| --- |
| language: "id" |
| license: "apache-2.0" |
| tags: |
| - sentence-transformers |
| - indonesian |
| - semantic-similarity |
| - stsb |
| - embedding |
| - fine-tuned |
| - education |
| datasets: |
| - rzkamalia/stsb-indo-mt-modified |
| - quarkss/stsb-indo-mt |
| - AkshitaS/semrel_2024_plus |
| metrics: |
| - cosine-similarity |
| pipeline_tag: sentence-similarity |
| model-index: |
| - name: Automatic Scoring (IndoBERT STS) |
| results: |
| - task: |
| name: Semantic Textual Similarity |
| type: sentence-similarity |
| dataset: |
| name: STSB Indo + SemRel 2024 |
| type: multiple |
| metrics: |
| - name: Cosine Similarity |
| type: cosine-similarity |
| value: Evaluated on test set (see below) |
| --- |
| |
| # Automatic Scoring for Indonesian Semantic Similarity ✨ |
|
|
| Model ini merupakan hasil fine-tuning dari [`indobenchmark/indobert-large-p2`](https://huggingface.co/indobenchmark/indobert-large-p2) menggunakan Sentence Transformers untuk tugas **Semantic Textual Similarity** (STS) dalam bahasa Indonesia. |
|
|
| Model ini dilatih secara **multi-dataset** menggunakan gabungan dari: |
| - 🟢 `rzkamalia/stsb-indo-mt-modified` |
| - 🟢 `quarkss/stsb-indo-mt` |
| - 🟢 `AkshitaS/semrel_2024_plus` (split `ind_Latn`) |
|
|
| Tujuan utama dari model ini adalah untuk mendukung **penilaian otomatis jawaban siswa** atau sistem pembelajaran berbasis teks dalam bahasa Indonesia. |
|
|
| ## 🧠 Model Details |
|
|
| - **Base Model**: [`indobenchmark/indobert-large-p2`](https://huggingface.co/indobenchmark/indobert-large-p2) |
| - **Framework**: `sentence-transformers` |
| - **Loss Function**: `CosineSimilarityLoss` |
| - **Training Epochs**: `5` |
| - **Batch Size**: `16` |
| - **Evaluation Metric**: `Cosine Similarity` |
| - **Total Datasets Combined**: 3 corpora (STS Indo + Semantic Relation) |
|
|
| ## 📊 Example Usage |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer, util |
| |
| model = SentenceTransformer("eugene702/Automatic-Scoring") |
| |
| score = util.cos_sim( |
| model.encode("Apa dampak pemanasan global?", convert_to_tensor=True), |
| model.encode("Bagaimana pengaruh perubahan iklim terhadap bumi?", convert_to_tensor=True) |
| ) |
| |
| print("Similarity Score:", score.item()) |
| ``` |
|
|
| ## 📁 Datasets Used |
|
|
| | Dataset | Deskripsi | |
| |--------|-----------| |
| | [`rzkamalia/stsb-indo-mt-modified`](https://huggingface.co/datasets/rzkamalia/stsb-indo-mt-modified) | Versi modifikasi STS bahasa Indonesia | |
| | [`quarkss/stsb-indo-mt`](https://huggingface.co/datasets/quarkss/stsb-indo-mt) | STS benchmark bahasa Indonesia | |
| | [`AkshitaS/semrel_2024_plus`](https://huggingface.co/datasets/AkshitaS/semrel_2024_plus) | Dataset Semantic Relation multilingual split `ind_Latn` | |
|
|
| ## 📈 Evaluation |
|
|
| Evaluasi dilakukan pada data `test` dari ketiga dataset yang digabung. Penilaian dilakukan menggunakan `EmbeddingSimilarityEvaluator` dari `sentence-transformers`. |
|
|
| **Metric utama**: *Cosine Similarity* terhadap pasangan kalimat dalam bahasa Indonesia. |
|
|
| ## 💡 Use Cases |
|
|
| - Penilaian otomatis jawaban siswa |
| - Deteksi parafrase dalam Bahasa Indonesia |
| - Penilaian kesamaan kalimat untuk e-learning |
| - Analisis pertanyaan dan jawaban semantik |
|
|
| ## 🛠 Training Code |
|
|
| Model dilatih menggunakan `sentence-transformers` di platform Kaggle. |
| Kode pelatihan tersedia secara privat namun dapat diminta melalui email. |
|
|
| ## 📌 Model Availability |
|
|
| Model tersedia di: |
| - Hugging Face: [eugene702/Automatic-Scoring](https://huggingface.co/eugene702/Automatic-Scoring) |
| - Kaggle Model Hub: [Automatic Scoring](https://www.kaggle.com/models/eugene702/automatic-scoring) |
|
|
| ## 📬 Contact |
|
|
| Untuk pertanyaan atau kolaborasi: |
|
|
| **Eugene Feilian Putra Rangga** |
| 📧 eugenefeilianputrarangga@gmail.com |
| 🔗 [Hugging Face Profile](https://huggingface.co/eugene702) |
| 🔗 [GitHub](https://github.com/Eugene702) |
|
|
| --- |
|
|
| > _Model ini merupakan bagian dari eksperimen untuk membangun sistem penilaian otomatis berbasis semantic similarity pada teks Bahasa Indonesia._ |
