Update README.md

README.md

@@ -4,383 +4,11 @@ tags:
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:8100
+- dataset_size:9000
 - loss:MultipleNegativesRankingLoss
-base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
-widget:
-- source_sentence: Apakah santri boleh keluar pondok saat dikunjungi?
-  sentences:
-  - Cukup menghubungi bagian keuangan atau humas PPS. Imam Syafi'i.
-  - Keluar pondok hanya boleh dengan izin resmi dan keadaan darurat.
-  - Ya, seperti menjadi ketua kelompok, mengatur antrian, dan memimpin doa.
-- source_sentence: Apakah santri boleh membawa HP?
-  sentences:
-  - HP tidak diperbolehkan dibawa ke lingkungan pesantren.
-  - Ya, kurikulum disesuaikan dengan tingkat perkembangan santri.
-  - Santri akan mendapatkan pendampingan psikologis dan konseling.
-- source_sentence: Apakah ada kegiatan kebersihan harian di TK?
-  sentences:
-  - Santri mendapat pembinaan khusus dan apresiasi.
-  - Ya, setiap pagi santri melakukan piket kebersihan lingkungan sesuai jadwal.
-  - Ya, kurikulum disesuaikan dengan tingkat perkembangan santri.
-- source_sentence: Apakah ada buku panduan bagi wali santri baru?
-  sentences:
-  - Wali harus mengajukan surat izin resmi dan mendapat persetujuan pengasuh.
-  - Ekskul dapat diganti satu kali di tengah semester dengan izin wali kelas.
-  - Ya, setiap wali mendapat buku panduan saat pendaftaran.
-- source_sentence: Apakah ekskul dibuka untuk santri baru?
-  sentences:
-  - Ya, santri harus menjaga ketenangan dan mengembalikan buku tepat waktu.
-  - Ya, santri baru dapat langsung mendaftar ekskul di awal semester.
-  - Ya, kurikulum terus dievaluasi dan disesuaikan dengan tantangan era modern.
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
 - pearson_cosine
 - spearman_cosine
-model-index:
-- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
-  results:
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: eval
-      type: eval
-    metrics:
-    - type: pearson_cosine
-      value: .nan
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: .nan
-      name: Spearman Cosine
----
-
-# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
-- **Maximum Sequence Length:** 128 tokens
-- **Output Dimensionality:** 384 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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 384, '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 = [
-    'Apakah ekskul dibuka untuk santri baru?',
-    'Ya, santri baru dapat langsung mendaftar ekskul di awal semester.',
-    'Ya, kurikulum terus dievaluasi dan disesuaikan dengan tantangan era modern.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 384]
-
-# 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: `eval`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value   |
-|:--------------------|:--------|
-| pearson_cosine      | nan     |
-| **spearman_cosine** | **nan** |
-
-<!--
-## 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: 8,100 training samples
-* Columns: <code>sentence_0</code> and <code>sentence_1</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                        | sentence_1                                                                        |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 7 tokens</li><li>mean: 11.19 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 15.87 tokens</li><li>max: 42 tokens</li></ul> |
-* Samples:
-  | sentence_0                                                  | sentence_1                                                                                    |
-  |:------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
-  | <code>Apakah kurikulum mencakup pendidikan karakter?</code> | <code>Ya, pembinaan karakter menjadi bagian utama kurikulum pesantren.</code>                 |
-  | <code>Apakah lingkungan pondok ramah anak?</code>           | <code>Ya, desain dan pengawasan mendukung kenyamanan dan keamanan santri.</code>              |
-  | <code>Apakah nilai adab berpengaruh pada kelulusan?</code>  | <code>Sangat berpengaruh, nilai adab menjadi pertimbangan utama dalam penilaian akhir.</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
-- `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`: 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`: 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
-- `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
-| Epoch  | Step | eval_spearman_cosine |
-|:------:|:----:|:--------------------:|
-| 0.1972 | 100  | nan                  |
-
-
-### Framework Versions
-- Python: 3.11.13
-- Sentence Transformers: 4.1.0
-- Transformers: 4.52.4
-- PyTorch: 2.6.0+cu124
-- Accelerate: 1.7.0
-- Datasets: 2.14.4
-- Tokenizers: 0.21.1
-
-## 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
-
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-## Model Card Authors
-
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