Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "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
+}

2_Dense/config.json

@@ -0,0 +1 @@
+{"in_features": 1024, "out_features": 256, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:514a2b011c5171b1add47e6ab3f8544102be7ff1dd57140802d0db0c8b216cc5
+size 1049760

README.md

@@ -0,0 +1,675 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1432
+- loss:MultipleNegativesRankingLoss
+base_model: denaya/indoSBERT-large
+widget:
+- source_sentence: 'Input-output domestik Indonesia: 17 sektor usaha, harga produsen,
+    data tahun 2016 (juta Rp)'
+  sentences:
+  - 'Impor Besi dan Baja Menurut Negara Asal Utama, 2017-2023 '
+  - 'IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 1996-2014
+    (1996=100) '
+  - 'Tabel Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen (17
+    Lapangan Usaha), 2016 (Juta Rupiah) '
+- source_sentence: 'Gaji bulanan: beda umur, beda jenis pekerjaan (9 sektor), 2017'
+  sentences:
+  - 'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur
+    dan Lapangan Pekerjaan Utama di 9 Sektor (Rupiah), 2017 '
+  - 'Ekspor Rumput Laut dan Ganggang Lainnya menurut Negara Tujuan Utama, 2012-2023 '
+  - 'Rata-Rata Harga Valuta Asing Terpilih menurut Provinsi 2017 '
+- source_sentence: Ringkasan aliran dana kuartal terakhir 2009 dalam Rupiah
+  sentences:
+  - 'Jumlah Perahu/Kapal, Luas Usaha Budidaya dan Produksi menurut Sub Sektor Perikanan,
+    2002-2016 '
+  - 'Jumlah Pendapatan Menurut Golongan Rumah Tangga (miliar rupiah) 2000, 2005, dan
+    2008 '
+  - 'Ringkasan Neraca Arus Dana, Triwulan IV, 2009, (Miliar Rupiah) '
+- source_sentence: Berapa total transaksi (harga pembeli) untuk 9 sektor ekonomi di
+    Indonesia tahun 2005? (miliar rupiah)
+  sentences:
+  - 'Jumlah Rumah Tangga Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2016 '
+  - 'Transaksi Total Atas Dasar Harga Pembeli 9 Sektor Ekonomi (miliar rupiah), 2005 '
+  - 'Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau
+    Jawa dan Sumatera dengan Nasional (2018=100) '
+- source_sentence: Bagaimana kaitan antara pendidikan dan kegiatan mingguan penduduk
+    usia 15+ pada tahun 2022?
+  sentences:
+  - 'Persentase Perkembangan Distribusi Pengeluaran '
+  - 'Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Lapangan Pekerjaan
+    Utama (ribu rupiah), 2018 '
+  - 'Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan
+    dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008-2024 '
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- cosine_mcc
+model-index:
+- name: SentenceTransformer based on denaya/indoSBERT-large
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: eval
+      type: eval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9120521172638436
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.990228013029316
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.993485342019544
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.996742671009772
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9120521172638436
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3572204125950054
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.23778501628664495
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.13745928338762217
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7097252402956855
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7867346590488319
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8052359035035943
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8221312325947948
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8348212945928647
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9497052892818366
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7729410950742827
+      name: Cosine Map@100
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: quora duplicates dev
+      type: quora_duplicates_dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9914529914529915
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.31953397393226624
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.9850953206239168
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.30364981293678284
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.988865692414753
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.981353591160221
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.9956970583311449
+      name: Cosine Ap
+    - type: cosine_mcc
+      value: 0.9791180702139771
+      name: Cosine Mcc
+---
+
+# SentenceTransformer based on denaya/indoSBERT-large
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large). It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) <!-- at revision 5c64d43f07f7054dfbf33d226b3066414b6ebc4a -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 256 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+  (2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("yahyaabd/allstats-search-large-bpstable-v1")
+# Run inference
+sentences = [
+    'Bagaimana kaitan antara pendidikan dan kegiatan mingguan penduduk usia 15+ pada tahun 2022?',
+    'Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008-2024 ',
+    'Persentase Perkembangan Distribusi Pengeluaran ',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 256]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Dataset: `eval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.9121     |
+| cosine_accuracy@3   | 0.9902     |
+| cosine_accuracy@5   | 0.9935     |
+| cosine_accuracy@10  | 0.9967     |
+| cosine_precision@1  | 0.9121     |
+| cosine_precision@3  | 0.3572     |
+| cosine_precision@5  | 0.2378     |
+| cosine_precision@10 | 0.1375     |
