| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:2602 |
| - loss:ContrastiveLoss |
| base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| widget: |
| - source_sentence: Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah |
| (triliun) 2010 |
| sentences: |
| - 'Nilai Ekspor Menurut Pelabuhan Utama (Nilai FOB: juta US$) 2000-2023' |
| - Suhu Minimum, Rata-Rata, dan Maksimum di Stasiun Pengamatan BMKG (oC), 2011-2015 |
| - 'Nilai Ekspor Menurut Negara Tujuan Utama (Nilai FOB: juta US$), 2000-2023' |
| - source_sentence: Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah |
| (triliun) 2010 |
| sentences: |
| - Tabungan Bruto, Investasi Nonfinansial, dan Pinjaman Neto Triwulanan Sektor Pemerintahan |
| Umum (triliun rupiah), 2009-2015 |
| - Produksi Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2020 |
| - Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur |
| (ribu rupiah), 2017 |
| - source_sentence: Gaji bersih vs kelompok umur dan lapangan pekerjaan, 2023 |
| sentences: |
| - Investasi Nonfinansial Menurut Sektor (triliun rupiah), 2008-2014 |
| - Posisi Kredit Usaha Mikro, Kecil, dan Menengah (UMKM) 1 pada Bank Umum (miliar |
| rupiah), 2012-2016 |
| - Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah) |
| - source_sentence: Data utang luar negeri Indonesia (pemerintah dan BI), detail kreditor |
| dan syarat, tahun 2010 |
| sentences: |
| - Angka Partisipasi Sekolah (APS) Penduduk Umur 7-18 Tahun Menurut Klasifikasi Desa, |
| Jenis Kelamin, dan Kelompok Umur, 2009-2023 |
| - Indeks Integritas Ujian Nasional |
| - Rekapitulasi Luas Penutupan Lahan Hutan dan Non Hutan Menurut Provinsi Tahun 2014-2022 |
| (Ribu Ha) |
| - source_sentence: Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015 |
| sentences: |
| - Indeks Harga Konsumen Menurut Kelompok Pengeluaran, 2020-2023 |
| - Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah) |
| - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan |
| dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023 |
| datasets: |
| - yahyaabd/bps-statictable-query-title-pairs |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: allstats semantic base v1 eval |
| type: allstats-semantic-base-v1-eval |
| metrics: |
| - type: pearson_cosine |
| value: 0.8898188833771716 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.779923841631983 |
| name: Spearman Cosine |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: allstat semantic base v1 test |
| type: allstat-semantic-base-v1-test |
| metrics: |
| - type: pearson_cosine |
| value: 0.9039024076661341 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.8077065435723709 |
| name: Spearman Cosine |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) dataset. 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:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) |
| <!-- - **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: XLMRobertaModel |
| (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("yahyaabd/allstats-ir-mpnet-base-v1") |
| # Run inference |
| sentences = [ |
| 'Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015', |
| 'Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)', |
| 'Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023', |
| ] |
| 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 |
|
|
| * Datasets: `allstats-semantic-base-v1-eval` and `allstat-semantic-base-v1-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test | |
| |:--------------------|:-------------------------------|:------------------------------| |
| | pearson_cosine | 0.8898 | 0.9039 | |
| | **spearman_cosine** | **0.7799** | **0.8077** | |
| |
| <!-- |
| ## 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 |
| |
| #### bps-statictable-query-title-pairs |
| |
| * Dataset: [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) at [c7df38f](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs/tree/c7df38f8b228efe13b1589b94c78fc7b57f02b58) |
| * Size: 2,602 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 | int | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 18.35 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.83 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>0: ~66.50%</li><li>1: ~33.50%</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Pertumbuhan populasi provinsi di Indonesia 1971-2024</code> | <code>Kecepatan Angin dan Kelembaban di Stasiun Pengamatan BMKG, 2000-2010</code> | <code>0</code> | |
| | <code>Perbandingan upah nominal dan riil pekerja pertanian di Indonesia (tahun dasar 2012), periode 2017.</code> | <code>Upah Nominal dan Riil Buruh Tani di Indonesia (Rupiah), 2009-2019 (2012=100)</code> | <code>1</code> | |
| | <code>Laporan singkat cash flow statement Q4/2005</code> | <code>Nilai Produksi dan Biaya Produksi per Hektar Usaha Tanaman Bawang Merah dan Cabai Merah, 2014</code> | <code>0</code> | |
| * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
| ```json |
| { |
| "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
| "margin": 0.5, |
| "size_average": true |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### bps-statictable-query-title-pairs |
| |
| * Dataset: [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) at [c7df38f](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs/tree/c7df38f8b228efe13b1589b94c78fc7b57f02b58) |
| * Size: 558 evaluation samples |
| * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 558 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 18.45 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.04 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>0: ~70.97%</li><li>1: ~29.03%</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Data pengeluaran makanan rata-rata warga Sulteng per minggu di tahun 2022, berdasarkan kelompok pendapatan</code> | <code>Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84)</code> | <code>0</code> | |
| | <code>Konsumsi & belanja makanan per orang di NTB, beda kelompok pengeluaran, 2021</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Nusa Tenggara Barat, 2018-2023</code> | <code>1</code> | |
| | <code>Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Penduduk Berumur 15 Tahun Ke Atas Menurut Provinsi dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008 - 2024</code> | <code>0</code> | |
| * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
| ```json |
| { |
| "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
| "margin": 0.5, |
| "size_average": true |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 32 |
| - `num_train_epochs`: 4 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
| - `load_best_model_at_end`: True |
| - `eval_on_start`: 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`: 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 |
| - `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`: 4 |
| - `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`: True |
| - `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`: True |
| - `use_liger_kernel`: False |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine | |
| |:----------:|:-------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:| |
| | 0 | 0 | - | 0.0099 | 0.7449 | - | |
| | 0.1220 | 10 | 0.0091 | 0.0065 | 0.7640 | - | |
| | 0.2439 | 20 | 0.0059 | 0.0040 | 0.7743 | - | |
| | 0.3659 | 30 | 0.0045 | 0.0036 | 0.7688 | - | |
| | 0.4878 | 40 | 0.0045 | 0.0036 | 0.7694 | - | |
| | 0.6098 | 50 | 0.0032 | 0.0037 | 0.7758 | - | |
| | 0.7317 | 60 | 0.003 | 0.0025 | 0.7753 | - | |
| | 0.8537 | 70 | 0.0035 | 0.0029 | 0.7710 | - | |
| | 0.9756 | 80 | 0.0028 | 0.0026 | 0.7745 | - | |
| | 1.0976 | 90 | 0.0015 | 0.0023 | 0.7754 | - | |
| | 1.2195 | 100 | 0.0013 | 0.0021 | 0.7760 | - | |
| | 1.3415 | 110 | 0.0013 | 0.0022 | 0.7751 | - | |
| | 1.4634 | 120 | 0.002 | 0.0021 | 0.7746 | - | |
| | 1.5854 | 130 | 0.0012 | 0.0020 | 0.7750 | - | |
| | 1.7073 | 140 | 0.0007 | 0.0019 | 0.7740 | - | |
| | 1.8293 | 150 | 0.0008 | 0.0019 | 0.7738 | - | |
| | 1.9512 | 160 | 0.0026 | 0.0018 | 0.7772 | - | |
| | 2.0732 | 170 | 0.0009 | 0.0019 | 0.7785 | - | |
| | 2.1951 | 180 | 0.0005 | 0.0020 | 0.7781 | - | |
| | 2.3171 | 190 | 0.0009 | 0.0017 | 0.7777 | - | |
| | 2.4390 | 200 | 0.0005 | 0.0017 | 0.7773 | - | |
| | 2.5610 | 210 | 0.0004 | 0.0018 | 0.7766 | - | |
| | 2.6829 | 220 | 0.0006 | 0.0018 | 0.7762 | - | |
| | 2.8049 | 230 | 0.0006 | 0.0019 | 0.7756 | - | |
| | 2.9268 | 240 | 0.0016 | 0.0019 | 0.7777 | - | |
| | 3.0488 | 250 | 0.0008 | 0.0018 | 0.7796 | - | |
| | 3.1707 | 260 | 0.0005 | 0.0017 | 0.7802 | - | |
| | **3.2927** | **270** | **0.0006** | **0.0017** | **0.7802** | **-** | |
| | 3.4146 | 280 | 0.0004 | 0.0017 | 0.7805 | - | |
| | 3.5366 | 290 | 0.0004 | 0.0017 | 0.7805 | - | |
| | 3.6585 | 300 | 0.0003 | 0.0018 | 0.7802 | - | |
| | 3.7805 | 310 | 0.0006 | 0.0018 | 0.7800 | - | |
| | 3.9024 | 320 | 0.0003 | 0.0018 | 0.7799 | - | |
| | -1 | -1 | - | - | - | 0.8077 | |
|
|
| * The bold row denotes the saved checkpoint. |
|
|
| ### 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", |
| } |
| ``` |
|
|
| #### ContrastiveLoss |
| ```bibtex |
| @inproceedings{hadsell2006dimensionality, |
| author={Hadsell, R. and Chopra, S. and LeCun, Y.}, |
| booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, |
| title={Dimensionality Reduction by Learning an Invariant Mapping}, |
| year={2006}, |
| volume={2}, |
| number={}, |
| pages={1735-1742}, |
| doi={10.1109/CVPR.2006.100} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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| ## Model Card Authors |
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| *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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