sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:4532 loss:CoSENTLoss arxiv:1908.10084 base_model:BAAI/bge-m3 base_model:finetune:BAAI/bge-m3 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-m3
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:4532
- loss:CoSENTLoss
widget:
- source_sentence: портативный проектор umiio a 008
  sentences:
  - портативный проектор philips a 008
  - logitech c270i iptv
  - детский электромобиль sundays land rover jj012
- source_sentence: запчасти для швейных машин bernette
  sentences:
  - мфу samsung m428fdw
  - запасные части для швейной машины bernette
  - steelseries apex pro mini wireless
- source_sentence: сушильная машина maunfeld mfdm1410wh06
  sentences:
  - кухонные уголки
  - сушильная машина simens mfdm1410wh06
  - сетевой удлинитель евро eu-4 multi-protection 4usb qy-923 2500w
- source_sentence: монитор acer k242hql
  sentences:
  - multiflashlight armytek zippy green
  - роутер mi router 4c r4cm dvb4231gl
  - монитор acer k224hql
- source_sentence: набор моя первая кухня
  sentences:
  - кухонные наборы
  - ea sports fc 23 ps4
  - da vinci белая
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.9701810342203735
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9168792089469636
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9695654298959763
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.9165761310923896
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9696385323216731
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.9166348972420479
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9631206697635591
      name: Pearson Dot
    - type: spearman_dot
      value: 0.9173046326579305
      name: Spearman Dot
    - type: pearson_max
      value: 0.9701810342203735
      name: Pearson Max
    - type: spearman_max
      value: 0.9173046326579305
      name: Spearman Max
---

# SentenceTransformer based on BAAI/bge-m3

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision babcf60cae0a1f438d7ade582983d4ba462303c2 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("seregadgl101/test_bge_2_10ep")
# Run inference
sentences = [
    'набор моя первая кухня',
    'кухонные наборы',
    'ea sports fc 23 ps4',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9702     |
| **spearman_cosine** | **0.9169** |
| pearson_manhattan   | 0.9696     |
| spearman_manhattan  | 0.9166     |
| pearson_euclidean   | 0.9696     |
| spearman_euclidean  | 0.9166     |
| pearson_dot         | 0.9631     |
| spearman_dot        | 0.9173     |
| pearson_max         | 0.9702     |
| spearman_max        | 0.9173     |

<!--
## 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: 4,532 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: 4 tokens</li><li>mean: 14.45 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.09 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.6</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                    | sentence2                                                    | score            |
  |:-------------------------------------------------------------|:-------------------------------------------------------------|:-----------------|
  | <code>батут evo jump internal 12ft</code>                    | <code>батут evo jump internal 12ft</code>                    | <code>1.0</code> |
  | <code>наручные часы orient casual</code>                     | <code>наручные часы orient</code>                            | <code>1.0</code> |
  | <code>электрический духовой шкаф weissgauff eov 19 mw</code> | <code>электрический духовой шкаф weissgauff eov 19 mx</code> | <code>0.4</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 504 evaluation 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: 4 tokens</li><li>mean: 14.93 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.1 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.59</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                     | sentence2                                               | score            |
  |:------------------------------------------------------------------------------|:--------------------------------------------------------|:-----------------|
  | <code>потолочный светильник yeelight smart led ceiling light c2001s500</code> | <code>yeelight smart led ceiling light c2001s500</code> | <code>1.0</code> |
  | <code>канцелярские принадлежности</code>                                      | <code>канцелярские принадлежности разные</code>         | <code>0.4</code> |
  | <code>usb-магнитола acv avs-1718g</code>                                      | <code>автомагнитола acv avs-1718g</code>                | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `save_only_model`: True
- `seed`: 33
- `fp16`: True
