quarkss
/

indobert-base-stsb

@@ -1,479 +1,490 @@
----
-base_model: indobenchmark/indobert-base-p2
-datasets: []
-language: []
-library_name: sentence-transformers
-metrics:
-- pearson_cosine
-- spearman_cosine
-- pearson_manhattan
-- spearman_manhattan
-- pearson_euclidean
-- spearman_euclidean
-- pearson_dot
-- spearman_dot
-- pearson_max
-- spearman_max
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:5749
-- loss:CosineSimilarityLoss
-widget:
-- source_sentence: Dua ekor anjing berenang di kolam renang.
-  sentences:
-  - Anjing-anjing sedang berenang di kolam renang.
-  - Seekor binatang sedang berjalan di atas tanah.
-  - Seorang pria sedang menyeka pinggiran mangkuk.
-- source_sentence: Seorang anak perempuan sedang mengiris mentega menjadi dua bagian.
-  sentences:
-  - Seorang wanita sedang mengiris tahu.
-  - Dua orang berkelahi.
-  - Seorang pria sedang menari.
-- source_sentence: Seorang gadis sedang makan kue mangkuk.
-  sentences:
-  - Seorang pria sedang mengiris bawang putih dengan alat pengiris mandolin.
-  - Seorang pria sedang memotong dan memotong bawang.
-  - Seorang wanita sedang makan kue mangkuk.
-- source_sentence: Sebuah helikopter mendarat di landasan helikopter.
-  sentences:
-  - Seorang pria sedang mengiris mentimun.
-  - Seorang pria sedang memotong batang pohon dengan kapak.
-  - Sebuah helikopter mendarat.
-- source_sentence: Seorang pria sedang berjalan dengan seekor kuda.
-  sentences:
-  - Seorang pria sedang menuntun seekor kuda dengan tali kekang.
-  - Seorang pria sedang menembakkan pistol.
-  - Seorang wanita sedang memetik tomat.
-model-index:
-- name: SentenceTransformer based on indobenchmark/indobert-base-p2
-  results:
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: Unknown
-      type: unknown
-    metrics:
-    - type: pearson_cosine
-      value: 0.8577280779646681
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8588776334781149
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.8315261521874587
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.8355406849443783
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.8318083198603524
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.8359194889385243
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.7767060276322824
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.783607744137448
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8577280779646681
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8588776334781149
-      name: Spearman Max
-    - type: pearson_cosine
-      value: 0.8122790124383042
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8123119892530147
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.7987643661729152
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.7966661480553803
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.7992882233155829
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.797227936168015
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.712195542080357
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.7014898656834544
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.8122790124383042
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.8123119892530147
-      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:** 512 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': 512, '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("quarkss/indobert-base-stsb")
-# Run inference
-sentences = [
-    'Seorang pria sedang berjalan dengan seekor kuda.',
-    'Seorang pria sedang menuntun seekor kuda dengan tali kekang.',
-    'Seorang pria sedang menembakkan pistol.',
-]
-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
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.8577     |
-| **spearman_cosine** | **0.8589** |
-| pearson_manhattan   | 0.8315     |
-| spearman_manhattan  | 0.8355     |
-| pearson_euclidean   | 0.8318     |
-| spearman_euclidean  | 0.8359     |
-| pearson_dot         | 0.7767     |
-| spearman_dot        | 0.7836     |
-| pearson_max         | 0.8577     |
-| spearman_max        | 0.8589     |
-#### Semantic Similarity
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-| Metric             | Value      |
-|:-------------------|:-----------|
-| pearson_cosine     | 0.8123     |
-| spearman_cosine    | 0.8123     |
-| pearson_manhattan  | 0.7988     |
-| spearman_manhattan | 0.7967     |
-| pearson_euclidean  | 0.7993     |
-| spearman_euclidean | 0.7972     |
-| pearson_dot        | 0.7122     |
-| spearman_dot       | 0.7015     |
-| pearson_max        | 0.8123     |
-| **spearman_max**   | **0.8123** |
-<!--
-## 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: 5,749 training samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
-  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                           | string                                                                           | float                                                          |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 9.65 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.59 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence1                                                              | sentence2                                                                                | score             |
-  |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------|
-  | <code>Sebuah pesawat sedang lepas landas.</code>                       | <code>Sebuah pesawat terbang sedang lepas landas.</code>                                 | <code>1.0</code>  |
