Upload folder using huggingface_hub

checkpoint-22000/1_Pooling/config.json

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+{
+  "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
+}

checkpoint-22000/README.md

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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1830648
+- loss:AnglELoss
+widget:
+- source_sentence: crunchy chips
+  sentences:
+  - big chips spiced gouda
+  - purse
+  - macaroni
+- source_sentence: genuine leather luggage
+  sentences:
+  - janatte luggage
+  - bomb chemise
+  - purse
+- source_sentence: head covers Rashguard
+  sentences:
+  - Double Shaded Blue Clutch
+  - Rashguard
+  - bathing costume
+- source_sentence: hand Made Sweatpants
+  sentences:
+  - acid cleanser
+  - reflective weave sweatpants
+  - rashguard
+- source_sentence: siamy wrap
+  sentences:
+  - siamy
+  - hair revival
+  - backpack
+---
+
+# all-MiniLM-L6-v5-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 256, '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})
+  (2): Normalize()
+)
+```
+
+## 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 = [
+    'siamy wrap',
+    'siamy',
+    'hair revival',
+]
+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.*
+-->
+
+<!--
+## 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 Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `fp16`: 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`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 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`: 2
+- `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`: 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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss | loss   |
+|:------:|:-----:|:-------------:|:------:|
+| 0.0070 | 100   | 16.865        | -      |
+| 0.0140 | 200   | 16.1556       | -      |
+| 0.0210 | 300   | 14.8008       | -      |
+| 0.0280 | 400   | 12.4025       | -      |
+| 0.0350 | 500   | 9.7465        | -      |
+| 0.0420 | 600   | 8.448         | -      |
+| 0.0489 | 700   | 8.1951        | -      |
+| 0.0559 | 800   | 8.1093        | -      |
+| 0.0629 | 900   | 8.0567        | -      |
+| 0.0699 | 1000  | 8.0401        | -      |
+| 0.0769 | 1100  | 7.9491        | -      |
+| 0.0839 | 1200  | 7.9494        | -      |
+| 0.0909 | 1300  | 7.9386        | -      |
+| 0.0979 | 1400  | 7.9033        | -      |
+| 0.1049 | 1500  | 7.9055        | -      |
+| 0.1119 | 1600  | 7.9203        | -      |
+| 0.1189 | 1700  | 7.8381        | -      |
+| 0.1259 | 1800  | 7.8679        | -      |
+| 0.1328 | 1900  | 7.8686        | -      |
+| 0.1398 | 2000  | 7.8252        | -      |
+| 0.1468 | 2100  | 7.856         | -      |
+| 0.1538 | 2200  | 7.8301        | -      |
+| 0.1608 | 2300  | 7.8595        | -      |
+| 0.1678 | 2400  | 7.8138        | -      |
+| 0.1748 | 2500  | 7.812         | -      |
+| 0.1818 | 2600  | 7.8261        | -      |
+| 0.1888 | 2700  | 7.7988        | -      |
+| 0.1958 | 2800  | 7.7965        | -      |
+| 0.2028 | 2900  | 7.783         | -      |
