anand095
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greek-bert-3epoch-lr-2e-5

@@ -1,199 +1,104 @@
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 ---
 library_name: transformers
+tags:
+- text-classification
+- bert
+- greek
+- ancient-text
+- multi-class-classification
+language:
+- el
+base_model: nlpaueb/bert-base-greek-uncased-v1
 ---
+# GreekBERT Fine-tuned — Ancient Text Location Classification
+Fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) for multi-class classification of ancient Greek texts by geographic provenance. Given an ancient inscription or scripture fragment, the model predicts the region or location it originated from across 15 classes.
+Built as part of the Ancient Texts Provenance Challenge (Kaggle — nppe1).
+---
 ## Model Details
+- **Model type:** BERT-based sequence classifier
+- **Base model:** nlpaueb/bert-base-greek-uncased-v1
+- **Task:** Multi-class text classification (15 classes)
+- **Language:** Ancient/Classical Greek
+- **Developed by:** Anand Kumar
+- **Training platform:** Kaggle (GPU T4/P100)
+- **Experiment tracking:** Weights & Biases (W&B)
+---
 ## Training Details
+### Dataset
+- **Source:** Ancient Texts Provenance Challenge (Kaggle — nppe1)
+- **Split:** 80/20 stratified train/test split (seed=42, stratified by label)
+- **Classes:** 15 geographic provenance labels
+- **Note:** Dataset has significant class imbalance — addressed via Macro-F1 as the primary evaluation metric
+### Preprocessing
+- Tokenized using `nlpaueb/bert-base-greek-uncased-v1` tokenizer
+- Truncated to max length of 512 tokens
+- Dynamic padding via `DataCollatorWithPadding`
+### Hyperparameters
+| Parameter | Value |
+|---|---|
+| Epochs | 5 |
+| Per-device batch size | 32 |
+| Learning rate | 5e-5 |
+| LR scheduler | Linear with warmup |
+| Warmup ratio | 0.1 |
+| Precision | fp16 mixed precision |
+| Evaluation strategy | Per epoch |
+---
 ## Evaluation
+### Metrics
+Macro-F1 was chosen as the primary metric due to class imbalance in the dataset. It evaluates performance equally across all 15 classes regardless of class frequency.
 ### Results
+| Metric | Score |
+|---|---|
+| Macro-F1 | 0.51 |
+| Accuracy | 0.66 |
+*Tracked and logged via Weights & Biases — project: nppe1, run: greek-bert*
+---
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_name = "anand095/greek-bert-5epoch-lr-5e-5-warmup"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+text = "your ancient greek text here"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    outputs = model(**inputs)
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+print(f"Predicted location class: {predicted_class}")
+```
+---
+## Limitations
+- Trained specifically on the nppe1 Kaggle dataset — performance on other ancient text corpora may vary
+- Limited to 15 predefined geographic classes from the training data
+- Model handles ancient/classical Greek text only; not suitable for modern Greek