---
library_name: transformers
license: apache-2.0
base_model: EuroBERT/EuroBERT-210m
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MulderFinders
  results: []
datasets:
- MorcuendeA/ConspiraText-ES
language:
- es
---

![MulderFinders Logo](./i_want_to_belive.png)


# MulderFinders

# MulderFinders

The truth is out there... and this model is here to help you find it.

**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.

Trust no one... except maybe the F1 score.


## Usage

You can use the model directly with the 🤗 Transformers library:


```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  model_name = "MorcuendeA/MulderFinders"
  
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
  
  text = "las redes 5G nos ayudan a tener mejor internet"
  
  inputs = tokenizer(text, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits
  probs = torch.softmax(logits, dim=1)  [0]
  labels = model.config.id2label
  pred = torch.argmax(probs).item()
  print(f"Prediction: {labels[pred]} ({probs[pred].item():.4f})")
  
  # Output:
  # Prediction: rational (0.9989)
```

It achieves the following results on the evaluation set:
- Loss: 0.0059
- Accuracy: 0.9981
- F1 Score: 0.9983

## Model description

Model description

**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.

## Intended uses & limitations

**Intended uses:**

- Content moderation on social media or online forums.
- Research and analysis of conspiratorial discourse in Spanish-language texts.
- Assisting fact-checking workflows by flagging potentially conspiratorial statements.

**Limitations:**

- May not handle sarcasm, irony, or ambiguous language reliably.
- Performance outside the original domain (i.e., texts similar to the training dataset) may degrade.
- May reflect biases present in the training data.
  
## Training and evaluation data

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.
During fine-tuning, regularization was applied with **attention_dropout** and **hidden_dropout** both set to 0.2.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 6

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1 Score |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
| 0.2601        | 0.3030 | 20   | 0.0532          | 0.9848   | 0.9855   |
| 0.0771        | 0.6061 | 40   | 0.0197          | 0.9981   | 0.9982   |
| 0.0271        | 0.9091 | 60   | 0.0218          | 0.9981   | 0.9982   |
| 0.0189        | 1.2121 | 80   | 0.0182          | 0.9943   | 0.9945   |
| 0.0176        | 1.5152 | 100  | 0.0093          | 0.9962   | 0.9963   |


### Framework versions

- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2