+| 0.2098 | 3000  | 7.7752        | -      |
+| 0.2168 | 3100  | 7.7715        | -      |
+| 0.2237 | 3200  | 7.7903        | -      |
+| 0.2307 | 3300  | 7.7656        | -      |
+| 0.2377 | 3400  | 7.749         | -      |
+| 0.2447 | 3500  | 7.7662        | -      |
+| 0.2517 | 3600  | 7.7492        | -      |
+| 0.2587 | 3700  | 7.737         | -      |
+| 0.2657 | 3800  | 7.7232        | -      |
+| 0.2727 | 3900  | 7.7616        | -      |
+| 0.2797 | 4000  | 7.7391        | -      |
+| 0.2867 | 4100  | 7.7552        | -      |
+| 0.2937 | 4200  | 7.7273        | -      |
+| 0.3007 | 4300  | 7.7216        | -      |
+| 0.3076 | 4400  | 7.7371        | -      |
+| 0.3146 | 4500  | 7.7426        | -      |
+| 0.3216 | 4600  | 7.7406        | -      |
+| 0.3286 | 4700  | 7.712         | -      |
+| 0.3356 | 4800  | 7.7466        | -      |
+| 0.3426 | 4900  | 7.7058        | -      |
+| 0.3496 | 5000  | 7.7139        | 7.6896 |
+| 0.3566 | 5100  | 7.7457        | -      |
+| 0.3636 | 5200  | 7.7172        | -      |
+| 0.3706 | 5300  | 7.739         | -      |
+| 0.3776 | 5400  | 7.7259        | -      |
+| 0.3846 | 5500  | 7.6977        | -      |
+| 0.3916 | 5600  | 7.7237        | -      |
+| 0.3985 | 5700  | 7.7118        | -      |
+| 0.4055 | 5800  | 7.7099        | -      |
+| 0.4125 | 5900  | 7.7142        | -      |
+| 0.4195 | 6000  | 7.6885        | -      |
+| 0.4265 | 6100  | 7.6799        | -      |
+| 0.4335 | 6200  | 7.7039        | -      |
+| 0.4405 | 6300  | 7.6825        | -      |
+| 0.4475 | 6400  | 7.6846        | -      |
+| 0.4545 | 6500  | 7.7078        | -      |
+| 0.4615 | 6600  | 7.6945        | -      |
+| 0.4685 | 6700  | 7.7017        | -      |
+| 0.4755 | 6800  | 7.6781        | -      |
+| 0.4825 | 6900  | 7.6885        | -      |
+| 0.4894 | 7000  | 7.7426        | -      |
+| 0.4964 | 7100  | 7.6809        | -      |
+| 0.5034 | 7200  | 7.6977        | -      |
+| 0.5104 | 7300  | 7.6964        | -      |
+| 0.5174 | 7400  | 7.6834        | -      |
+| 0.5244 | 7500  | 7.6593        | -      |
+| 0.5314 | 7600  | 7.6745        | -      |
+| 0.5384 | 7700  | 7.6587        | -      |
+| 0.5454 | 7800  | 7.6389        | -      |
+| 0.5524 | 7900  | 7.6298        | -      |
+| 0.5594 | 8000  | 7.6693        | -      |
+| 0.5664 | 8100  | 7.6454        | -      |
+| 0.5733 | 8200  | 7.6491        | -      |
+| 0.5803 | 8300  | 7.661         | -      |
+| 0.5873 | 8400  | 7.6525        | -      |
+| 0.5943 | 8500  | 7.6669        | -      |
+| 0.6013 | 8600  | 7.6379        | -      |
+| 0.6083 | 8700  | 7.6706        | -      |
+| 0.6153 | 8800  | 7.6487        | -      |
+| 0.6223 | 8900  | 7.6607        | -      |
+| 0.6293 | 9000  | 7.6334        | -      |
+| 0.6363 | 9100  | 7.6891        | -      |
+| 0.6433 | 9200  | 7.734         | -      |
+| 0.6503 | 9300  | 7.6283        | -      |
+| 0.6573 | 9400  | 7.6461        | -      |
+| 0.6642 | 9500  | 7.623         | -      |
+| 0.6712 | 9600  | 7.6251        | -      |
+| 0.6782 | 9700  | 7.6663        | -      |