+| cosine_recall@1     | 0.7097     |
+| cosine_recall@3     | 0.7867     |
+| cosine_recall@5     | 0.8052     |
+| cosine_recall@10    | 0.8221     |
+| **cosine_ndcg@10**  | **0.8348** |
+| cosine_mrr@10       | 0.9497     |
+| cosine_map@100      | 0.7729     |
+
+#### Binary Classification
+
+* Dataset: `quora_duplicates_dev`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                    | Value      |
+|:--------------------------|:-----------|
+| cosine_accuracy           | 0.9915     |
+| cosine_accuracy_threshold | 0.3195     |
+| cosine_f1                 | 0.9851     |
+| cosine_f1_threshold       | 0.3036     |
+| cosine_precision          | 0.9889     |
+| cosine_recall             | 0.9814     |
+| **cosine_ap**             | **0.9957** |
+| cosine_mcc                | 0.9791     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 1,432 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | label                        |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
+  | type    | string                                                                            | string                                                                            | int                          |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 16.84 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 20.88 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
+* Samples:
+  | sentence_0                                                                                             | sentence_1                                                                                                                       | label          |
+  |:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Average monthly net wage/salary of employees by age group and type of work (Rupiah), 2018</code> | <code>Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur dan Jenis Pekerjaan (Rupiah), 2018 </code> | <code>1</code> |
+  | <code>Cek average real wage buruh industri pengolahan (level bawah) sekitar tahun 2009</code>          | <code>Rata-rata Upah Riil Per Bulan Buruh Industri Pengolahan di Bawah Mandor, 2005-2014 (1996=100) </code>                      | <code>1</code> |
+  | <code>Dimana saya bisa lihat rekapitulasi dokumen RPB kabupaten/kota?</code>                           | <code>Rekap Dokumen RPB Kabupaten/Kota </code>                                                                                   | <code>1</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 30
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 30
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step | Training Loss | eval_cosine_ndcg@10 | quora_duplicates_dev_cosine_ap |
+|:-------:|:----:|:-------------:|:-------------------:|:------------------------------:|
+| 0.2222  | 20   | -             | 0.7769              | -                              |
+| 0.4444  | 40   | -             | 0.8167              | -                              |
+| 0.6667  | 60   | -             | 0.8221              | -                              |
+| 0.8889  | 80   | -             | 0.8282              | -                              |
+| 1.0     | 90   | -             | 0.8256              | -                              |
+| 1.1111  | 100  | -             | 0.8278              | -                              |
+| 1.3333  | 120  | -             | 0.8388              | -                              |
+| 1.5556  | 140  | -             | 0.8347              | -                              |
+| 1.7778  | 160  | -             | 0.8351              | -                              |
+| 2.0     | 180  | -             | 0.8407              | -                              |
+| 2.2222  | 200  | -             | 0.8302              | -                              |
+| 2.4444  | 220  | -             | 0.8261              | -                              |
+| 2.6667  | 240  | -             | 0.8217              | -                              |
+| 2.8889  | 260  | -             | 0.8161              | -                              |
+| 3.0     | 270  | -             | 0.8143              | -                              |
+| 3.1111  | 280  | -             | 0.8133              | -                              |
+| 3.3333  | 300  | -             | 0.8259              | -                              |
+| 3.5556  | 320  | -             | 0.8342              | -                              |
+| 3.7778  | 340  | -             | 0.8267              | -                              |
+| 4.0     | 360  | -             | 0.8190              | -                              |
+| 4.2222  | 380  | -             | 0.8193              | -                              |
+| 4.4444  | 400  | -             | 0.8281              | -                              |
+| 4.6667  | 420  | -             | 0.8283              | -                              |
+| 4.8889  | 440  | -             | 0.8197              | -                              |
+| 5.0     | 450  | -             | 0.8211              | -                              |
+| 5.1111  | 460  | -             | 0.8118              | -                              |
+| 5.3333  | 480  | -             | 0.8298              | -                              |
+| 5.5556  | 500  | 0.0412        | 0.8283              | -                              |
+| 5.7778  | 520  | -             | 0.8264              | -                              |
+| 6.0     | 540  | -             | 0.8271              | -                              |
+| 6.2222  | 560  | -             | 0.8243              | -                              |
+| 6.4444  | 580  | -             | 0.8256              | -                              |
