Upload README.md

README.md

@@ -9,14 +9,54 @@ metrics:
 model-index:
 - name: MulderFinders
   results: []
+datasets:
+- MorcuendeA/ConspiraText-ES
+language:
+- es
 ---
 
-<!-- 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. -->
+![MulderFinders Logo](./i_want_to_belive.png)
+
+
+# MulderFinders
 
 # MulderFinders
 
-This model is a fine-tuned version of [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) on an unknown dataset.
+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
@@ -24,15 +64,28 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+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
 
-More information needed
+**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
 
-More information needed
+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
 
@@ -62,7 +115,7 @@ The following hyperparameters were used during training:
 
 ### Framework versions
 
-- Transformers 4.54.0
+- Transformers 4.53.2
 - Pytorch 2.6.0+cu124
-- Datasets 4.0.0
-- Tokenizers 0.21.2
+- Datasets 2.14.4
+- Tokenizers 0.21.2