Instructions to use Cleverlytics/offres_classification_bert_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cleverlytics/offres_classification_bert_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cleverlytics/offres_classification_bert_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cleverlytics/offres_classification_bert_v1") model = AutoModelForSequenceClassification.from_pretrained("Cleverlytics/offres_classification_bert_v1") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| base_model: SI2M-Lab/DarijaBERT | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: offres_classification_bert_v1 | |
| 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. --> | |
| # offres_classification_bert_v1 | |
| This model is a fine-tuned version of [SI2M-Lab/DarijaBERT](https://huggingface.co/SI2M-Lab/DarijaBERT) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0010 | |
| ## 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: 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: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 135 | 0.0265 | | |
| | No log | 2.0 | 270 | 0.0045 | | |
| | No log | 3.0 | 405 | 0.0063 | | |
| | 0.1371 | 4.0 | 540 | 0.0023 | | |
| | 0.1371 | 5.0 | 675 | 0.0030 | | |
| | 0.1371 | 6.0 | 810 | 0.0020 | | |
| | 0.1371 | 7.0 | 945 | 0.0013 | | |
| | 0.0009 | 8.0 | 1080 | 0.0011 | | |
| | 0.0009 | 9.0 | 1215 | 0.0010 | | |
| | 0.0009 | 10.0 | 1350 | 0.0010 | | |
| ### Framework versions | |
| - Transformers 4.48.3 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.21.0 | |