+| 6.6667  | 600  | -             | 0.8356              | -                              |
+| 6.8889  | 620  | -             | 0.8332              | -                              |
+| 7.0     | 630  | -             | 0.8250              | -                              |
+| 7.1111  | 640  | -             | 0.8179              | -                              |
+| 7.3333  | 660  | -             | 0.8356              | -                              |
+| 7.5556  | 680  | -             | 0.8400              | -                              |
+| 7.7778  | 700  | -             | 0.8349              | -                              |
+| 8.0     | 720  | -             | 0.8281              | -                              |
+| 8.2222  | 740  | -             | 0.8330              | -                              |
+| 8.4444  | 760  | -             | 0.8338              | -                              |
+| 8.6667  | 780  | -             | 0.8338              | -                              |
+| 8.8889  | 800  | -             | 0.8344              | -                              |
+| 9.0     | 810  | -             | 0.8319              | -                              |
+| 9.1111  | 820  | -             | 0.8328              | -                              |
+| 9.3333  | 840  | -             | 0.8325              | -                              |
+| 9.5556  | 860  | -             | 0.8375              | -                              |
+| 9.7778  | 880  | -             | 0.8306              | -                              |
+| 10.0    | 900  | -             | 0.8263              | -                              |
+| 10.2222 | 920  | -             | 0.8280              | -                              |
+| 10.4444 | 940  | -             | 0.8272              | -                              |
+| 10.6667 | 960  | -             | 0.8280              | -                              |
+| 10.8889 | 980  | -             | 0.8313              | -                              |
+| 11.0    | 990  | -             | 0.8307              | -                              |
+| 11.1111 | 1000 | 0.0198        | 0.8324              | -                              |
+| 11.3333 | 1020 | -             | 0.8303              | -                              |
+| 11.5556 | 1040 | -             | 0.8262              | -                              |
+| 11.7778 | 1060 | -             | 0.8294              | -                              |
+| 12.0    | 1080 | -             | 0.8309              | -                              |
+| 12.2222 | 1100 | -             | 0.8274              | -                              |
+| 12.4444 | 1120 | -             | 0.8312              | -                              |
+| 12.6667 | 1140 | -             | 0.8371              | -                              |
+| 12.8889 | 1160 | -             | 0.8408              | -                              |
+| 13.0    | 1170 | -             | 0.8374              | -                              |
+| 13.1111 | 1180 | -             | 0.8344              | -                              |
+| 13.3333 | 1200 | -             | 0.8341              | -                              |
+| 13.5556 | 1220 | -             | 0.8333              | -                              |
+| 13.7778 | 1240 | -             | 0.8388              | -                              |
+| 14.0    | 1260 | -             | 0.8414              | -                              |
+| 14.2222 | 1280 | -             | 0.8344              | -                              |
+| 14.4444 | 1300 | -             | 0.8328              | -                              |
+| 14.6667 | 1320 | -             | 0.8340              | -                              |
+| 14.8889 | 1340 | -             | 0.8317              | -                              |
+| 15.0    | 1350 | -             | 0.8260              | -                              |
+| 15.1111 | 1360 | -             | 0.8252              | -                              |
+| 15.3333 | 1380 | -             | 0.8244              | -                              |
+| 15.5556 | 1400 | -             | 0.8269              | -                              |
+| 15.7778 | 1420 | -             | 0.8275              | -                              |
+| 16.0    | 1440 | -             | 0.8281              | -                              |
+| 16.2222 | 1460 | -             | 0.8294              | -                              |
+| 16.4444 | 1480 | -             | 0.8299              | -                              |
+| 16.6667 | 1500 | 0.0136        | 0.8318              | -                              |
+| 16.8889 | 1520 | -             | 0.8320              | -                              |
+| 17.0    | 1530 | -             | 0.8332              | -                              |
+| 17.1111 | 1540 | -             | 0.8337              | -                              |
+| 17.3333 | 1560 | -             | 0.8299              | -                              |
+| 17.5556 | 1580 | -             | 0.8283              | -                              |
+| 17.7778 | 1600 | -             | 0.8309              | -                              |
+| 18.0    | 1620 | -             | 0.8329              | -                              |
+| 18.2222 | 1640 | -             | 0.8317              | -                              |
+| 18.4444 | 1660 | -             | 0.8313              | -                              |
+| 18.6667 | 1680 | -             | 0.8317              | -                              |
+| 18.8889 | 1700 | -             | 0.8356              | -                              |
+| 19.0    | 1710 | -             | 0.8345              | -                              |
+| 19.1111 | 1720 | -             | 0.8358              | -                              |
+| 19.3333 | 1740 | -             | 0.8334              | -                              |
+| 19.5556 | 1760 | -             | 0.8335              | -                              |
+| 19.7778 | 1780 | -             | 0.8318              | -                              |
+| 20.0    | 1800 | -             | 0.8326              | -                              |
+| 20.2222 | 1820 | -             | 0.8318              | -                              |
+| 20.4444 | 1840 | -             | 0.8335              | -                              |
+| 20.6667 | 1860 | -             | 0.8333              | -                              |
+| 20.8889 | 1880 | -             | 0.8335              | -                              |
+| 21.0    | 1890 | -             | 0.8341              | -                              |
+| 21.1111 | 1900 | -             | 0.8341              | -                              |
+| 21.3333 | 1920 | -             | 0.8355              | -                              |
+| 21.5556 | 1940 | -             | 0.8360              | -                              |
+| 21.7778 | 1960 | -             | 0.8343              | -                              |
+| 22.0    | 1980 | -             | 0.8351              | -                              |
+| 22.2222 | 2000 | 0.015         | 0.8342              | -                              |
+| 22.4444 | 2020 | -             | 0.8342              | -                              |
+| 22.6667 | 2040 | -             | 0.8339              | -                              |
+| 22.8889 | 2060 | -             | 0.8342              | -                              |
+| 23.0    | 2070 | -             | 0.8345              | -                              |
+| 23.1111 | 2080 | -             | 0.8354              | -                              |
+| 23.3333 | 2100 | -             | 0.8366              | -                              |
+| 23.5556 | 2120 | -             | 0.8379              | -                              |
+| 23.7778 | 2140 | -             | 0.8386              | -                              |
+| 24.0    | 2160 | -             | 0.8367              | -                              |
+| 24.2222 | 2180 | -             | 0.8357              | -                              |
+| 24.4444 | 2200 | -             | 0.8372              | -                              |
+| 24.6667 | 2220 | -             | 0.8377              | -                              |
+| 24.8889 | 2240 | -             | 0.8373              | -                              |
+| 25.0    | 2250 | -             | 0.8367              | -                              |
+| 25.1111 | 2260 | -             | 0.8366              | -                              |
+| 25.3333 | 2280 | -             | 0.8369              | -                              |
+| 25.5556 | 2300 | -             | 0.8373              | -                              |
+| 25.7778 | 2320 | -             | 0.8366              | -                              |
+| 26.0    | 2340 | -             | 0.8354              | -                              |
+| 26.2222 | 2360 | -             | 0.8347              | -                              |
+| 26.4444 | 2380 | -             | 0.8344              | -                              |
+| 26.6667 | 2400 | -             | 0.8341              | -                              |
+| 26.8889 | 2420 | -             | 0.8343              | -                              |
+| 27.0    | 2430 | -             | 0.8344              | -                              |
+| 27.1111 | 2440 | -             | 0.8345              | -                              |
+| 27.3333 | 2460 | -             | 0.8344              | -                              |
+| 27.5556 | 2480 | -             | 0.8347              | -                              |
+| 27.7778 | 2500 | 0.0136        | 0.8342              | -                              |
+| 28.0    | 2520 | -             | 0.8347              | -                              |
+| 28.2222 | 2540 | -             | 0.8346              | -                              |
+| 28.4444 | 2560 | -             | 0.8346              | -                              |
+| 28.6667 | 2580 | -             | 0.8347              | -                              |
+| 28.8889 | 2600 | -             | 0.8348              | -                              |
+| 29.0    | 2610 | -             | 0.8348              | -                              |
+| 29.1111 | 2620 | -             | 0.8348              | -                              |
+| 29.3333 | 2640 | -             | 0.8348              | -                              |
+| 29.5556 | 2660 | -             | 0.8348              | -                              |
+| 29.7778 | 2680 | -             | 0.8348              | -                              |
+| 30.0    | 2700 | -             | 0.8348              | -                              |
+| -1      | -1   | -             | -                   | 0.9957                         |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    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},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,47 @@
+{
+  "_name_or_path": "denaya/indoSBERT-Large",
+  "_num_labels": 5,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:571d77e13317318c61517a3957be66da18e48e6355c79923b587f37727ca0fee
+size 1340612432

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 256,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}