Hvare commited on
Commit
c7fab17
1 Parent(s): d12e805

Add new SentenceTransformer model.

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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ language: []
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+ library_name: sentence-transformers
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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:10330
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: indobenchmark/indobert-base-p2
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+ datasets: []
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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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+ widget:
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+ - source_sentence: Pura Ulun Danu terletak sekitar 56 kilometer dari Kota Denpasar.
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+ sentences:
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+ - Dalam tujuh bulan kehamilan, organ tubuh bayi sudah sempurna.
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+ - Dokter Adeline menjelaskan aturan-aturan agar diabetisi aman berpuasa.
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+ - Pura Ulun Danu terletak sekitar satu jam perjalanan dari Kota Denpasar.
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+ - source_sentence: Di luar ujung barat laut, taiga dominan, mencakup bagian besar
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+ dari seluruh Siberia.
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+ sentences:
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+ - Banyak keraguan mengenai tanggal kelahiran Gaudapa.
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+ - Sebagian besar Siberia terletak di ujung barat laut,.
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+ - Maia menyaksikan balapan tanpa alasan.
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+ - source_sentence: Widodo Cahyono Putro adalah seorang pelatih dan pemain sepak bola
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+ legendaris Indonesia.
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+ sentences:
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+ - Ia berjanji untuk jatuh di lubang yang sama.
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+ - Pemain sepak bola legendaris pasti menjadi pelatih sepak bola.
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+ - Nazaruddin menegaskan bahwa mantan Wakil Ketua Komisi II DPR itu menerima uang
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+ dari proyek e-KTP sebesar $500 ribu.
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+ - source_sentence: Salah satunya seorang lelaki yang sedang memakan permen karet yang
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+ dengan paksa dikeluarkan dari mulutnya.
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+ sentences:
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+ - Charles Leclerc gagal menjadi juara dunia F2.
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+ - Pendukung pembrontakan Cina sudah tidak ada.
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+ - Lelaki itu bukan salah satunya.
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+ - source_sentence: Tumenggung Wirapraja setelah mangkat dimakamkan di Kebon Alas Warudoyong,
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+ Kecamatan Panumbangan, Kabupaten Ciamis.
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+ sentences:
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+ - Peristiwa Pemberontakan Besar di Minahasa memiliki dampak besar pada tentara Sekutu.
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+ - Di hari libur ini, Pengunjung semua taman nasional tidak dibebaskan biaya.
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+ - Tumenggung Wirapraja dikremasi setelah dipastikan mangkat dan abunya kemudian
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+ dilarungkan ke Pantai Laut Selatan.
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+ pipeline_tag: sentence-similarity
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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: -0.05296221890135024
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: -0.06107163627723088
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: -0.06399377304712585
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: -0.06835801919486152
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: -0.0642574675392147
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: -0.06906447787846218
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: -0.024528943319169508
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: -0.024236369255517205
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: -0.024528943319169508
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: -0.024236369255517205
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on indobenchmark/indobert-base-p2
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+
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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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+
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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:** 75 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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+
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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': 75, '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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+
132
+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
136
+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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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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+
144
+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("Hvare/Athena-indobert-finetuned-indonli")
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+ # Run inference
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+ sentences = [
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+ 'Tumenggung Wirapraja setelah mangkat dimakamkan di Kebon Alas Warudoyong, Kecamatan Panumbangan, Kabupaten Ciamis.',
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+ 'Tumenggung Wirapraja dikremasi setelah dipastikan mangkat dan abunya kemudian dilarungkan ke Pantai Laut Selatan.',
150
+ 'Di hari libur ini, Pengunjung semua taman nasional tidak dibebaskan biaya.',
151
+ ]
152
+ 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)
159
+ # [3, 3]
160
+ ```
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+
162
+ <!--
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+ ### Direct Usage (Transformers)
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+
165
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
167
+ </details>
168
+ -->
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+
170
+ <!--
171
+ ### Downstream Usage (Sentence Transformers)
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+
173
+ You can finetune this model on your own dataset.
