Instructions to use Mahmoud22/All-Train-data-F1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud22/All-Train-data-F1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud22/All-Train-data-F1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud22/All-Train-data-F1") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud22/All-Train-data-F1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: res | |
| 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. --> | |
| # res | |
| This model is a fine-tuned version of [salti/AraElectra-base-finetuned-ARCD](https://huggingface.co/salti/AraElectra-base-finetuned-ARCD) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1732 | |
| - F1-macro: 0.6754 | |
| ## 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: 8e-05 | |
| - train_batch_size: 40 | |
| - eval_batch_size: 80 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 7 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | F1-macro | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 229 | 0.5144 | 0.2375 | | |
| | No log | 2.0 | 458 | 0.2974 | 0.3838 | | |
| | 0.7175 | 3.0 | 687 | 0.2268 | 0.6071 | | |
| | 0.7175 | 4.0 | 916 | 0.2206 | 0.5803 | | |
| | 0.2536 | 5.0 | 1145 | 0.1816 | 0.6773 | | |
| | 0.2536 | 6.0 | 1374 | 0.1713 | 0.6893 | | |
| | 0.1932 | 7.0 | 1603 | 0.1695 | 0.6832 | | |
| ### Framework versions | |
| - Transformers 4.26.1 | |
| - Pytorch 1.13.0 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.13.2 | |