File size: 4,197 Bytes
78c193b 191b62e 78c193b 191b62e 78c193b 191b62e 78c193b 90c0077 78c193b 191b62e 78c193b 191b62e 9de205e 191b62e 78c193b 191b62e 78c193b 90c0077 78c193b 4602971 78c193b 191b62e 78c193b 191b62e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
---
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
# 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 |