| --- |
| tags: |
| - sentence-transformers |
| - sparse-encoder |
| - sparse |
| - splade |
| - generated_from_trainer |
| - dataset_size:1298215 |
| - loss:SpladeLoss |
| - loss:SparseMultipleNegativesRankingLoss |
| - loss:FlopsLoss |
| base_model: distilbert/distilbert-base-multilingual-cased |
| widget: |
| - text: '[Clan London] Women''s Logo Tote Bag Olive (Olive) | حقيبة توت بشعار العلامة |
| التجارية للنساء، زيتي (زيتي). Category: Bags > Shoppers & Totes.' |
| - text: '[Fire & Glory] Women''s Animal Print Midi Dress Light Gray (Light Gray) | |
| فستان ميدي بأكمام طويلة وطبعة جرافيك للنساء، أسود (رمادي فاتح). Category: Dresses |
| > Midi Dresses.' |
| - text: '[melissa] Melissa Becky AD Pink (Pink) | ميليسا بيكي إيه دي (وردي). Category: |
| Shoes > Slides & Flip-Flops.' |
| - text: '[H&M] Curvy Ultra High-Rise Wide-Leg Jeans (Pastel Blue) | جينز كيرفي بخصر |
| مرتفع جداً وأرجل واسعة (أزرق باستيل). Category: Denim > Jeans.' |
| - text: '[Splash Basics] Flexi Comfort Drawstring A-line Midi Skirt Blue (Blue) | |
| الرباط ألف خط تنورة ميدي (أزرق). Category: Bottoms > Skirts.' |
| pipeline_tag: feature-extraction |
| library_name: sentence-transformers |
| --- |
| |
| # SPLADE Sparse Encoder |
|
|
| This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the pairs and triplets datasets using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 119547-dimensional sparse vector space and can be used for semantic search and sparse retrieval. |
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** SPLADE Sparse Encoder |
| - **Base model:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 119547 dimensions |
| - **Similarity Function:** Dot Product |
| - **Supported Modality:** Text |
| - **Training Datasets:** |
| - pairs |
| - triplets |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SparseEncoder( |
| (0): Transformer({'transformer_task': 'fill-mask', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'token_embeddings', 'architecture': 'DistilBertForMaskedLM'}) |
| (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'embedding_dimension': 119547}) |
| ) |
| ``` |
|
|
| ## 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 SparseEncoder |
| |
| # Download from the 🤗 Hub |
| model = SparseEncoder("sparse_encoder_model_id") |
| # Run inference |
| sentences = [ |
| 'شنطة زهّانة', |
| '[PANDORA] Aquarius Zodiac Charm (Silver) | دلاية برج الدلو (فضي). Category: > Accessories.', |
| "[Hurley] Women's Crew Neck Solid Sweatshirt Dark Salmon (Dark Salmon) | سويت شيرت نسائي سادة بياقة دائرية، برتقالي (سلموني غامق). Category: Hoodies & Sweatshirt > Sweatshirts.", |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 119547] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[ 11.9930, 13.0235, 7.8521], |
| # [ 13.0235, 491.0651, 65.4858], |
| # [ 7.8521, 65.4858, 246.8788]]) |
| ``` |
| <!-- |
| ### 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.* |
| --> |
|
|
| <!-- |
| ## 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 Datasets |
|
|
| #### pairs |
|
|
| * Dataset: pairs |
| * Size: 1,292,307 training samples |
| * Columns: <code>query</code> and <code>positive</code> |
| * Approximate statistics based on the first 100 samples: |
| | | query | positive | |
| |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | type | string | string | |
| | modality | text | text | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 10.33 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 51.14 tokens</li><li>max: 124 tokens</li></ul> | |
| * Samples: |
| | query | positive | |
| |:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>men's watch analog</code> | <code>[CASIO] Leather Strap Analog Watch (Mint Green) \| ساعة انالوج جلد اصلي (أخضر نعناعي). Category: > .</code> | |
| | <code>black hoodie women</code> | <code>[Tribe of 6] Women's Logo Hooded Sweatshirt Black (Black) \| سويت شيرت نسائي سادة بأكمام طويلة وغطاء رأس، أسود (أسود). Category: Hoodies & Sweatshirt > Hoodies.</code> | |
| | <code>وشاح مخطط للنساء</code> | <code>[MANGO] Geometric Stripe Scarf Beige (Beige) \| وشاح بنقوش هندسية مخططة (بيج). Category: Accessories > Scarves.</code> | |
| * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: |
| ```json |
| { |
| "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)", |
| "document_regularizer_weight": 3e-05, |
| "query_regularizer_weight": 5e-05 |
| } |
| ``` |
|
|
| #### triplets |
|
|
| * Dataset: triplets |
| * Size: 5,908 training samples |
| * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 100 samples: |
| | | query | positive | negative | |
| |:---------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | modality | text | text | text | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 8.75 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 57.33 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 54.78 tokens</li><li>max: 88 tokens</li></ul> | |
| * Samples: |
| | query | positive | negative | |
| |:----------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>don't call me jennyfer jumpsuits & bodysuits</code> | <code>[Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits.</code> | <code>[Ashita Fernandes] Kenza Cupro V-Neck Jumpsuit Royal Blue (Facebook Blue) \| كينزا جمبسوت كوبرو بفتحة رقبة V أزرق ملكي (أزرق فيسبوك). Category: Jumpsuits & Bodysuits > Jumpsuits.</code> | |
| | <code>don't call me jennyfer jumpsuits & bodysuits</code> | <code>[Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits.</code> | <code>[Splash Intimates] Mesh Panel Shaping Bodysuit (Black) \| بودي سوت مشد بأجزاء شبكية (أسود). Category: Jumpsuits & Bodysuits > Body Suits.</code> | |
| | <code>don't call me jennyfer jumpsuits & bodysuits</code> | <code>[Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits.</code> | <code>[Ashita Fernandes] Kenza Cupro V-Neck Jumpsuit Royal Blue (Facebook Blue) \| كينزا جمبسوت كوبرو بفتحة رقبة V أزرق ملكي (أزرق فيسبوك). Category: Jumpsuits & Bodysuits > Jumpsuits.</code> | |
| * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: |
| ```json |
| { |
| "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)", |
| "document_regularizer_weight": 3e-05, |
| "query_regularizer_weight": 5e-05 |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 16 |
| - `num_train_epochs`: 1 |
| - `learning_rate`: 2e-05 |
| - `warmup_steps`: 0.1 |
| - `gradient_accumulation_steps`: 4 |
| - `bf16`: True |
| - `dataloader_num_workers`: 2 |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `per_device_train_batch_size`: 16 |
| - `num_train_epochs`: 1 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.1 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 4 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: True |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: None |
| - `trackio_bucket_id`: None |
| - `trackio_static_space_id`: None |
| - `per_device_eval_batch_size`: 8 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: False |
| - `hub_private_repo`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 2 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_static_graph`: None |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: None |
| - `fsdp_config`: None |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | |
| |:------:|:-----:|:-------------:| |
| | 0.0099 | 200 | 679.9589 | |
| | 0.0197 | 400 | 1.7857 | |
| | 0.0296 | 600 | 1.0238 | |
| | 0.0394 | 800 | 0.8982 | |
| | 0.0493 | 1000 | 0.8221 | |
| | 0.0592 | 1200 | 0.7875 | |
| | 0.0690 | 1400 | 0.7405 | |
| | 0.0789 | 1600 | 0.7131 | |
| | 0.0887 | 1800 | 0.6761 | |
| | 0.0986 | 2000 | 0.6732 | |
| | 0.1085 | 2200 | 0.6397 | |
| | 0.1183 | 2400 | 0.6303 | |
| | 0.1282 | 2600 | 0.6021 | |
| | 0.1380 | 2800 | 0.5794 | |
| | 0.1479 | 3000 | 0.5552 | |
| | 0.1578 | 3200 | 0.5638 | |
| | 0.1676 | 3400 | 0.5633 | |
| | 0.1775 | 3600 | 0.5550 | |
| | 0.1873 | 3800 | 0.5473 | |
| | 0.1972 | 4000 | 0.5281 | |
| | 0.2070 | 4200 | 0.5189 | |
| | 0.2169 | 4400 | 0.5282 | |
| | 0.2268 | 4600 | 0.5080 | |
| | 0.2366 | 4800 | 0.5046 | |
| | 0.2465 | 5000 | 0.5111 | |
| | 0.2563 | 5200 | 0.4957 | |
| | 0.2662 | 5400 | 0.4932 | |
| | 0.2761 | 5600 | 0.5045 | |
| | 0.2859 | 5800 | 0.5019 | |
| | 0.2958 | 6000 | 0.4789 | |
| | 0.3056 | 6200 | 0.4866 | |
