.0 | 50 | 0.6913 | 0.5076 | 0.4327 | 0.3217 | 0.6607 | | |
| | 0.6419 | 2.0 | 100 | 0.6826 | 0.5381 | 0.4204 | 0.3267 | 0.5893 | | |
| | 0.5901 | 3.0 | 150 | 0.6784 | 0.5838 | 0.4225 | 0.3488 | 0.5357 | | |
| ### Framework versions | |
| - Transformers 5.12.1 | |
| - Pytorch 2.11.0+cpu | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |