Ericu950
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SyllaBERTa

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 ---
 library_name: transformers
+language:
+- grc
 ---
+# SyllaBERTa: A Syllable-Based RoBERTa for Ancient Greek
+**SyllaBERTa** is an experimental Transformer-based masked language model (MLM) trained on Ancient Greek texts, tokenized at the *syllable* level.
+It is specifically designed to tackle tasks involving prosody, meter, and rhyme.
+---
+# Model Summary
+| Attribute              | Value                             |
+|:------------------------|:----------------------------------|
+| Base architecture       | RoBERTa (custom configuration)    |
+| Vocabulary size         | 42,042 syllabic tokens            |
+| Hidden size             | 768                               |
+| Number of layers        | 12                                |
+| Attention heads         | 12                                |
+| Intermediate size       | 3,072                             |
+| Max sequence length     | 514                               |
+| Pretraining objective   | Masked Language Modeling (MLM)    |
+| Optimizer               | AdamW                             |
+| Loss function           | CrossEntropy with 15% token masking probability |
+---
+The tokenizer is a custom subclass of `PreTrainedTokenizer`, operating on syllables rather than words or characters.
+It:
+- Maps each syllable to an ID.
+- Supports diphthong merging and Greek orthographic phenomena.
+- Uses space-separated syllable tokens.
+**Example tokenization:**
+Input:
+`Κατέβην χθὲς εἰς Πειραιᾶ`
+Tokens:
+`['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ']`
+> Observe that words are fused at the syllabic level.
+---
+# Usage Example
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained("your-path/SyllaBERTa", trust_remote_code=True)
+model = AutoModelForMaskedLM.from_pretrained("your-path/SyllaBERTa", trust_remote_code=True)
+# Encode a sentence
+text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
+tokens = tokenizer.tokenize(text)
+print(tokens)
+# Insert a mask at random
+import random
+tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
+masked_text = tokenizer.convert_tokens_to_string(tokens)
+# Predict masked token
+inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
+inputs.pop("token_type_ids", None)
+with torch.no_grad():
+    outputs = model(**inputs)
+# Fetch prediction
+logits = outputs.logits
+mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
+top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
+predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
+print("Top predictions:", predicted)
+```
+It should print:
+```
+Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
+Masked at position 6
+Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
+Top 5 predictions for masked token:
+ραι          (score: 23.12)
+ρα           (score: 14.69)
+ραισ         (score: 12.63)
+σαι          (score: 12.43)
+ρη           (score: 12.26)
+```
+---
+# License
+MIT License.
+---
+# Authors
+This work is part of ongoing research by **Eric Cullhed** (Uppsala University) and **Albin Thörn Cleland** (Lund University).
+---
+# Acknowledgements
+The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725.