Instructions to use ElMad/fun-wolf-403 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElMad/fun-wolf-403 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ElMad/fun-wolf-403")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ElMad/fun-wolf-403") model = AutoModelForSequenceClassification.from_pretrained("ElMad/fun-wolf-403", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ElMad/fun-wolf-403: direct link, hf CLI and curl.
- Browser
- Download file 3.1 kB
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https://huggingface.co/ElMad/fun-wolf-403/resolve/main/README.md
- Command line
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hf download hf://ElMad/fun-wolf-403/README.md
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curl -L -o README.md https://huggingface.co/ElMad/fun-wolf-403/resolve/main/README.md
3.1 kB
| library_name: transformers | |
| license: mit | |
| base_model: FacebookAI/roberta-base | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: fun-wolf-403 | |
| 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. --> | |
| # fun-wolf-403 | |
| This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1834 | |
| - Hamming Loss: 0.0494 | |
| - Zero One Loss: 1.0 | |
| - Jaccard Score: 1.0 | |
| - Hamming Loss Optimised: 0.0494 | |
| - Hamming Loss Threshold: 0.9000 | |
| - Zero One Loss Optimised: 1.0 | |
| - Zero One Loss Threshold: 0.9000 | |
| - Jaccard Score Optimised: 1.0 | |
| - Jaccard Score Threshold: 0.9000 | |
| ## 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: 0.0011128424281972827 | |
| - train_batch_size: 20 | |
| - eval_batch_size: 20 | |
| - seed: 2024 | |
| - 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: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Hamming Loss | Zero One Loss | Jaccard Score | Hamming Loss Optimised | Hamming Loss Threshold | Zero One Loss Optimised | Zero One Loss Threshold | Jaccard Score Optimised | Jaccard Score Threshold | | |
| |:-------------:|:-----:|:----:|:---------------:|:------------:|:-------------:|:-------------:|:----------------------:|:----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:| | |
| | No log | 1.0 | 160 | 0.1902 | 0.0497 | 1.0 | 1.0 | 0.0497 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 | | |
| | No log | 2.0 | 320 | 0.1868 | 0.0497 | 1.0 | 1.0 | 0.0497 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 | | |
| | No log | 3.0 | 480 | 0.1852 | 0.0497 | 1.0 | 1.0 | 0.0497 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 | | |
| | 0.2032 | 4.0 | 640 | 0.1842 | 0.0497 | 1.0 | 1.0 | 0.0497 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 | | |
| ### Framework versions | |
| - Transformers 4.46.2 | |
| - Pytorch 2.5.1+cu118 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 | |