+| 0.6852 | 9800  | 7.6376        | -      |
+| 0.6922 | 9900  | 7.6834        | -      |
+| 0.6992 | 10000 | 7.6851        | 7.6099 |
+| 0.7062 | 10100 | 7.6034        | -      |
+| 0.7132 | 10200 | 7.6512        | -      |
+| 0.7202 | 10300 | 7.6413        | -      |
+| 0.7272 | 10400 | 7.6083        | -      |
+| 0.7342 | 10500 | 7.6475        | -      |
+| 0.7412 | 10600 | 7.61          | -      |
+| 0.7481 | 10700 | 7.6404        | -      |
+| 0.7551 | 10800 | 7.6308        | -      |
+| 0.7621 | 10900 | 7.638         | -      |
+| 0.7691 | 11000 | 7.5954        | -      |
+| 0.7761 | 11100 | 7.6037        | -      |
+| 0.7831 | 11200 | 7.6405        | -      |
+| 0.7901 | 11300 | 7.6396        | -      |
+| 0.7971 | 11400 | 7.5898        | -      |
+| 0.8041 | 11500 | 7.644         | -      |
+| 0.8111 | 11600 | 7.639         | -      |
+| 0.8181 | 11700 | 7.6146        | -      |
+| 0.8251 | 11800 | 7.6076        | -      |
+| 0.8321 | 11900 | 7.5997        | -      |
+| 0.8390 | 12000 | 7.6196        | -      |
+| 0.8460 | 12100 | 7.6139        | -      |
+| 0.8530 | 12200 | 7.6335        | -      |
+| 0.8600 | 12300 | 7.6057        | -      |
+| 0.8670 | 12400 | 7.5759        | -      |
+| 0.8740 | 12500 | 7.6044        | -      |
+| 0.8810 | 12600 | 7.589         | -      |
+| 0.8880 | 12700 | 7.5871        | -      |
+| 0.8950 | 12800 | 7.6161        | -      |
+| 0.9020 | 12900 | 7.5797        | -      |
+| 0.9090 | 13000 | 7.6202        | -      |
+| 0.9160 | 13100 | 7.6116        | -      |
+| 0.9229 | 13200 | 7.6253        | -      |
+| 0.9299 | 13300 | 7.5891        | -      |
+| 0.9369 | 13400 | 7.5856        | -      |
+| 0.9439 | 13500 | 7.5824        | -      |
+| 0.9509 | 13600 | 7.6288        | -      |
+| 0.9579 | 13700 | 7.5653        | -      |
+| 0.9649 | 13800 | 7.6073        | -      |
+| 0.9719 | 13900 | 7.5958        | -      |
+| 0.9789 | 14000 | 7.599         | -      |
+| 0.9859 | 14100 | 7.5982        | -      |
+| 0.9929 | 14200 | 7.5634        | -      |
+| 0.9999 | 14300 | 7.5923        | -      |
+| 1.0069 | 14400 | 7.6072        | -      |
+| 1.0138 | 14500 | 7.5589        | -      |
+| 1.0208 | 14600 | 7.6           | -      |
+| 1.0278 | 14700 | 7.5464        | -      |
+| 1.0348 | 14800 | 7.5824        | -      |
+| 1.0418 | 14900 | 7.5528        | -      |
+| 1.0488 | 15000 | 7.568         | 7.5618 |
+| 1.0558 | 15100 | 7.559         | -      |
+| 1.0628 | 15200 | 7.5555        | -      |
+| 1.0698 | 15300 | 7.552         | -      |
+| 1.0768 | 15400 | 7.5851        | -      |
+| 1.0838 | 15500 | 7.5256        | -      |
+| 1.0908 | 15600 | 7.5683        | -      |
+| 1.0977 | 15700 | 7.5909        | -      |
+| 1.1047 | 15800 | 7.5655        | -      |
+| 1.1117 | 15900 | 7.5476        | -      |
+| 1.1187 | 16000 | 7.5721        | -      |
+| 1.1257 | 16100 | 7.5593        | -      |
+| 1.1327 | 16200 | 7.5783        | -      |
+| 1.1397 | 16300 | 7.5905        | -      |
+| 1.1467 | 16400 | 7.542         | -      |
+| 1.1537 | 16500 | 7.5794        | -      |
