Instructions to use AnonymousCS/populism_classifier_bsample_364 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_classifier_bsample_364 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_classifier_bsample_364")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_classifier_bsample_364") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_classifier_bsample_364", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: AnonymousCS/populism_english_bert_base_uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: populism_classifier_bsample_364 | |
| 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_364 | |
| This model is a fine-tuned version of [AnonymousCS/populism_english_bert_base_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_base_uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.9024 | |
| - Accuracy: 0.7466 | |
| - 1-f1: 0.2697 | |
| - 1-recall: 0.8889 | |
| - 1-precision: 0.1589 | |
| - Balanced Acc: 0.8138 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | |
| | 0.0753 | 1.0 | 8 | 1.3891 | 0.5029 | 0.1694 | 0.9630 | 0.0929 | 0.7202 | | |
| | 0.0897 | 2.0 | 16 | 0.9531 | 0.6628 | 0.2242 | 0.9259 | 0.1276 | 0.7870 | | |
| | 0.0263 | 3.0 | 24 | 0.8116 | 0.7427 | 0.2584 | 0.8519 | 0.1523 | 0.7942 | | |
| | 0.0245 | 4.0 | 32 | 1.0668 | 0.6647 | 0.2182 | 0.8889 | 0.1244 | 0.7706 | | |
| | 0.0491 | 5.0 | 40 | 0.7283 | 0.7973 | 0.3067 | 0.8519 | 0.1870 | 0.8230 | | |
| | 0.0139 | 6.0 | 48 | 0.9519 | 0.7290 | 0.2567 | 0.8889 | 0.15 | 0.8045 | | |
| | 0.0091 | 7.0 | 56 | 0.9024 | 0.7466 | 0.2697 | 0.8889 | 0.1589 | 0.8138 | | |
| ### Framework versions | |
| - Transformers 4.46.3 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 | |