Update README.md

README.md

@@ -86,3 +86,26 @@ id2label = {idx:label for idx, label in enumerate(values)}
 model_name = "VictorYeste/deberta-based-human-value-detection"
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
 model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Performance
+
+This work proposes a system to resolve the challenge sub-tasks related to human values detection. Our approach uses cascade DeBERTa models, where the first detects the presence of each human value, and the second detects if the sentence attains or constrains the present human values in each sentence. The latter approach improves the effectiveness of the baseline at the test dataset by 4 on sub-task 1 and by 1 on sub-task 2. These models were trained on a subset of 44,758 sentences in English, validated on a subset of 14,904 sentences, and tested on a separate subset of 14,569 sentences.
+
+This model has got the third place in the subtask 1 of Human Value Detection at CLEF 2024.
+
+# Limitations and bias
+
+At the time of submission, no measures have been taken to estimate the bias embedded in the model, so it may not be safe for use in production.
+
+# License
+
+The model is released under open license CC BY 4.0, available at https://creativecommons.org/licenses/by/4.0/legalcode.
+
+# BibTeX entry and citation information
+
+@inproceedings{yeste2024philo,
+  title={Philo of Alexandria at touch{\'e}: a cascade model approach to human value detection},
+  author={Yeste, V{\'\i}ctor and Ardanuy, MC and Rosso, Paolo},
+  booktitle={Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024). CEUR Workshop Proceedings, CEUR-WS. org},
+  year={2024}
+}