Update README.md

README.md

@@ -10,4 +10,191 @@ tags:
 - classification
 - NER
 - NewsArticle
----
+---
+
+# Model Description
+This model is finetuned version of [GreekBert](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1)
+
+## Dataset
+The model finetuned on the [GreekNews-20k](https://huggingface.co/datasets/katrjohn/GreekNews-20k) dataset.
+
+### Results
+
+Perfomance on the [GreekNews-20k dataset](https://huggingface.co/datasets/katrjohn/GreekNews-20k) :
+
+| Class                                           | Precision | Recall | F1-score | Support |
+|-------------------------------------------------|-----------|--------|----------|---------|
+| Αυτοκίνητο                                      | 0.87      | 0.95   | 0.90     | 201     |
+| Επιχειρήσεις και βιομηχανία                     | 0.68      | 0.76   | 0.72     | 369     |
+| Έγκλημα και δικαιοσύνη                          | 0.86      | 0.87   | 0.87     | 314     |
+| Ειδήσεις για καταστροφές και έκτακτες ανάγκες   | 0.79      | 0.71   | 0.75     | 272     |
+| Οικονομικά και χρηματοοικονομικά                | 0.78      | 0.70   | 0.73     | 495     |
+| Εκπαίδευση                                      | 0.86      | 0.83   | 0.84     | 259     |
+| Ψυχαγωγία και πολιτισμός                        | 0.68      | 0.79   | 0.73     | 251     |
+| Περιβάλλον και κλίμα                           | 0.78      | 0.65   | 0.71     | 292     |
+| Οικογένεια και σχέσεις                          | 0.80      | 0.81   | 0.81     | 294     |
+| Μόδα                                            | 0.84      | 0.91   | 0.87     | 259     |
+| Τρόφιμα και ποτά                                | 0.65      | 0.75   | 0.70     | 262     |
+| Υγεία και ιατρική                               | 0.74      | 0.64   | 0.68     | 346     |
+| Μεταφορές και υποδομές                          | 0.78      | 0.82   | 0.80     | 321     |
+| Ψυχική υγεία και ευεξία                         | 0.72      | 0.72   | 0.72     | 348     |
+| Πολιτική και κυβέρνηση                          | 0.76      | 0.68   | 0.72     | 339     |
+| Θρησκεία                                        | 0.92      | 0.87   | 0.90     | 271     |
+| Αθλητισμός                                      | 0.97      | 0.98   | 0.97     | 212     |
+| Ταξίδια και αναψυχή                             | 0.80      | 0.80   | 0.80     | 424     |
+| Τεχνολογία και επιστήμη                         | 0.65      | 0.75   | 0.70     | 308     |
+| **Accuracy**                                    |           |        | 0.78     | 5837    |
+| **Macro avg**                                   | 0.79      | 0.79   | 0.79     | 5837    |
+| **Weighted avg**                                | 0.78      | 0.78   | 0.78     | 5837    |
+
+| Class    | Precision | Recall | F1-score | Support |
+|----------|-----------|--------|----------|---------|
+| CARDINAL | 0.87      | 0.93   | 0.90     | 25656   |
+| DATE     | 0.87      | 0.90   | 0.88     | 15469   |
+| EVENT    | 0.60      | 0.59   | 0.59     | 1720    |
+| FAC      | 0.39      | 0.51   | 0.44     | 2118    |
+| GPE      | 0.88      | 0.86   | 0.87     | 16010   |
+| LOC      | 0.72      | 0.65   | 0.68     | 3547    |
+| MONEY    | 0.73      | 0.76   | 0.74     | 3882    |
+| NORP     | 0.89      | 0.84   | 0.86     | 1926    |
+| ORDINAL  | 0.92      | 0.96   | 0.94     | 3891    |
+| ORG      | 0.69      | 0.76   | 0.72     | 22184   |
+| PERCENT  | 0.72      | 0.78   | 0.75     | 7286    |
+| PERSON   | 0.79      | 0.85   | 0.82     | 16524   |
+| PRODUCT  | 0.48      | 0.48   | 0.48     | 2071    |
+| QUANTITY | 0.64      | 0.68   | 0.66     | 2588    |
+| TIME     | 0.74      | 0.76   | 0.75     | 2390    |
+| **Micro avg** | 0.78  | 0.83   | 0.81     | 127262  |
+| **Macro avg** | 0.73  | 0.75   | 0.74     | 127262  |
+| **Weighted avg** | 0.79 | 0.83 | 0.81     | 127262  |
+
+Performance on the [elNER dataset](https://github.com/nmpartzio/elNER) :
+
+| Class    | Precision | Recall | F1-score | Support |
+|----------|-----------|--------|----------|---------|
