|
--- |
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base_model: indobenchmark/indobert-base-p1 |
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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:12000 |
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- loss:MegaBatchMarginLoss |
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widget: |
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- source_sentence: Awalnya merupakan singkatan dari John's Macintosh Project. |
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sentences: |
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- Sebuah formasi yang terdiri dari sekitar 50 petugas Polisi Baltimore akhirnya |
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menempatkan diri mereka di antara para perusuh dan milisi, memungkinkan Massachusetts |
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ke-6 untuk melanjutkan ke Stasiun Camden. |
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- Mengecat luka dapat melindungi dari jamur dan hama. |
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- Dulunya merupakan singkatan dari John's Macintosh Project. |
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- source_sentence: Boueiz berprofesi sebagai pengacara. |
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sentences: |
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- Mereka juga gagal mengembangkan Water Cooperation Quotient yang baru. |
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- Pada Pemilu 1970, ia ikut serta dari Partai Persatuan Nasional namun dikalahkan. |
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- Seorang pengacara berprofesi sebagai Boueiz. |
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- source_sentence: Fakultas Studi Oriental memiliki seorang profesor. |
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sentences: |
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- Di tempat lain di New Mexico, LAHS terkadang dianggap sebagai sekolah untuk orang |
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kaya. |
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- Laporan lain juga menunjukkan kandungannya lebih rendah dari 0,1% di Australia. |
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- Profesor tersebut merupakan bagian dari Fakultas Studi Oriental. |
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- source_sentence: Hal ini terjadi di sejumlah negara, termasuk Ethiopia, Republik |
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Demokratik Kongo, dan Afrika Selatan. |
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sentences: |
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- Hal ini diketahui terjadi di Eritrea, Ethiopia, Kongo, Tanzania, Namibia dan Afrika |
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Selatan. |
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- Gugus amil digantikan oleh gugus pentil. |
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- Dan saya beritahu Anda sesuatu, itu tidak adil. |
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- source_sentence: Ini adalah wilayah sosial-ekonomi yang lebih rendah. |
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sentences: |
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- Ini adalah bengkel perbaikan mobil terbaru yang masih beroperasi di kota. |
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- Zelinsky hanya berteori bahwa tidak ada tiga bilangan bulat berurutan yang semuanya |
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dapat difaktorkan ulang. |
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- Ini adalah wilayah sosial-ekonomi yang lebih tinggi. |
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model-index: |
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- name: SentenceTransformer based on indobenchmark/indobert-base-p1 |
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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: str dev |
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type: str-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.45499177580198114 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.47824954773877343 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.5063760846250573 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.49835693711719375 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.5062153453050553 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.4982327535492364 |
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name: Spearman Euclidean |
|
- type: pearson_dot |
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value: 0.27097056415300647 |
|
name: Pearson Dot |
|
- type: spearman_dot |
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value: 0.25179460239023077 |
|
name: Spearman Dot |
|
- type: pearson_max |
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value: 0.5063760846250573 |
|
name: Pearson Max |
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- type: spearman_max |
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value: 0.49835693711719375 |
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name: Spearman Max |
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- task: |
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type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
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name: str test |
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type: str-test |
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metrics: |
|
- type: pearson_cosine |
|
value: 0.47495139518851825 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.5059515739122313 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.50154011084872 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.5058071904463332 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.5028237271275693 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
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value: 0.5061159996491946 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.3250041946830172 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.31627719040314917 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.5028237271275693 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.5061159996491946 |
|
name: Spearman Max |
|
--- |
|
|
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# SentenceTransformer based on indobenchmark/indobert-base-p1 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1). 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-p1](https://huggingface.co/indobenchmark/indobert-base-p1) <!-- at revision c2cd0b51ddce6580eb35263b39b0a1e5fb0a39e2 --> |
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- **Maximum Sequence Length:** 32 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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|
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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': 32, '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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|
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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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|
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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("damand2061/negasibert-mbm") |
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# Run inference |
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sentences = [ |
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'Ini adalah wilayah sosial-ekonomi yang lebih rendah.', |
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'Ini adalah wilayah sosial-ekonomi yang lebih tinggi.', |
