Create README.md

README.md

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+---
+license: mit
+language:
+- tr
+metrics:
+- rouge
+- meteor
+base_model:
+- google/umt5-small
+pipeline_tag: text2text-generation
+---
+
+# 📝 umt5-small Turkish Abstractive Summarization
+
+## 🧠 Abstract
+
+This model presents a fine-tuned version of `umt5-small`, specifically adapted for **abstractive summarization** of Turkish-language texts. Leveraging the multilingual capabilities of the original mT5 architecture, the model has been trained on a high-quality Turkish summarization dataset containing diverse news articles and their human-written summaries. The goal of this model is to generate coherent, concise, and semantically accurate summaries from long-form Turkish content, making it suitable for real-world applications such as news aggregation, document compression, and information retrieval.
+
+Despite its small size (~60M parameters), the model demonstrates strong performance across standard evaluation metrics including **ROUGE** and **METEOR**, achieving results within the commonly accepted thresholds for Turkish-language summarization tasks. It strikes a practical balance between efficiency and quality, making it ideal for use in resource-constrained environments.
+
+---
+
+## 🔍 Metric Interpretation (Specific to Turkish)
+
+- **ROUGE-1:** Measures unigram (word-level) overlap between the generated summary and the reference text. For Turkish summarization tasks, scores below **0.30** generally indicate weak lexical alignment, while scores above **0.40** are considered strong and fluent outputs.
+
+- **ROUGE-2:** Evaluates bigram (two-word sequence) overlap. Since Turkish is an agglutinative language with rich morphology, achieving high bigram overlap is more difficult. Therefore, a range between **0.15–0.30** is considered average and acceptable for Turkish.
+
+- **ROUGE-L:** Captures the longest common subsequence, reflecting sentence-level fluency and structure similarity. Acceptable ranges for Turkish are generally close to ROUGE-1, typically between **0.28–0.40**.
+
+- **METEOR:** Unlike ROUGE, METEOR also incorporates semantic similarity and synonymy. It performs relatively well on morphologically rich languages like Turkish. Scores in the range of **0.25–0.38** are commonly observed and considered good in Turkish summarization settings.
+
+---
+
+## 📊 Acceptable Metric Ranges
+
+| Metric   | Acceptable Range | Interpretation                    |
+|----------|------------------|-----------------------------------|
+| ROUGE-1  | 0.30 – 0.45      | Weak < 0.30, Good > 0.40          |
+| ROUGE-2  | 0.15 – 0.30      | Typical for bigram-level          |
+| ROUGE-L  | 0.28 – 0.40      | Similar to ROUGE-1                |
+| METEOR   | 0.25 – 0.38      | Balanced lexical & semantic match |
+
+---
+
+## 🚀 Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("your_username/umt5-small-turkish-summary")
+model = AutoModelForSeq2SeqLM.from_pretrained("your_username/umt5-small-turkish-summary")
+
+text = "Insert Turkish text to summarize."
+inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+
+summary_ids = model.generate(
+    **inputs,
+    max_length=100,
+    num_beams=4,
+    early_stopping=True
+)
+
+summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+print(summary)
+