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+
175
+ <details><summary>Click to expand</summary>
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+
177
+ </details>
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+ -->
179
+
180
+ <!--
181
+ ### Out-of-Scope Use
182
+
183
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
185
+
186
+ ## Evaluation
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+
188
+ ### Metrics
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+
190
+ #### 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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+
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+ | Metric | Value |
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+ |:-------------------|:------------|
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+ | pearson_cosine | -0.053 |
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+ | spearman_cosine | -0.0611 |
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+ | pearson_manhattan | -0.064 |
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+ | spearman_manhattan | -0.0684 |
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+ | pearson_euclidean | -0.0643 |
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+ | spearman_euclidean | -0.0691 |
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+ | pearson_dot | -0.0245 |
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+ | spearman_dot | -0.0242 |
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+ | pearson_max | -0.0245 |
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+ | **spearman_max** | **-0.0242** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ <!--
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+ ### Recommendations
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+
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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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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 10,330 training samples
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+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence_0 | sentence_1 | label |
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+ |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
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+ | type | string | string | int |
232
+ | details | <ul><li>min: 11 tokens</li><li>mean: 29.47 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.25 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>0: ~35.90%</li><li>1: ~32.00%</li><li>2: ~32.10%</li></ul> |
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+ * Samples:
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+ | sentence_0 | sentence_1 | label |
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+ |:--------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:---------------|
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+ | <code>"" "Akan ada protes dan hal-hal lain, semua nya sudah direncanakan," "ungkap oposisi kepada El Mundo."</code> | <code>Protes dan hal-hal lain sudah direncanakan.</code> | <code>0</code> |
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+ | <code>Tak jarang, bangun kesiangan pun jadi alasan untuk tak berolahraga.</code> | <code>Salah satu alasan tidak berolahraga adalah bangun kesiangan.</code> | <code>0</code> |
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+ | <code>Namun, saingannya Prabowo Subianto juga mendeklarasikan kemenangan, membuat orang Indonesia bingung.</code> | <code>Prabowo menerima bahwa Dia kalah.</code> | <code>2</code> |
239
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
240
+ ```json
241
+ {
242
+ "scale": 20.0,
243
+ "similarity_fct": "cos_sim"
244
+ }
245
+ ```
246
+
247
+ ### Training Hyperparameters
248
+ #### Non-Default Hyperparameters
249
+
250
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 16
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+ - `per_device_eval_batch_size`: 16
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+ - `num_train_epochs`: 1
254
+ - `multi_dataset_batch_sampler`: round_robin
255
+
256
+ #### All Hyperparameters
257
+ <details><summary>Click to expand</summary>
258
+
259
+ - `overwrite_output_dir`: False
260
+ - `do_predict`: False
261
+ - `eval_strategy`: steps
262
+ - `prediction_loss_only`: True
263
+ - `per_device_train_batch_size`: 16
264
+ - `per_device_eval_batch_size`: 16
265
+ - `per_gpu_train_batch_size`: None
266
+ - `per_gpu_eval_batch_size`: None
267
+ - `gradient_accumulation_steps`: 1
268
+ - `eval_accumulation_steps`: None
269
+ - `learning_rate`: 5e-05
270
+ - `weight_decay`: 0.0
271
+ - `adam_beta1`: 0.9
272
+ - `adam_beta2`: 0.999
273
+ - `adam_epsilon`: 1e-08
274
+ - `max_grad_norm`: 1
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+ - `num_train_epochs`: 1
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
