Instructions to use AnonymousCS/populism_classifier_bsample_365 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_classifier_bsample_365 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_classifier_bsample_365")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_classifier_bsample_365") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_classifier_bsample_365", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: AnonymousCS/populism_english_bert_large_uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: populism_classifier_bsample_365 | |
| 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. --> | |
| # populism_classifier_bsample_365 | |
| This model is a fine-tuned version of [AnonymousCS/populism_english_bert_large_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_large_uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.8616 | |
| - Accuracy: 0.7503 | |
| - 1-f1: 0.2609 | |
| - 1-recall: 0.9233 | |
| - 1-precision: 0.1519 | |
| - Balanced Acc: 0.8325 | |
| ## 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: 1e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Use 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: 20 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | |
| | 0.1998 | 1.0 | 167 | 1.0855 | 0.4383 | 0.1436 | 0.9865 | 0.0774 | 0.6986 | | |
| | 0.1837 | 2.0 | 334 | 0.8375 | 0.5827 | 0.1811 | 0.9669 | 0.0999 | 0.7652 | | |
| | 0.2009 | 3.0 | 501 | 1.0069 | 0.5708 | 0.1747 | 0.9519 | 0.0962 | 0.7518 | | |
| | 0.1606 | 4.0 | 668 | 0.6591 | 0.7428 | 0.2474 | 0.8857 | 0.1438 | 0.8107 | | |
| | 0.195 | 5.0 | 835 | 0.7884 | 0.7467 | 0.2528 | 0.8977 | 0.1471 | 0.8184 | | |
| | 0.048 | 6.0 | 1002 | 0.8616 | 0.7503 | 0.2609 | 0.9233 | 0.1519 | 0.8325 | | |
| ### Framework versions | |
| - Transformers 4.46.3 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 | |