Update README

README.md

@@ -1,15 +1,17 @@
----
-license: cc-by-nc-4.0
-language:
-- it
-base_model:
-- gsarti/it5-large
----
+---
+license: cc-by-nc-4.0
+language:
+- it
+base_model:
+- gsarti/it5-large
+datasets:
+- cruciverb-it/evalita2026
+---
 This model card is designed for **Model 2** from the UNIBA system presented at EVALITA 2026. This version of the model is specifically optimized for Italian crossword solving by exploiting partial answer strings.
 
 ---
 
-# Model Card: UNIBA-Cruciverb-IT-Model2
+# Model Card: uniba/cruciverb-it-IT5-partial
 
 ## Model Details
 
@@ -73,8 +75,8 @@ Evaluation was performed on the official Cruciverb-IT validation and test sets u
 ```python
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-tokenizer = AutoTokenizer.from_pretrained("uniba/it5-cruciverb-model2")
-model = AutoModelForSeq2SeqLM.from_pretrained("uniba/it5-cruciverb-model2")
+tokenizer = AutoTokenizer.from_pretrained("uniba/cruciverb-it-IT5-partial")
+model = AutoModelForSeq2SeqLM.from_pretrained("uniba/cruciverb-it-IT5-partial")
 
 input_text = "Trova la soluzione dove _ indica un carattere mancante. Caratteri mancanti: 1. Lunghezza soluzione: 4. Soluzione parziale: i_ri. Indizio: Un passo indietro nel tempo"
 inputs = tokenizer(input_text, return_tensors="pt")