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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: EuroBERT/EuroBERT-210m |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: MulderFinders |
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results: [] |
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datasets: |
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- MorcuendeA/ConspiraText-ES |
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language: |
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- es |
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--- |
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# MulderFinders |
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# MulderFinders |
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The truth is out there... and this model is here to help you find it. |
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**MulderFinders** is a fine-tuned version of [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m), trained on [MorcuendeA/ConspiraText-ES](https://huggingface.co/datasets/MorcuendeA/ConspiraText-ES), a dataset full of Spanish-language conspiratorial and non-conspiratorial text. Whether it's aliens, 5G towers, or secret societies, this model is ready to classify them all. |
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Trust no one... except maybe the F1 score. |
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## Usage |
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You can use the model directly with the 🤗 Transformers library: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_name = "MorcuendeA/MulderFinders" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True) |
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text = "las redes 5G nos ayudan a tener mejor internet" |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.softmax(logits, dim=1) [0] |
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labels = model.config.id2label |
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pred = torch.argmax(probs).item() |
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print(f"Prediction: {labels[pred]} ({probs[pred].item():.4f})") |
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# Output: |
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# Prediction: rational (0.9989) |
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``` |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0059 |
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- Accuracy: 0.9981 |
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- F1 Score: 0.9983 |
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## Model description |
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Model description |
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**MulderFinders** is a Spanish-language text classification model fine-tuned to detect conspiracy-related content. It is based on [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m), a transformer model pre-trained on multiple European languages. MulderFinders performs binary classification, identifying whether a given piece of text expresses conspiratorial ideas or not. |
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## Intended uses & limitations |
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**Intended uses:** |
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- Content moderation on social media or online forums. |
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- Research and analysis of conspiratorial discourse in Spanish-language texts. |
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- Assisting fact-checking workflows by flagging potentially conspiratorial statements. |
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**Limitations:** |
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- May not handle sarcasm, irony, or ambiguous language reliably. |
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- Performance outside the original domain (i.e., texts similar to the training dataset) may degrade. |
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- May reflect biases present in the training data. |
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## Training and evaluation data |
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The model was fine-tuned using the [ConspiraText-ES](https://huggingface.co/datasets/MorcuendeA/ConspiraText-ES) dataset, which contains Spanish-language examples labeled as conspiratorial or not. The dataset includes only synthetic text samples, covering various conspiracy-related themes. |
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During fine-tuning, regularization was applied with **attention_dropout** and **hidden_dropout** both set to 0.2. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 69 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:| |
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| 0.2601 | 0.3030 | 20 | 0.0532 | 0.9848 | 0.9855 | |
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| 0.0771 | 0.6061 | 40 | 0.0197 | 0.9981 | 0.9982 | |
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| 0.0271 | 0.9091 | 60 | 0.0218 | 0.9981 | 0.9982 | |
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| 0.0189 | 1.2121 | 80 | 0.0182 | 0.9943 | 0.9945 | |
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| 0.0176 | 1.5152 | 100 | 0.0093 | 0.9962 | 0.9963 | |
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### Framework versions |
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- Transformers 4.53.2 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 2.14.4 |
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- Tokenizers 0.21.2 |