| | 0.3155 | 6400 | 0.4810 | |
| | 0.3254 | 6600 | 0.4891 | |
| | 0.3352 | 6800 | 0.4800 | |
| | 0.3451 | 7000 | 0.4659 | |
| | 0.3549 | 7200 | 0.4612 | |
| | 0.3648 | 7400 | 0.4644 | |
| | 0.3747 | 7600 | 0.4364 | |
| | 0.3845 | 7800 | 0.4672 | |
| | 0.3944 | 8000 | 0.4546 | |
| | 0.4042 | 8200 | 0.4631 | |
| | 0.4141 | 8400 | 0.4565 | |
| | 0.4240 | 8600 | 0.4242 | |
| | 0.4338 | 8800 | 0.4425 | |
| | 0.4437 | 9000 | 0.4597 | |
| | 0.4535 | 9200 | 0.4292 | |
| | 0.4634 | 9400 | 0.4406 | |
| | 0.4733 | 9600 | 0.4331 | |
| | 0.4831 | 9800 | 0.4342 | |
| | 0.4930 | 10000 | 0.4483 | |
| | 0.5028 | 10200 | 0.4254 | |
| | 0.5127 | 10400 | 0.4182 | |
| | 0.5226 | 10600 | 0.4173 | |
| | 0.5324 | 10800 | 0.4120 | |
| | 0.5423 | 11000 | 0.4197 | |
| | 0.5521 | 11200 | 0.4029 | |
| | 0.5620 | 11400 | 0.4016 | |
| | 0.5719 | 11600 | 0.4159 | |
| | 0.5817 | 11800 | 0.4033 | |
| | 0.5916 | 12000 | 0.4250 | |
| | 0.6014 | 12200 | 0.4013 | |
| | 0.6113 | 12400 | 0.4234 | |
| | 0.6211 | 12600 | 0.4103 | |
| | 0.6310 | 12800 | 0.4046 | |
| | 0.6409 | 13000 | 0.4123 | |
| | 0.6507 | 13200 | 0.4074 | |
| | 0.6606 | 13400 | 0.4098 | |
| | 0.6704 | 13600 | 0.4192 | |
| | 0.6803 | 13800 | 0.3887 | |
| | 0.6902 | 14000 | 0.4017 | |
| | 0.7000 | 14200 | 0.3956 | |
| | 0.7099 | 14400 | 0.3971 | |
| | 0.7197 | 14600 | 0.3964 | |
| | 0.7296 | 14800 | 0.3921 | |
| | 0.7395 | 15000 | 0.3947 | |
| | 0.7493 | 15200 | 0.3795 | |
| | 0.7592 | 15400 | 0.3813 | |
| | 0.7690 | 15600 | 0.3931 | |
| | 0.7789 | 15800 | 0.3792 | |
| | 0.7888 | 16000 | 0.3878 | |
| | 0.7986 | 16200 | 0.3703 | |
| | 0.8085 | 16400 | 0.3836 | |
| | 0.8183 | 16600 | 0.3871 | |
| | 0.8282 | 16800 | 0.3751 | |
| | 0.8381 | 17000 | 0.4005 | |
| | 0.8479 | 17200 | 0.3887 | |
| | 0.8578 | 17400 | 0.3873 | |
| | 0.8676 | 17600 | 0.3811 | |
| | 0.8775 | 17800 | 0.3841 | |
| | 0.8874 | 18000 | 0.3841 | |
| | 0.8972 | 18200 | 0.3701 | |
| | 0.9071 | 18400 | 0.3913 | |
| | 0.9169 | 18600 | 0.3848 | |
| | 0.9268 | 18800 | 0.3885 | |
| | 0.9367 | 19000 | 0.3648 | |
| | 0.9465 | 19200 | 0.3842 | |
| | 0.9564 | 19400 | 0.3989 | |
| | 0.9662 | 19600 | 0.3930 | |
| | 0.9761 | 19800 | 0.3944 | |
| | 0.9860 | 20000 | 0.3712 | |
| | 0.9958 | 20200 | 0.3762 | |
|
|
| </details> |
|
|
| ### Training Time |
| - **Training**: 48.5 minutes |
|
|
| ### Framework Versions |
| - Python: 3.11.12 |
| - Sentence Transformers: 5.6.0 |
| - Transformers: 5.12.1 |
| - PyTorch: 2.7.0+cu128 |
| - Accelerate: 1.14.0 |
| - Datasets: 5.0.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## Additional Resources |
|
|
| - [Training and Finetuning Sparse Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sparse-encoder): the end-to-end guide for training or finetuning SPLADE and other sparse encoder models. |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### SpladeLoss |
| ```bibtex |
| @misc{formal2022distillationhardnegativesampling, |
| title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, |
| author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, |
| year={2022}, |
| eprint={2205.04733}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.IR}, |
| url={https://arxiv.org/abs/2205.04733}, |
| } |
| ``` |
|
|
| #### SparseMultipleNegativesRankingLoss |
| ```bibtex |
| @misc{oord2019representationlearningcontrastivepredictive, |
| title={Representation Learning with Contrastive Predictive Coding}, |
| author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, |
| year={2019}, |
| eprint={1807.03748}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/1807.03748}, |
| } |
| ``` |
|
|
| #### FlopsLoss |
| ```bibtex |
| @article{paria2020minimizing, |
| title={Minimizing flops to learn efficient sparse representations}, |
| author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, |
| journal={arXiv preprint arXiv:2004.05665}, |
| year={2020} |
| } |
| ``` |
|
|
| <!-- |
| ## 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.* |
| --> |
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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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