Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use AnonymousCS/populism_classifier_230 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_classifier_230 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_classifier_230")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_classifier_230") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_classifier_230") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_classifier_230")
model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_classifier_230")Quick Links
populism_classifier_230
This model is a fine-tuned version of AnonymousCS/populism_xlmr_large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2072
- Accuracy: 0.9581
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|---|
| 0.4109 | 1.0 | 96 | 0.2241 | 0.9581 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0126 | 2.0 | 192 | 0.2105 | 0.9581 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3376 | 3.0 | 288 | 0.2040 | 0.9581 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0187 | 4.0 | 384 | 0.1921 | 0.9581 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3191 | 5.0 | 480 | 0.2003 | 0.9581 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.173 | 6.0 | 576 | 0.2228 | 0.9581 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0172 | 7.0 | 672 | 0.2072 | 0.9581 | 0.0 | 0.0 | 0.0 | 0.5 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
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Model tree for AnonymousCS/populism_classifier_230
Base model
FacebookAI/xlm-roberta-large Finetuned
AnonymousCS/populism_xlmr_large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_classifier_230")