-  | <code>Seorang pria sedang memainkan seruling besar.</code>             | <code>Seorang pria sedang memainkan seruling.</code>                                     | <code>0.76</code> |
-  | <code>Seorang pria sedang mengoleskan keju parut di atas pizza.</code> | <code>Seorang pria sedang mengoleskan keju parut di atas pizza yang belum matang.</code> | <code>0.76</code> |
-* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
-  ```json
-  {
-      "loss_fct": "torch.nn.modules.loss.MSELoss"
-  }
-  ```
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `learning_rate`: 2e-05
-- `weight_decay`: 0.01
-- `num_train_epochs`: 5
-- `warmup_ratio`: 0.1
-- `fp16`: True
-#### 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
-- `learning_rate`: 2e-05
-- `weight_decay`: 0.01
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 5
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.1
-- `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`: 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
-- `eval_on_start`: False
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
-</details>
-### Training Logs
-| Epoch  | Step | Training Loss | spearman_cosine | spearman_max |
-|:------:|:----:|:-------------:|:---------------:|:------------:|
-| 0.2778 | 100  | 0.0615        | -               | -            |
-| 0.5556 | 200  | 0.0336        | -               | -            |
-| 0.8333 | 300  | 0.0331        | -               | -            |
-| 1.1111 | 400  | 0.0235        | -               | -            |
-| 1.3889 | 500  | 0.018         | 0.8472          | -            |
-| 1.6667 | 600  | 0.0164        | -               | -            |
-| 1.9444 | 700  | 0.0159        | -               | -            |
-| 2.2222 | 800  | 0.0097        | -               | -            |
-| 2.5    | 900  | 0.0085        | -               | -            |
-| 2.7778 | 1000 | 0.0084        | 0.8563          | -            |
-| 3.0556 | 1100 | 0.0076        | -               | -            |
-| 3.3333 | 1200 | 0.0056        | -               | -            |
-| 3.6111 | 1300 | 0.0054        | -               | -            |
-| 3.8889 | 1400 | 0.0052        | -               | -            |
-| 4.1667 | 1500 | 0.0047        | 0.8589          | -            |
-| 4.4444 | 1600 | 0.0045        | -               | -            |
-| 4.7222 | 1700 | 0.004         | -               | -            |
-| 5.0    | 1800 | 0.0042        | -               | 0.8123       |
-### Framework Versions
-- Python: 3.10.13
-- Sentence Transformers: 3.0.1
-- Transformers: 4.42.4
-- PyTorch: 2.0.1+cu117
-- Accelerate: 0.32.1
-- Datasets: 2.17.0
-- 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",
-}
-```
-<!--
-## 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.*
 -->

+---
+base_model: indobenchmark/indobert-base-p2
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:5749
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: Dua ekor anjing berenang di kolam renang.
+  sentences:
+  - Anjing-anjing sedang berenang di kolam renang.
+  - Seekor binatang sedang berjalan di atas tanah.
+  - Seorang pria sedang menyeka pinggiran mangkuk.
+- source_sentence: Seorang anak perempuan sedang mengiris mentega menjadi dua bagian.
+  sentences:
+  - Seorang wanita sedang mengiris tahu.
+  - Dua orang berkelahi.
+  - Seorang pria sedang menari.
+- source_sentence: Seorang gadis sedang makan kue mangkuk.
+  sentences:
+  - Seorang pria sedang mengiris bawang putih dengan alat pengiris mandolin.
+  - Seorang pria sedang memotong dan memotong bawang.
+  - Seorang wanita sedang makan kue mangkuk.
+- source_sentence: Sebuah helikopter mendarat di landasan helikopter.
+  sentences:
+  - Seorang pria sedang mengiris mentimun.
+  - Seorang pria sedang memotong batang pohon dengan kapak.
+  - Sebuah helikopter mendarat.
+- source_sentence: Seorang pria sedang berjalan dengan seekor kuda.
+  sentences:
+  - Seorang pria sedang menuntun seekor kuda dengan tali kekang.
+  - Seorang pria sedang menembakkan pistol.
+  - Seorang wanita sedang memetik tomat.
+model-index:
+- name: SentenceTransformer based on indobenchmark/indobert-base-p2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: pearson_cosine
+      value: 0.8577280779646681
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8588776334781149
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8315261521874587
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8355406849443783
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8318083198603524
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8359194889385243
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.7767060276322824
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.783607744137448
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8577280779646681
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8588776334781149
+      name: Spearman Max
+    - type: pearson_cosine
+      value: 0.8122790124383042
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8123119892530147
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.7987643661729152
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.7966661480553803
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.7992882233155829
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.797227936168015
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.712195542080357
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.7014898656834544
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8122790124383042
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8123119892530147
+      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.
