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--- |
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base_model: indobenchmark/indobert-base-p2 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:133472 |
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- loss:SoftmaxLoss |
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widget: |
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- source_sentence: Dua tim anak-anak, yang satu berwarna hijau dan yang lainnya berwarna |
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merah, bermain bersama dalam permainan Rugby saat hujan. |
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sentences: |
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- Tiga orang berada di dalam perahu. |
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- seorang pria di atas sepeda |
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- Tim rugby anak-anak, merah versus hijau bermain di tengah hujan. |
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- source_sentence: Seorang pria melakukan perawatan di rel kereta api |
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sentences: |
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- Dua orang terlibat dalam percakapan. |
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- Ada seorang wanita melakukan pekerjaan di rel kereta api. |
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- orang-orang duduk di bar |
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- source_sentence: Sepasang suami istri dengan pakaian renang berjalan di pantai. |
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sentences: |
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- pasangan itu duduk di dalam |
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- Pria itu sedang makan. |
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- Dua orang sedang berpose untuk difoto. |
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- source_sentence: Dua orang sedang duduk di samping api unggun bertumpuk kayu di |
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malam hari. |
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sentences: |
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- Seseorang memegang jeruk dan berjalan |
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- Orang-orang duduk di luar di malam hari. |
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- Orang-orang berada di luar. |
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- source_sentence: Wanita profesional di meja pendaftaran acara sementara pria berjas |
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melihat. |
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sentences: |
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- Orang-orang berkumpul untuk sebuah acara. |
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- Seorang wanita sedang berjalan menuju taman. |
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- Ada seorang anak yang tersenyum untuk difoto. |
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model-index: |
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- name: SentenceTransformer based on indobenchmark/indobert-base-p2 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.23146247451934734 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.23182555096720683 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.19847600869622337 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.2038189662328075 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.198744291061789 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.20385658228775938 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.2561502821889763 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.25101474046220823 |
|
name: Spearman Dot |
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- type: pearson_max |
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value: 0.2561502821889763 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.25101474046220823 |
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name: Spearman Max |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test |
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type: sts-test |
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metrics: |
|
- type: pearson_cosine |
|
value: 0.5914831439397401 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.5978838704506128 |
|
name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.5131648451956073 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.5147175261736068 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.5942850778734059 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
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value: 0.6001963453484881 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.5880400881430983 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5933998114680769 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.5942850778734059 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6001963453484881 |
|
name: Spearman Max |
|
--- |
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|
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# SentenceTransformer based on indobenchmark/indobert-base-p2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("cassador/indobert-snli-v1") |
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# Run inference |
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sentences = [ |
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'Wanita profesional di meja pendaftaran acara sementara pria berjas melihat.', |
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'Orang-orang berkumpul untuk sebuah acara.', |
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'Ada seorang anak yang tersenyum untuk difoto.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `sts-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.2315 | |
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| **spearman_cosine** | **0.2318** | |
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| pearson_manhattan | 0.1985 | |
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| spearman_manhattan | 0.2038 | |
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| pearson_euclidean | 0.1987 | |
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| spearman_euclidean | 0.2039 | |
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| pearson_dot | 0.2562 | |
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| spearman_dot | 0.251 | |
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| pearson_max | 0.2562 | |
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| spearman_max | 0.251 | |
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|
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#### Semantic Similarity |
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* Dataset: `sts-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.5915 | |
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| **spearman_cosine** | **0.5979** | |
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| pearson_manhattan | 0.5132 | |
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| spearman_manhattan | 0.5147 | |
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| pearson_euclidean | 0.5943 | |
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| spearman_euclidean | 0.6002 | |
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| pearson_dot | 0.588 | |
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| spearman_dot | 0.5934 | |
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| pearson_max | 0.5943 | |
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| spearman_max | 0.6002 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 133,472 training samples |
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* Columns: <code>label</code>, <code>kalimat1</code>, and <code>kalimat2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | label | kalimat1 | kalimat2 | |
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|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | int | string | string | |
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| details | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.47 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.62 tokens</li><li>max: 22 tokens</li></ul> | |
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* Samples: |
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| label | kalimat1 | kalimat2 | |
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|:---------------|:------------------------------------------------------------------|:----------------------------------------------------------------| |
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| <code>0</code> | <code>Seseorang di atas kuda melompati pesawat yang rusak.</code> | <code>Seseorang sedang makan malam, memesan telur dadar.</code> | |
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| <code>1</code> | <code>Seseorang di atas kuda melompati pesawat yang rusak.</code> | <code>Seseorang berada di luar ruangan, di atas kuda.</code> | |
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| <code>1</code> | <code>Anak-anak tersenyum dan melambai ke kamera</code> | <code>Ada anak-anak yang hadir</code> | |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 6,607 evaluation samples |
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* Columns: <code>label</code>, <code>kalimat1</code>, and <code>kalimat2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | label | kalimat1 | kalimat2 | |
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|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | int | string | string | |
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| details | <ul><li>0: ~50.10%</li><li>1: ~49.90%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.87 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.45 tokens</li><li>max: 27 tokens</li></ul> | |
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* Samples: |
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| label | kalimat1 | kalimat2 | |
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|:---------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------| |
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| <code>1</code> | <code>Dua wanita berpelukan sambil memegang paket untuk pergi.</code> | <code>Dua wanita memegang paket.</code> | |
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| <code>0</code> | <code>Dua wanita berpelukan sambil memegang paket untuk pergi.</code> | <code>Orang-orang berkelahi di luar toko makanan.</code> | |
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| <code>1</code> | <code>Dua anak kecil berbaju biru, satu dengan nomor 9 dan satu dengan nomor 2 berdiri di tangga kayu di kamar mandi dan mencuci tangan di wastafel.</code> | <code>Dua anak dengan kaus bernomor mencuci tangan mereka.</code> | |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 2 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 2 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
|
|:-----:|:----:|:-----------------------:|:------------------------:| |
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| 0 | 0 | 0.2318 | - | |
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| 2.0 | 8342 | - | 0.5979 | |
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|
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|
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.31.0 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers and SoftmaxLoss |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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