| --- |
| base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| datasets: |
| - yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:70280 |
| - loss:CosineSimilarityLoss |
| widget: |
| - source_sentence: Data SBH tahun 2012 di Mamuju |
| sentences: |
| - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Harmonized System November |
| 2013 |
| - SBH 2012 - Mamuju |
| - IHK di 66 Kota di Indonesia 2013 |
| - source_sentence: Statistik konstruksi tahun 2020 |
| sentences: |
| - Indeks Ketimpangan Gender 2022 |
| - Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020 |
| - Perkembangan Beberapa Indikator Utama sosial-Ekonomi Indonesia Edisi Februari |
| 2016 |
| - source_sentence: Berapa besar inflasi pada bulan Oktober 2008? |
| sentences: |
| - Tinjauan Ekonomi Regional Indonesia Berdasarkan Data PDRB 2004-2008 Buku 2 |
| - Statistik Sumber Daya Laut dan Pesisir 2020 |
| - Inflasi September 2008 sebesar 0,97 persen. |
| - source_sentence: 'Sektor konstruksi Indonesia: data statistik 1990-2013' |
| sentences: |
| - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan |
| Lapangan Pekerjaan Utama, 2023 |
| - Direktori Perusahaan Kehutanan 2019 |
| - Sensus Ekonomi 2006 Hasil Pendaftaran Perusahaan Sumatera Selatan |
| - source_sentence: Perdagangan luar negeri, impor, Oktober 2020 |
| sentences: |
| - Indikator Ekonomi September 2005 |
| - Statistik Potensi Desa Provinsi DI Yogyakarta 2005 |
| - Indikator Ekonomi November 1999 |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: allstats semantic search mini v2 eval |
| type: allstats-semantic-search-mini-v2-eval |
| metrics: |
| - type: pearson_cosine |
| value: 0.9617082550278393 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.8518022238549516 |
| name: Spearman Cosine |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: allstat semantic search mini v2 test |
| type: allstat-semantic-search-mini-v2-test |
| metrics: |
| - type: pearson_cosine |
| value: 0.9604638064122318 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.8480797444308495 |
| 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) on the [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) dataset. 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 8d6b950845285729817bf8e1af1861502c2fed0c --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) |
| <!-- - **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("yahyaabd/allstats-semantic-search-mini-model-v2-2") |
| # Run inference |
| sentences = [ |
| 'Perdagangan luar negeri, impor, Oktober 2020', |
| 'Indikator Ekonomi November 1999', |
| 'Indikator Ekonomi September 2005', |
| ] |
| 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 |
|
|
| * Datasets: `allstats-semantic-search-mini-v2-eval` and `allstat-semantic-search-mini-v2-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | allstats-semantic-search-mini-v2-eval | allstat-semantic-search-mini-v2-test | |
| |:--------------------|:--------------------------------------|:-------------------------------------| |
| | pearson_cosine | 0.9617 | 0.9605 | |
| | **spearman_cosine** | **0.8518** | **0.8481** | |
| |
| <!-- |
| ## 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 |
| |
| #### allstats-semantic-search-synthetic-dataset-v2-mini |
| |
| * Dataset: [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) at [8222b01](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini/tree/8222b01e37490603bc838a6368bc2946a6455a7c) |
| * Size: 70,280 training samples |
| * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 10.92 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.68 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:------------------| |
| | <code>Statistik perusahaan pembudidaya tanaman kehutanan 2018</code> | <code>Statistik Perusahaan Pembudidaya Tanaman Kehutanan 2018</code> | <code>0.97</code> | |
| | <code>Berapa persen pertumbuhan PDB Indonesia pada Triwulan III Tahun 2002?</code> | <code>Inflasi Bulan November 2002 Sebesar 1,85 %</code> | <code>0.0</code> | |
| | <code>Perdagangan luar negeri Indonesia, impor 2019, jilid 2</code> | <code>Pendataan Sapi Potong Sapi Perah (PSPK 2011) Sulawesi Barat</code> | <code>0.06</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" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### allstats-semantic-search-synthetic-dataset-v2-mini |
| |
| * Dataset: [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) at [8222b01](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini/tree/8222b01e37490603bc838a6368bc2946a6455a7c) |
| * Size: 15,060 evaluation samples |
| * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 10.96 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.74 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:----------------------------------------------------------------|:-----------------------------------------------------------------|:------------------| |
| | <code>Review PDRB daerah di Pulau Sumatera 2010-2013</code> | <code>Statistik Pendidikan 2006</code> | <code>0.12</code> | |
| | <code>Analisis data angkatan kerja Agustus 2021</code> | <code>Booklet Survei Angkatan Kerja Nasional Agustus 2021</code> | <code>0.9</code> | |
| | <code>Berapa persen inflasi yang terjadi pada Juli 2015?</code> | <code>Inflasi pada bulan lain tidak disebutkan</code> | <code>0.0</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`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `num_train_epochs`: 24 |
| - `warmup_ratio`: 0.1 |
| - `bf16`: 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`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `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`: 1.0 |
| - `num_train_epochs`: 24 |
| - `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`: True |
| - `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 |
| - `eval_on_start`: False |
| - `eval_use_gather_object`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-mini-v2-eval_spearman_cosine | allstat-semantic-search-mini-v2-test_spearman_cosine | |
| |:-------:|:-----:|:-------------:|:---------------:|:-----------------------------------------------------:|:----------------------------------------------------:| |
| | 0.4550 | 500 | 0.0643 | 0.0413 | 0.6996 | - | |
| | 0.9099 | 1000 | 0.0348 | 0.0280 | 0.7533 | - | |
| | 1.3649 | 1500 | 0.0254 | 0.0238 | 0.7737 | - | |
| | 1.8198 | 2000 | 0.0223 | 0.0205 | 0.7831 | - | |
| | 2.2748 | 2500 | 0.0181 | 0.0197 | 0.7894 | - | |
| | 2.7298 | 3000 | 0.0173 | 0.0184 | 0.7876 | - | |
| | 3.1847 | 3500 | 0.0152 | 0.0170 | 0.7954 | - | |
| | 3.6397 | 4000 | 0.0123 | 0.0175 | 0.7970 | - | |
| | 4.0946 | 4500 | 0.0125 | 0.0163 | 0.8118 | - | |
| | 4.5496 | 5000 | 0.01 | 0.0161 | 0.8047 | - | |
| | 5.0045 | 5500 | 0.0103 | 0.0157 | 0.8126 | - | |
| | 5.4595 | 6000 | 0.0079 | 0.0150 | 0.8224 | - | |
| | 5.9145 | 6500 | 0.0087 | 0.0156 | 0.8219 | - | |
| | 6.3694 | 7000 | 0.0071 | 0.0152 | 0.8145 | - | |
| | 6.8244 | 7500 | 0.0068 | 0.0153 | 0.8172 | - | |
| | 7.2793 | 8000 | 0.0061 | 0.0147 | 0.8216 | - | |
| | 7.7343 | 8500 | 0.0062 | 0.0146 | 0.8267 | - | |
| | 8.1893 | 9000 | 0.0055 | 0.0145 | 0.8325 | - | |
| | 8.6442 | 9500 | 0.005 | 0.0146 | 0.8335 | - | |
| | 9.0992 | 10000 | 0.0052 | 0.0143 | 0.8356 | - | |
| | 9.5541 | 10500 | 0.0043 | 0.0144 | 0.8313 | - | |
| | 10.0091 | 11000 | 0.0051 | 0.0144 | 0.8362 | - | |
| | 10.4641 | 11500 | 0.004 | 0.0145 | 0.8376 | - | |
| | 10.9190 | 12000 | 0.0039 | 0.0142 | 0.8331 | - | |
| | 11.3740 | 12500 | 0.0034 | 0.0141 | 0.8397 | - | |
| | 11.8289 | 13000 | 0.0033 | 0.0140 | 0.8398 | - | |
| | 12.2839 | 13500 | 0.0032 | 0.0143 | 0.8411 | - | |
| | 12.7389 | 14000 | 0.003 | 0.0141 | 0.8407 | - | |
| | 13.1938 | 14500 | 0.0031 | 0.0141 | 0.8379 | - | |
| | 13.6488 | 15000 | 0.0026 | 0.0141 | 0.8419 | - | |
| | 14.1037 | 15500 | 0.0028 | 0.0141 | 0.8442 | - | |
| | 14.5587 | 16000 | 0.0023 | 0.0138 | 0.8455 | - | |
| | 15.0136 | 16500 | 0.0025 | 0.0147 | 0.8359 | - | |
| | 15.4686 | 17000 | 0.0021 | 0.0141 | 0.8459 | - | |
| | 15.9236 | 17500 | 0.0023 | 0.0140 | 0.8433 | - | |
| | 16.3785 | 18000 | 0.002 | 0.0139 | 0.8465 | - | |
| | 16.8335 | 18500 | 0.002 | 0.0139 | 0.8461 | - | |
| | 17.2884 | 19000 | 0.0018 | 0.0139 | 0.8482 | - | |
| | 17.7434 | 19500 | 0.0018 | 0.0138 | 0.8477 | - | |
| | 18.1984 | 20000 | 0.0017 | 0.0138 | 0.8503 | - | |
| | 18.6533 | 20500 | 0.0016 | 0.0136 | 0.8493 | - | |
| | 19.1083 | 21000 | 0.0016 | 0.0139 | 0.8501 | - | |
| | 19.5632 | 21500 | 0.0015 | 0.0138 | 0.8478 | - | |
| | 20.0182 | 22000 | 0.0015 | 0.0139 | 0.8501 | - | |
| | 20.4732 | 22500 | 0.0013 | 0.0139 | 0.8508 | - | |
| | 20.9281 | 23000 | 0.0015 | 0.0139 | 0.8511 | - | |
| | 21.3831 | 23500 | 0.0013 | 0.0139 | 0.8517 | - | |
| | 21.8380 | 24000 | 0.0013 | 0.0139 | 0.8512 | - | |
| | 22.2930 | 24500 | 0.0012 | 0.0139 | 0.8512 | - | |
| | 22.7480 | 25000 | 0.0012 | 0.0138 | 0.8520 | - | |
| | 23.2029 | 25500 | 0.0012 | 0.0139 | 0.8520 | - | |
| | 23.6579 | 26000 | 0.0011 | 0.0139 | 0.8518 | - | |
| | 24.0 | 26376 | - | - | - | 0.8481 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.3.1 |
| - Transformers: 4.44.2 |
| - PyTorch: 2.4.1+cu121 |
| - Accelerate: 0.34.2 |
| - Datasets: 3.2.0 |
| - 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.* |
| --> |
|
|
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| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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