279
+ - `warmup_ratio`: 0.0
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+ - `warmup_steps`: 0
281
+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
284
+ - `logging_nan_inf_filter`: True
285
+ - `save_safetensors`: True
286
+ - `save_on_each_node`: False
287
+ - `save_only_model`: False
288
+ - `restore_callback_states_from_checkpoint`: False
289
+ - `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
294
+ - `jit_mode_eval`: False
295
+ - `use_ipex`: False
296
+ - `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
305
+ - `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
315
+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
318
+ - `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
326
+ - `adafactor`: False
327
+ - `group_by_length`: False
328
+ - `length_column_name`: length
329
+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
331
+ - `ddp_broadcast_buffers`: False
332
+ - `dataloader_pin_memory`: True
333
+ - `dataloader_persistent_workers`: False
334
+ - `skip_memory_metrics`: True
335
+ - `use_legacy_prediction_loop`: False
336
+ - `push_to_hub`: False
337
+ - `resume_from_checkpoint`: None
338
+ - `hub_model_id`: None
339
+ - `hub_strategy`: every_save
340
+ - `hub_private_repo`: False
341
+ - `hub_always_push`: False
342
+ - `gradient_checkpointing`: False
343
+ - `gradient_checkpointing_kwargs`: None
344
+ - `include_inputs_for_metrics`: False
345
+ - `eval_do_concat_batches`: True
346
+ - `fp16_backend`: auto
347
+ - `push_to_hub_model_id`: None
348
+ - `push_to_hub_organization`: None
349
+ - `mp_parameters`:
350
+ - `auto_find_batch_size`: False
351
+ - `full_determinism`: False
352
+ - `torchdynamo`: None
353
+ - `ray_scope`: last
354
+ - `ddp_timeout`: 1800
355
+ - `torch_compile`: False
356
+ - `torch_compile_backend`: None
357
+ - `torch_compile_mode`: None
358
+ - `dispatch_batches`: None
359
+ - `split_batches`: None
360
+ - `include_tokens_per_second`: False
361
+ - `include_num_input_tokens_seen`: False
362
+ - `neftune_noise_alpha`: None
363
+ - `optim_target_modules`: None
364
+ - `batch_eval_metrics`: False
365
+ - `batch_sampler`: batch_sampler
366
+ - `multi_dataset_batch_sampler`: round_robin
367
+
368
+ </details>
369
+
370
+ ### Training Logs
371
+ | Epoch | Step | Training Loss | sts-dev_spearman_max |
372
+ |:------:|:----:|:-------------:|:--------------------:|
373
+ | 0.0991 | 64 | - | -0.0411 |
374
+ | 0.1981 | 128 | - | -0.0426 |
375
+ | 0.2972 | 192 | - | -0.0419 |
376
+ | 0.3963 | 256 | - | -0.0425 |
377
+ | 0.4954 | 320 | - | -0.0384 |
378
+ | 0.5944 | 384 | - | -0.0260 |
379
+ | 0.6935 | 448 | - | -0.0216 |
380
+ | 0.7740 | 500 | 0.0531 | - |
381
+ | 0.7926 | 512 | - | -0.0243 |
382
+ | 0.8916 | 576 | - | -0.0241 |
383
+ | 0.9907 | 640 | - | -0.0242 |
384
+ | 1.0 | 646 | - | -0.0242 |
385
+
386
+
387
+ ### Framework Versions
388
+ - Python: 3.10.12
389
+ - Sentence Transformers: 3.0.1
390
+ - Transformers: 4.41.2
391
+ - PyTorch: 2.3.0+cu121
392
+ - Accelerate: 0.31.0
393
+ - Datasets: 2.19.2
394
+ - Tokenizers: 0.19.1
395
+
396
+ ## Citation
397
+
398
+ ### BibTeX
399
+
400
+ #### Sentence Transformers
401
+ ```bibtex
402
+ @inproceedings{reimers-2019-sentence-bert,
403
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
404
+ author = "Reimers, Nils and Gurevych, Iryna",
405
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
406
+ month = "11",
407
+ year = "2019",
408
+ publisher = "Association for Computational Linguistics",
409
+ url = "https://arxiv.org/abs/1908.10084",
410
+ }
411
+ ```
412
+
413
+ #### MultipleNegativesRankingLoss
414
+ ```bibtex
415
+ @misc{henderson2017efficient,
416
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
417
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
418
+ year={2017},
419
+ eprint={1705.00652},
420
+ archivePrefix={arXiv},
421
+ primaryClass={cs.CL}
422
+ }
423
+ ```
424
+
425
+ <!--
426
+ ## Glossary
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+
428
+ *Clearly define terms in order to be accessible across audiences.*
429
+ -->
430
+
431
+ <!--
432
+ ## Model Card Authors
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+
434
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
435
+ -->
436
+
437
+ <!--
438
+ ## Model Card Contact
439
+
440
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
441
+ -->
config.json ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "_name_or_path": "Hvare/Athena-indobert-finetuned-indonli",
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+ "_num_labels": 5,
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+ "architectures": [
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+ "BertModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "directionality": "bidi",
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "LABEL_0",
15
+ "1": "LABEL_1",
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