Update README.md

README.md

@@ -15,6 +15,8 @@ tags:
 model-index:
 - name: ModernBERT-base-subjectivity-english
   results: []
+datasets:
+- MatteoFasulo/clef2025_checkthat_task1_subjectivity
 ---
 
 # ModernBERT-base-subjectivity-english
@@ -52,28 +54,22 @@ The `ModernBERT-base-subjectivity-english` model was fine-tuned on the English p
 You can use this model directly with the `transformers` library for text classification:
 
 ```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-model_name = "MatteoFasulo/ModernBERT-base-subjectivity-english"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# Example text
-text = "The new policy is an absolute disaster for the economy."
-
-# Tokenize and perform inference
-inputs = tokenizer(text, return_tensors="pt")
-with torch.no_grad():
-    logits = model(**inputs).logits
-
-# Get predicted class (0 for OBJ, 1 for SUBJ as per model config)
-predicted_class_id = logits.argmax().item()
-labels = model.config.id2label # Access the label mapping from model config
-predicted_label = labels[predicted_class_id]
-
-print(f"Text: '{text}'")
-print(f"Predicted label: {predicted_label}")
+from transformers import pipeline
+
+# Load the text classification pipeline
+classifier = pipeline(
+    "text-classification",
+    model="MatteoFasulo/ModernBERT-base-subjectivity-english",
+    tokenizer="answerdotai/ModernBERT-base",
+)
+
+text1 = "The company reported a 10% increase in profits in the last quarter."
+result1 = classifier(text1)
+print(f"Text: '{text1}' Classification: {result1}")
+
+text2 = "This product is absolutely amazing and everyone should try it!"
+result2 = classifier(text2)
+print(f"Text: '{text2}' Classification: {result2}")
 ```
 
 ## Training procedure
@@ -106,4 +102,25 @@ The following hyperparameters were used during training:
 - Transformers 4.49.0
 - Pytorch 2.5.1+cu121
 - Datasets 3.3.1
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0
+
+## Code
+
+The official code and materials for this submission are available on GitHub:
+[https://github.com/MatteoFasulo/clef2025-checkthat](https://github.com/MatteoFasulo/clef2025-checkthat)
+
+## Citation
+
+If you find our work helpful or inspiring, please feel free to cite it:
+
+```bibtex
+@misc{fasulo2025aiwizardscheckthat2025,
+      title={AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles}, 
+      author={Matteo Fasulo and Luca Babboni and Luca Tedeschini},
+      year={2025},
+      eprint={2507.11764},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.11764}, 
+}
+```