- `load_best_model_at_end`: 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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `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`: True
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 33
- `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`: 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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch      | Step     | Training Loss | loss       | sts-dev_spearman_cosine |
|:----------:|:--------:|:-------------:|:----------:|:-----------------------:|
| 0.0882     | 50       | -             | 2.7444     | 0.4991                  |
| 0.1764     | 100      | -             | 2.5535     | 0.6093                  |
| 0.2646     | 150      | -             | 2.3365     | 0.6761                  |
| 0.3527     | 200      | -             | 2.1920     | 0.7247                  |
| 0.4409     | 250      | -             | 2.2210     | 0.7446                  |
| 0.5291     | 300      | -             | 2.1432     | 0.7610                  |
| 0.6173     | 350      | -             | 2.2488     | 0.7769                  |
| 0.7055     | 400      | -             | 2.3736     | 0.7749                  |
| 0.7937     | 450      | -             | 2.0688     | 0.7946                  |
| 0.8818     | 500      | 2.3647        | 2.5331     | 0.7879                  |
| 0.9700     | 550      | -             | 2.1087     | 0.7742                  |
| 1.0582     | 600      | -             | 2.1302     | 0.8068                  |
| 1.1464     | 650      | -             | 2.2669     | 0.8114                  |
| 1.2346     | 700      | -             | 2.0269     | 0.8039                  |
| 1.3228     | 750      | -             | 2.2095     | 0.8138                  |
| 1.4109     | 800      | -             | 2.5288     | 0.8190                  |
| 1.4991     | 850      | -             | 2.3442     | 0.8222                  |
| 1.5873     | 900      | -             | 2.3759     | 0.8289                  |
| 1.6755     | 950      | -             | 2.1893     | 0.8280                  |
| 1.7637     | 1000     | 2.0682        | 2.0056     | 0.8426                  |
| 1.8519     | 1050     | -             | 2.0832     | 0.8527                  |
| 1.9400     | 1100     | -             | 2.0336     | 0.8515                  |
| 2.0282     | 1150     | -             | 2.0571     | 0.8591                  |
| 2.1164     | 1200     | -             | 2.1516     | 0.8565                  |
| 2.2046     | 1250     | -             | 2.2035     | 0.8602                  |
| 2.2928     | 1300     | -             | 2.5294     | 0.8513                  |
| 2.3810     | 1350     | -             | 2.4177     | 0.8647                  |
| 2.4691     | 1400     | -             | 2.1630     | 0.8709                  |
| 2.5573     | 1450     | -             | 2.1279     | 0.8661                  |
| 2.6455     | 1500     | 1.678         | 2.1639     | 0.8744                  |
| 2.7337     | 1550     | -             | 2.2592     | 0.8799                  |
| 2.8219     | 1600     | -             | 2.2288     | 0.8822                  |
| 2.9101     | 1650     | -             | 2.2427     | 0.8831                  |
| 2.9982     | 1700     | -             | 2.4380     | 0.8776                  |
| 3.0864     | 1750     | -             | 2.1689     | 0.8826                  |
| 3.1746     | 1800     | -             | 1.8099     | 0.8868                  |
| 3.2628     | 1850     | -             | 2.0881     | 0.8832                  |
| 3.3510     | 1900     | -             | 2.0785     | 0.8892                  |
| 3.4392     | 1950     | -             | 2.2512     | 0.8865                  |
| 3.5273     | 2000     | 1.2168        | 2.1249     | 0.8927                  |
| 3.6155     | 2050     | -             | 2.1179     | 0.8950                  |
| 3.7037     | 2100     | -             | 2.1932     | 0.8973                  |
| 3.7919     | 2150     | -             | 2.2628     | 0.8967                  |
| 3.8801     | 2200     | -             | 2.0764     | 0.8972                  |
| 3.9683     | 2250     | -             | 1.9575     | 0.9012                  |
| 4.0564     | 2300     | -             | 2.3302     | 0.8985                  |
| 4.1446     | 2350     | -             | 2.3008     | 0.8980                  |
| 4.2328     | 2400     | -             | 2.2886     | 0.8968                  |
| 4.3210     | 2450     | -             | 2.1694     | 0.8973                  |
| 4.4092     | 2500     | 1.0851        | 2.1102     | 0.9010                  |
| 4.4974     | 2550     | -             | 2.2596     | 0.9021                  |
| 4.5855     | 2600     | -             | 2.1944     | 0.9019                  |
| 4.6737     | 2650     | -             | 2.0728     | 0.9029                  |
| 4.7619     | 2700     | -             | 2.4573     | 0.9031                  |