+| 1.1607 | 16600 | 7.5669        | -      |
+| 1.1677 | 16700 | 7.5738        | -      |
+| 1.1747 | 16800 | 7.5431        | -      |
+| 1.1817 | 16900 | 7.5401        | -      |
+| 1.1886 | 17000 | 7.5629        | -      |
+| 1.1956 | 17100 | 7.5534        | -      |
+| 1.2026 | 17200 | 7.571         | -      |
+| 1.2096 | 17300 | 7.5387        | -      |
+| 1.2166 | 17400 | 7.5596        | -      |
+| 1.2236 | 17500 | 7.5427        | -      |
+| 1.2306 | 17600 | 7.5305        | -      |
+| 1.2376 | 17700 | 7.556         | -      |
+| 1.2446 | 17800 | 7.5442        | -      |
+| 1.2516 | 17900 | 7.5635        | -      |
+| 1.2586 | 18000 | 7.5675        | -      |
+| 1.2656 | 18100 | 7.5412        | -      |
+| 1.2725 | 18200 | 7.5148        | -      |
+| 1.2795 | 18300 | 7.546         | -      |
+| 1.2865 | 18400 | 7.5608        | -      |
+| 1.2935 | 18500 | 7.5269        | -      |
+| 1.3005 | 18600 | 7.5614        | -      |
+| 1.3075 | 18700 | 7.5276        | -      |
+| 1.3145 | 18800 | 7.5586        | -      |
+| 1.3215 | 18900 | 7.5783        | -      |
+| 1.3285 | 19000 | 7.5312        | -      |
+| 1.3355 | 19100 | 7.536         | -      |
+| 1.3425 | 19200 | 7.5497        | -      |
+| 1.3495 | 19300 | 7.5256        | -      |
+| 1.3565 | 19400 | 7.5364        | -      |
+| 1.3634 | 19500 | 7.5311        | -      |
+| 1.3704 | 19600 | 7.5557        | -      |
+| 1.3774 | 19700 | 7.5531        | -      |
+| 1.3844 | 19800 | 7.4844        | -      |
+| 1.3914 | 19900 | 7.5408        | -      |
+| 1.3984 | 20000 | 7.5336        | 7.5100 |
+| 1.4054 | 20100 | 7.524         | -      |
+| 1.4124 | 20200 | 7.5236        | -      |
+| 1.4194 | 20300 | 7.5354        | -      |
+| 1.4264 | 20400 | 7.5228        | -      |
+| 1.4334 | 20500 | 7.5453        | -      |
+| 1.4404 | 20600 | 7.5492        | -      |
+| 1.4474 | 20700 | 7.5145        | -      |
+| 1.4543 | 20800 | 7.5464        | -      |
+| 1.4613 | 20900 | 7.5377        | -      |
+| 1.4683 | 21000 | 7.5221        | -      |
+| 1.4753 | 21100 | 7.5793        | -      |
+| 1.4823 | 21200 | 7.5234        | -      |
+| 1.4893 | 21300 | 7.4922        | -      |
+| 1.4963 | 21400 | 7.5196        | -      |
+| 1.5033 | 21500 | 7.5265        | -      |
+| 1.5103 | 21600 | 7.5251        | -      |
+| 1.5173 | 21700 | 7.4982        | -      |
+| 1.5243 | 21800 | 7.5225        | -      |
+| 1.5313 | 21900 | 7.5725        | -      |
+| 1.5382 | 22000 | 7.5109        | -      |
+
+</details>
+
+### Framework Versions
+- Python: 3.8.10
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.4.1+cu118
+- Accelerate: 1.0.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.3
+
+## 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",
+}
+```
+
+#### AnglELoss
+```bibtex
+@misc{li2023angleoptimized,
+    title={AnglE-optimized Text Embeddings},
+    author={Xianming Li and Jing Li},
+    year={2023},
+    eprint={2309.12871},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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.*
+-->

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