Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use AnonymousCS/populism_model125 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_model125 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_model125")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_model125") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_model125") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: AnonymousCS/populism_multilingual_modernbert_base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: populism_model125 | |
| 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_model125 | |
| This model is a fine-tuned version of [AnonymousCS/populism_multilingual_modernbert_base](https://huggingface.co/AnonymousCS/populism_multilingual_modernbert_base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4182 | |
| - Accuracy: 0.9588 | |
| - 1-f1: 0.5556 | |
| - 1-recall: 0.625 | |
| - 1-precision: 0.5 | |
| - Balanced Acc: 0.7991 | |
| ## 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: 64 | |
| - eval_batch_size: 64 | |
| - 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: 3 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | |
| | 0.2301 | 1.0 | 25 | 0.2704 | 0.8995 | 0.4 | 0.8125 | 0.2653 | 0.8579 | | |
| | 0.1192 | 2.0 | 50 | 0.3998 | 0.9613 | 0.5714 | 0.625 | 0.5263 | 0.8004 | | |
| | 0.0659 | 3.0 | 75 | 0.4182 | 0.9588 | 0.5556 | 0.625 | 0.5 | 0.7991 | | |
| ### Framework versions | |
| - Transformers 4.49.0.dev0 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 | |