| 4.8501     | 2750     | -             | 2.2306     | 0.9057                  |
| 4.9383     | 2800     | -             | 2.2637     | 0.9068                  |
| 5.0265     | 2850     | -             | 2.5110     | 0.9068                  |
| 5.1146     | 2900     | -             | 2.6613     | 0.9042                  |
| 5.2028     | 2950     | -             | 2.4713     | 0.9070                  |
| 5.2910     | 3000     | 0.8143        | 2.3709     | 0.9082                  |
| 5.3792     | 3050     | -             | 2.6083     | 0.9058                  |
| 5.4674     | 3100     | -             | 2.5377     | 0.9044                  |
| 5.5556     | 3150     | -             | 2.3146     | 0.9071                  |
| 5.6437     | 3200     | -             | 2.2603     | 0.9085                  |
| 5.7319     | 3250     | -             | 2.5842     | 0.9068                  |
| 5.8201     | 3300     | -             | 2.6045     | 0.9093                  |
| 5.9083     | 3350     | -             | 2.6207     | 0.9103                  |
| 5.9965     | 3400     | -             | 2.5992     | 0.9098                  |
| 6.0847     | 3450     | -             | 2.7799     | 0.9090                  |
| 6.1728     | 3500     | 0.5704        | 2.7198     | 0.9098                  |
| 6.2610     | 3550     | -             | 2.9783     | 0.9089                  |
| 6.3492     | 3600     | -             | 2.4165     | 0.9120                  |
| 6.4374     | 3650     | -             | 2.4488     | 0.9122                  |
| 6.5256     | 3700     | -             | 2.6764     | 0.9113                  |
| 6.6138     | 3750     | -             | 2.5327     | 0.9130                  |
| 6.7019     | 3800     | -             | 2.5875     | 0.9129                  |
| 6.7901     | 3850     | -             | 2.7036     | 0.9130                  |
| 6.8783     | 3900     | -             | 2.7566     | 0.9120                  |
| 6.9665     | 3950     | -             | 2.5488     | 0.9127                  |
| 7.0547     | 4000     | 0.4287        | 2.8512     | 0.9127                  |
| 7.1429     | 4050     | -             | 2.7361     | 0.9128                  |
| 7.2310     | 4100     | -             | 2.7434     | 0.9135                  |
| 7.3192     | 4150     | -             | 2.9410     | 0.9129                  |
| 7.4074     | 4200     | -             | 2.9452     | 0.9126                  |
| 7.4956     | 4250     | -             | 2.8665     | 0.9140                  |
| 7.5838     | 4300     | -             | 2.8215     | 0.9145                  |
| 7.6720     | 4350     | -             | 2.6978     | 0.9147                  |
| 7.7601     | 4400     | -             | 2.8445     | 0.9143                  |
| 7.8483     | 4450     | -             | 2.6041     | 0.9155                  |
| 7.9365     | 4500     | 0.3099        | 2.7219     | 0.9155                  |
| 8.0247     | 4550     | -             | 2.7180     | 0.9160                  |
| 8.1129     | 4600     | -             | 2.6906     | 0.9160                  |
| 8.2011     | 4650     | -             | 2.8628     | 0.9156                  |
| 8.2892     | 4700     | -             | 2.7820     | 0.9158                  |
| 8.3774     | 4750     | -             | 2.8457     | 0.9157                  |
| 8.4656     | 4800     | -             | 2.7286     | 0.9160                  |
| 8.5538     | 4850     | -             | 2.7131     | 0.9164                  |
| 8.6420     | 4900     | -             | 2.8368     | 0.9165                  |
| 8.7302     | 4950     | -             | 2.8033     | 0.9167                  |
| 8.8183     | 5000     | 0.2342        | 2.7307     | 0.9169                  |
| 8.9065     | 5050     | -             | 2.8483     | 0.9167                  |
| 8.9947     | 5100     | -             | 2.9736     | 0.9167                  |
| 9.0829     | 5150     | -             | 2.9151     | 0.9168                  |
| 9.1711     | 5200     | -             | 2.9375     | 0.9167                  |
| 9.2593     | 5250     | -             | 2.9968     | 0.9168                  |
| 9.3474     | 5300     | -             | 3.0024     | 0.9167                  |
| 9.4356     | 5350     | -             | 2.9444     | 0.9167                  |
| 9.5238     | 5400     | -             | 2.9477     | 0.9167                  |
| 9.6120     | 5450     | -             | 2.9205     | 0.9168                  |
| **9.7002** | **5500** | **0.1639**    | **2.9286** | **0.9167**              |
| 9.7884     | 5550     | -             | 2.9421     | 0.9168                  |
| 9.8765     | 5600     | -             | 2.9733     | 0.9168                  |
| 9.9647     | 5650     | -             | 2.9777     | 0.9169                  |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

<!--
## 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.*
-->