Instructions to use Simonom/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simonom/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Simonom/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Simonom/results") model = AutoModelForSequenceClassification.from_pretrained("Simonom/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Simonom/results: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/Simonom/results/resolve/main/README.md
- Command line
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hf download hf://Simonom/results/README.md
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curl -L -o README.md https://huggingface.co/Simonom/results/resolve/main/README.md
1.47 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: bert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: results | |
| 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. --> | |
| # results | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3454 | |
| ## 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: 5e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH 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 | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.2874 | 1.0 | 3125 | 0.3921 | | |
| | 0.1575 | 2.0 | 6250 | 0.2571 | | |
| | 0.0647 | 3.0 | 9375 | 0.3454 | | |
| ### Framework versions | |
| - Transformers 4.47.1 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 | |