+| CARDINAL | 0.90      | 0.93   | 0.91     | 911     |
+| DATE     | 0.90      | 0.92   | 0.91     | 838     |
+| EVENT    | 0.43      | 0.46   | 0.45     | 130     |
+| FAC      | 0.34      | 0.47   | 0.40     | 77      |
+| GPE      | 0.83      | 0.90   | 0.86     | 826     |
+| LOC      | 0.70      | 0.63   | 0.66     | 178     |
+| MONEY    | 0.93      | 0.95   | 0.94     | 111     |
+| NORP     | 0.81      | 0.87   | 0.84     | 141     |
+| ORDINAL  | 0.94      | 0.92   | 0.93     | 172     |
+| ORG      | 0.74      | 0.72   | 0.73     | 1388    |
+| PERCENT  | 0.93      | 0.99   | 0.96     | 206     |
+| PERSON   | 0.84      | 0.86   | 0.85     | 1051    |
+| PRODUCT  | 0.46      | 0.41   | 0.43     | 83      |
+| QUANTITY | 0.70      | 0.75   | 0.73     | 65      |
+| TIME     | 0.88      | 0.81   | 0.84     | 137     |
+| **Micro avg** | 0.82  | 0.83   | 0.82     | 6314    |
+| **Macro avg** | 0.76  | 0.77   | 0.76     | 6314    |
+| **Weighted avg** | 0.82 | 0.83 | 0.82     | 6314    |
+
+
+
+#### To use this model 
+```
+pip install transformers, torch
+```
+
+```
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("katrjohn/TinyGreekNewsBERT", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
+```
+
+##### Example usage 
+```
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
+model = AutoModel.from_pretrained("katrjohn/TinyGreekNewsBERT", trust_remote_code=True)
+
+# Classification label dictionary (reverse)
+classification_label_dict_reverse = {
+    0: "Αυτοκίνητο", 1: "Επιχειρήσεις και βιομηχανία", 2: "Έγκλημα και δικαιοσύνη",
+    3: "Ειδήσεις για καταστροφές και έκτακτες ανάγκες", 4: "Οικονομικά και χρηματοοικονομικά", 5: "Εκπαίδευση",
+    6: "Ψυχαγωγία και πολιτισμός", 7: "Περιβάλλον και κλίμα", 8: "Οικογένεια και σχέσεις",
+    9: "Μόδα", 10: "Τρόφιμα και ποτά", 11: "Υγεία και ιατρική", 12: "Μεταφορές και υποδομές",
+    13: "Ψυχική υγεία και ευεξία", 14: "Πολιτική και κυβέρνηση", 15: "Θρησκεία",
+    16: "Αθλητισμός", 17: "Ταξίδια και αναψυχή", 18: "Τεχνολογία και επιστήμη"
+}
+
+ner_label_set = ["PAD", "O",
+    "B-ORG", "I-ORG", "B-PERSON", "I-PERSON", "B-CARDINAL", "I-CARDINAL",
+    "B-GPE", "I-GPE", "B-DATE", "I-DATE", "B-ORDINAL", "I-ORDINAL",
+    "B-PERCENT", "I-PERCENT", "B-LOC", "I-LOC", "B-NORP", "I-NORP",
+    "B-MONEY", "I-MONEY", "B-TIME", "I-TIME", "B-EVENT", "I-EVENT",
+    "B-PRODUCT", "I-PRODUCT", "B-FAC", "I-FAC", "B-QUANTITY", "I-QUANTITY"
+]
+tag2idx = {t:i for i,t in enumerate(ner_label_set)}
+idx2tag = {i:t for t,i in tag2idx.items()}
+
+sentence = "Ο Κυριάκος Μητσοτάκης επισκέφθηκε τη Θεσσαλονίκη για τα εγκαίνια της ΔΕΘ."
+inputs = tokenizer(sentence, return_tensors="pt")
+
+with torch.no_grad():
+    classification_logits, ner_logits = model(**inputs)
+
+# Classification
+classification_probs = torch.softmax(classification_logits, dim=-1)
+predicted_class = torch.argmax(classification_probs, dim=-1).item()
+predicted_class_label = classification_label_dict_reverse.get(predicted_class, "Unknown")
+
+print(f"Predicted class index: {predicted_class}")
+print(f"Predicted class label: {predicted_class_label}")
+
+# NER
+ner_predictions = torch.argmax(ner_logits, dim=-1).squeeze().tolist()
+tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'].squeeze())
+
+for token, pred_idx in zip(tokens, ner_predictions):
+    tag = idx2tag.get(pred_idx, "O")
+    if token in ["[CLS]", "[SEP]"]:
+        tag = "O"
+    print(f"{token}: {tag}")
+
+```
+
+Output:
+```
+Predicted class index: 14
+Predicted class label: Πολιτική και κυβέρνηση
+[CLS]: O
+ο: O
+κυριακος: B-PERSON
+μητσοτακης: I-PERSON
+επισκεφθηκε: O
+τη: O
+θεσσαλονικη: B-GPE
+για: O
+τα: O
+εγκαινια: O
+της: O
+δεθ: B-EVENT
+.: O
+[SEP]: O
+
+```
+
+#### Author
+This model has been released along side with the article XXX. To use this model please cite the following:
+```
+@inproceedings{XXX,
+author = {XXX},
+title = {XXX},
+year = {XXX},
+isbn = {XXX},
+publisher = {XXX},
+address = {XXX},
+url = {XXX},
+booktitle = {XXX},
+pages = {XXX},
+numpages = {XXX},
+location = {XXX},
+series = {XXX}
+}
+```