+## STSB Test
+|                                         |   Spearman Correlation |
+|:----------------------------------------|-----------------------:|
+| models/indobert-large-stsb              |                 0.8366 |
+| models/indobert-base-stsb               |                 0.8123 |
+| sentence-transformers/all-MiniLM-L6-v2  |                 0.5952 |
+| indobenchmark/indobert-large-p2         |                 0.5673 |
+| sentence-transformers/all-mpnet-base-v2 |                 0.5531 |
+| sentence-transformers/stsb-bert-base    |                 0.5349 |
+| indobenchmark/indobert-base-p2          |                 0.5309 |
+## 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:** 512 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': 512, '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("quarkss/indobert-base-stsb")
+# Run inference
+sentences = [
+    'Seorang pria sedang berjalan dengan seekor kuda.',
+    'Seorang pria sedang menuntun seekor kuda dengan tali kekang.',
+    'Seorang pria sedang menembakkan pistol.',
+]
+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
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8577     |
+| **spearman_cosine** | **0.8589** |
+| pearson_manhattan   | 0.8315     |
+| spearman_manhattan  | 0.8355     |
+| pearson_euclidean   | 0.8318     |
+| spearman_euclidean  | 0.8359     |
+| pearson_dot         | 0.7767     |
+| spearman_dot        | 0.7836     |
+| pearson_max         | 0.8577     |
+| spearman_max        | 0.8589     |
+#### Semantic Similarity
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | 0.8123     |
+| spearman_cosine    | 0.8123     |
+| pearson_manhattan  | 0.7988     |
+| spearman_manhattan | 0.7967     |
+| pearson_euclidean  | 0.7993     |
+| spearman_euclidean | 0.7972     |
+| pearson_dot        | 0.7122     |
+| spearman_dot       | 0.7015     |
+| pearson_max        | 0.8123     |
+| **spearman_max**   | **0.8123** |
+<!--
+## 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: 5,749 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 9.65 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.59 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                              | sentence2                                                                                | score             |
+  |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------|
+  | <code>Sebuah pesawat sedang lepas landas.</code>                       | <code>Sebuah pesawat terbang sedang lepas landas.</code>                                 | <code>1.0</code>  |
+  | <code>Seorang pria sedang memainkan seruling besar.</code>             | <code>Seorang pria sedang memainkan seruling.</code>                                     | <code>0.76</code> |
+  | <code>Seorang pria sedang mengoleskan keju parut di atas pizza.</code> | <code>Seorang pria sedang mengoleskan keju parut di atas pizza yang belum matang.</code> | <code>0.76</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `fp16`: True
+#### 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
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: 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
+- `eval_on_start`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+</details>
+### Training Logs
+| Epoch  | Step | Training Loss | spearman_cosine | spearman_max |
+|:------:|:----:|:-------------:|:---------------:|:------------:|
+| 0.2778 | 100  | 0.0615        | -               | -            |
+| 0.5556 | 200  | 0.0336        | -               | -            |
+| 0.8333 | 300  | 0.0331        | -               | -            |
+| 1.1111 | 400  | 0.0235        | -               | -            |
+| 1.3889 | 500  | 0.018         | 0.8472          | -            |
+| 1.6667 | 600  | 0.0164        | -               | -            |
+| 1.9444 | 700  | 0.0159        | -               | -            |
+| 2.2222 | 800  | 0.0097        | -               | -            |
+| 2.5    | 900  | 0.0085        | -               | -            |
+| 2.7778 | 1000 | 0.0084        | 0.8563          | -            |
+| 3.0556 | 1100 | 0.0076        | -               | -            |
+| 3.3333 | 1200 | 0.0056        | -               | -            |
+| 3.6111 | 1300 | 0.0054        | -               | -            |
+| 3.8889 | 1400 | 0.0052        | -               | -            |
+| 4.1667 | 1500 | 0.0047        | 0.8589          | -            |
+| 4.4444 | 1600 | 0.0045        | -               | -            |
+| 4.7222 | 1700 | 0.004         | -               | -            |
+| 5.0    | 1800 | 0.0042        | -               | 0.8123       |
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.0.1+cu117
+- Accelerate: 0.32.1
+- Datasets: 2.17.0
+- 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",
+}
+```
+<!--
+## 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.*
 -->