Instructions to use mrwolf883/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrwolf883/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrwolf883/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrwolf883/results") model = AutoModelForSequenceClassification.from_pretrained("mrwolf883/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mrwolf883/results: direct link, hf CLI and curl.
- Browser
- Download file 1.77 kB
-
https://huggingface.co/mrwolf883/results/resolve/main/README.md
- Command line
-
hf download hf://mrwolf883/results/README.md
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curl -L -o README.md https://huggingface.co/mrwolf883/results/resolve/main/README.md
1.77 kB
| library_name: transformers | |
| base_model: UBC-NLP/MARBERT | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| 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 [UBC-NLP/MARBERT](https://huggingface.co/UBC-NLP/MARBERT) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3539 | |
| - Accuracy: 0.8538 | |
| - F1: 0.7298 | |
| - Precision: 0.8739 | |
| - Recall: 0.6265 | |
| ## 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: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | |
| | No log | 1.0 | 307 | 0.3549 | 0.8441 | 0.6974 | 0.8552 | 0.5888 | | |
| | 0.3472 | 2.0 | 614 | 0.4719 | 0.8612 | 0.7801 | 0.7551 | 0.8069 | | |
| | 0.3472 | 3.0 | 921 | 0.5533 | 0.8612 | 0.7675 | 0.7850 | 0.7508 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |