first commit

1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:10330
+- loss:MultipleNegativesRankingLoss
+base_model: indobenchmark/indobert-base-p2
+datasets: []
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: Dari hasil pernikahan keduanya, Desy dikaruniai seorang anak, yaitu
+    Nasywa Nathania Hamzah yang lahir pada tanggal 21 Juli 2002.
+  sentences:
+  - Aturan baru ini membuat pemain bertubuh tinggi kesulitan.
+  - Penderita diabetes hendaknya mengonsumsi makanan dengan indeks glikemik rendah.
+  - Seorang anak bernama Nasywa Nathania Hamzah lahir pada tanggal 21 Juli 2002.
+- source_sentence: Marini pun langsung mengabadikan momen saat melihat bayi Iyek dan
+    Dewi tersebut, ke akun Instagramnya.
+  sentences:
+  - Kurang dari 500 orang terjangkit virus Corona di Indonesia.
+  - Marini memiliki akun instagram.
+  - Spesies ini pernah dipublikasikan sebelum 1753.
+- source_sentence: Tujuan kami sebenarnya adalah strategi untuk melindungi kulit dari
+    radiasi UV dan kanker.
+  sentences:
+  - Pendapatan Airbnb berkurang sebelum Airbnb mengumumkan protokol kebersihan baru.
+  - Kami membuat strategi ini karena tingginya pasien kanker kulit.
+  - Terdapat distrik Faro di Portugis.
+- source_sentence: Pembahasan tentang mitos gerhana bulan sebenarnya sudah terjadi
+    sejak dulu. Ada yang percaya gerhana bulan berpengaruh pada bumi bahkan kesehatan
+    tubuh.
+  sentences:
+  - Menonton orang main game seru.
+  - Orang yang terkaya ke-35 di Indonesia adalah seorang pria.
+  - Mitos gerhana bulan berpengaruh pada kesehatan tubuh.
+- source_sentence: Waduk wadaslintang sebenarnya terbagi menjadi dua kabupaten yaitu
+    kabupaten kebumen dan kabupaten wonosobo.
+  sentences:
+  - Amir menjadi pemimpin sayap kiri terdepan ketika Revolusi Nasional Indonesia berlangsung.
+  - Musim ini di ajang PBL 2020 Hendra melawan tim Pune 7 aces.
+  - Kabupaten kebumen dan kabupaten wonosobo bertentaggaan.
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on indobenchmark/indobert-base-p2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: -0.051616661741529624
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: -0.059260236757554256
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: -0.06426082223860986
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: -0.06596359759097158
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: -0.06368615893415144
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: -0.06528449816144678
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: -0.027898791319537007
+      name: Pearson Dot
+    - type: spearman_dot
+      value: -0.02595347491107127
+      name: Spearman Dot
+    - type: pearson_max
+      value: -0.027898791319537007
+      name: Pearson Max
+    - type: spearman_max
+      value: -0.02595347491107127
+      name: Spearman Max
+---
+
+# SentenceTransformer based on indobenchmark/indobert-base-p2
+
+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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f -->
+- **Maximum Sequence Length:** 200 tokens
+- **Output Dimensionality:** 768 tokens
+- **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': 200, 'do_lower_case': False}) with Transformer model: BertModel 
+  (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})
+)
+```
+
+## 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Waduk wadaslintang sebenarnya terbagi menjadi dua kabupaten yaitu kabupaten kebumen dan kabupaten wonosobo.',
+    'Kabupaten kebumen dan kabupaten wonosobo bertentaggaan.',
+    'Musim ini di ajang PBL 2020 Hendra melawan tim Pune 7 aces.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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
+
+#### Semantic Similarity
+* Dataset: `sts-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | -0.0516    |
+| spearman_cosine    | -0.0593    |
+| pearson_manhattan  | -0.0643    |
+| spearman_manhattan | -0.066     |
+| pearson_euclidean  | -0.0637    |
+| spearman_euclidean | -0.0653    |
+| pearson_dot        | -0.0279    |
+| spearman_dot       | -0.026     |
+| pearson_max        | -0.0279    |
+| **spearman_max**   | **-0.026** |
+
+<!--
+## 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: 10,330 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: 11 tokens</li><li>mean: 29.14 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.95 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>0: ~36.30%</li><li>1: ~32.90%</li><li>2: ~30.80%</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                             | sentence_1                                                                          | label          |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------|
+  | <code>Pada tahun 1436, pulau Timor mempunyai 12 kota bandar namun tidak disebutkan namanya.</code>                                                                                                                                                                                                                                                                                                                                                     | <code>Pulau Timor memiliki 10 kota bandar.</code>                                   | <code>2</code> |
+  | <code>Komoditas pertanian yang ada di desa ini antara lain: bunga potong, sayur mayur, waluh (lejet) terutama Paprika (Capsicum annuum L.). Komoditas ini menjadi sumber perekonomian utama di desa ini karena harganya yang lumayan dibandingkan sayuran lain.</code>                                                                                                                                                                                 | <code>Komoditas pertanian di desa ini lebih mahal dibandingkan sayuran lain.</code> | <code>1</code> |
+  | <code>Setelah batas waktu pencalonan pada tanggal 15 Juli 2003, sembilan kota telah mencalonkan diri untuk mengadakan Olimpiade 2012. Kota-kota tersebut adalah Havana, Istanbul, Leipzig, London, Madrid, Moskwa, New York City, Paris, dan Rio de Janeiro. Pada 18 Mei 2004, Komite Olimpiade Internasional (IOC), sebagai hasil penilaian teknis, mengurangi jumlah kota kandidat menjadi lima: London, Madrid, Moskwa, New York, dan Paris.</code> | <code>Jumlah kota kandidat tuan rumah olimpide bertambah pada 18 Mei 2004.</code>   | <code>2</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`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 3
+- `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`: False
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | sts-dev_spearman_max |
+|:------:|:----:|:-------------:|:--------------------:|
+| 0.0991 | 32   | -             | -0.0592              |
+| 0.1981 | 64   | -             | -0.0425              |
+| 0.2972 | 96   | -             | -0.0467              |
+| 0.3963 | 128  | -             | -0.0428              |
+| 0.4954 | 160  | -             | -0.0512              |
+| 0.5944 | 192  | -             | -0.0473              |
+| 0.6935 | 224  | -             | -0.0412              |
+| 0.7926 | 256  | -             | -0.0435              |
+| 0.8916 | 288  | -             | -0.0405              |
+| 0.9907 | 320  | -             | -0.0425              |
+| 1.0    | 323  | -             | -0.0420              |
+| 1.0898 | 352  | -             | -0.0346              |
+| 1.1889 | 384  | -             | -0.0333              |
+| 1.2879 | 416  | -             | -0.0325              |
+| 1.3870 | 448  | -             | -0.0312              |
+| 1.4861 | 480  | -             | -0.0316              |
+| 1.5480 | 500  | 0.077         | -                    |
+| 1.5851 | 512  | -             | -0.0260              |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## 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": "indobenchmark/indobert-base-p2",
+  "_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": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": 12,
+  "num_hidden_layers": 12,
+  "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": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+size 497787752

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

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sentence_bert_config.json

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+{
+  "max_seq_length": 200,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "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
+    },
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+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
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+      "special": true
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+      "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,
+  "mask_token": "[MASK]",
+  "model_max_length": 200,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}