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'Zelinsky hanya berteori bahwa tidak ada tiga bilangan bulat berurutan yang semuanya dapat difaktorkan ulang.', |
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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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<!-- |
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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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<!-- |
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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: `str-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 | 0.455 | |
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| spearman_cosine | 0.4782 | |
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| pearson_manhattan | 0.5064 | |
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| spearman_manhattan | 0.4984 | |
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| pearson_euclidean | 0.5062 | |
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| spearman_euclidean | 0.4982 | |
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| pearson_dot | 0.271 | |
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| spearman_dot | 0.2518 | |
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| pearson_max | 0.5064 | |
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| **spearman_max** | **0.4984** | |
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|
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#### Semantic Similarity |
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* Dataset: `str-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 | 0.475 | |
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| spearman_cosine | 0.506 | |
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| pearson_manhattan | 0.5015 | |
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| spearman_manhattan | 0.5058 | |
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| pearson_euclidean | 0.5028 | |
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| spearman_euclidean | 0.5061 | |
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| pearson_dot | 0.325 | |
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| spearman_dot | 0.3163 | |
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| pearson_max | 0.5028 | |
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| **spearman_max** | **0.5061** | |
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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: 12,000 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 14.84 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.83 tokens</li><li>max: 32 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:-------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| |
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| <code>Pusat Peringatan Topan Gabungan (JTWC) juga mengeluarkan peringatan dalam kapasitas tidak resmi.</code> | <code>Pusat Peringatan Topan Gabungan (JTWC) hanya mengeluarkan peringatan dalam kapasitas yang tidak resmi.</code> | |
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| <code>DNP komersial digunakan sebagai antiseptik dan pestisida bioakumulasi non-selektif.</code> | <code>DNP komersial tidak dapat digunakan sebagai antiseptik atau pestisida bioakumulasi non-selektif.</code> | |
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| <code>Kuncian tulang belakang dan kuncian serviks diperbolehkan dan wajib dalam kompetisi jiu-jitsu Brasil IBJJF.</code> | <code>Kuncian tulang belakang dan kuncian serviks dilarang dalam kompetisi jiu-jitsu Brasil IBJJF.</code> | |
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* Loss: [<code>MegaBatchMarginLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#megabatchmarginloss) |
|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `per_device_train_batch_size`: 500 |
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- `per_device_eval_batch_size`: 500 |
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- `num_train_epochs`: 5 |
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- `multi_dataset_batch_sampler`: round_robin |
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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`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 500 |
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- `per_device_eval_batch_size`: 500 |
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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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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-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 |
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- `num_train_epochs`: 5 |
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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`: 0.0 |
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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`: False |
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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 |
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- `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 |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `eval_use_gather_object`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | str-dev_spearman_max | str-test_spearman_max | |
|
|:-----:|:----:|:--------------------:|:---------------------:| |
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| 1.0 | 24 | 0.4904 | 0.5030 | |
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| 2.0 | 48 | 0.4905 | 0.5036 | |
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| 3.0 | 72 | 0.4947 | 0.5041 | |
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| 4.0 | 96 | 0.4963 | 0.5061 | |
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| 5.0 | 120 | 0.4984 | 0.5061 | |
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|
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|
|
### Framework Versions |
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- Python: 3.10.14 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.44.0 |
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- PyTorch: 2.4.0 |
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- Accelerate: 0.33.0 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.19.1 |
|
|
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## Citation |
|
|
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### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
|
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", |
|
url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
|
#### MegaBatchMarginLoss |
|
```bibtex |
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@inproceedings{wieting-gimpel-2018-paranmt, |
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title = "{P}ara{NMT}-50{M}: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations", |
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author = "Wieting, John and Gimpel, Kevin", |
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editor = "Gurevych, Iryna and Miyao, Yusuke", |
|
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = jul, |
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year = "2018", |
|
address = "Melbourne, Australia", |
|
publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/P18-1042", |
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doi = "10.18653/v1/P18-1042", |
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pages = "451--462", |
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} |
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``` |
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