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aleynahukmet
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all-MiniLM-L6-v2-8-layers

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9014210
loss:MSELoss
Eval Results
text-embeddings-inference
Model card Files
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Add new SentenceTransformer model

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Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +592 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +20 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +64 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,592 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:9014210
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+ - loss:MSELoss
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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ widget:
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+ - source_sentence: At an outdoor event in an Asian-themed area, a crowd congregates
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+ as one person in a yellow Chinese dragon costume confronts the camera.
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+ sentences:
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+ - Boy dressed in blue holds a toy.
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+ - the animal is running
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+ - Two young asian men are squatting.
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+ - source_sentence: A man with a shopping cart is studying the shelves in a supermarket
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+ aisle.
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+ sentences:
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+ - The children are watching TV at home.
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+ - Three young boys one is holding a camera and another is holding a green toy all
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+ are wearing t-shirt and smiling.
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+ - A large group of people are gathered outside of a brick building lit with spotlights.
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+ - source_sentence: The door is open.
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+ sentences:
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+ - There are three men in this picture, two are on motorbikes, one of the men has
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+ a large piece of furniture on the back of his bike, the other is about to be handed
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+ a piece of paper by a man in a white shirt.
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+ - People are playing music.
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+ - A girl is using an apple laptop with her headphones in her ears.
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+ - source_sentence: A small group of children are standing in a classroom and one of
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+ them has a foot in a trashcan, which also has a rope leading out of it.
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+ sentences:
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+ - Children are swimming at the beach.
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+ - Women are celebrating at a bar.
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+ - Some men with jerseys are in a bar, watching a soccer match.
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+ - source_sentence: A black dog is drinking next to a brown and white dog that is looking
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+ at an orange ball in the lake, whilst a horse and rider passes behind.
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+ sentences:
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+ - There are two people running around a track in lane three and the one wearing
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+ a blue shirt with a green thing over the eyes is just barely ahead of the guy
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+ wearing an orange shirt and sunglasses.
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+ - A girl is sitting
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+ - the guy is dead
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ - negative_mse
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+ model-index:
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+ - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev
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+ type: sts-dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8649118460865306
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.864870367786895
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+ name: Spearman Cosine
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+ - task:
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+ type: knowledge-distillation
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+ name: Knowledge Distillation
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ metrics:
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+ - type: negative_mse
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+ value: -0.024522081366740167
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+ name: Negative Mse
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts test
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+ type: sts-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8202873084532003
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8190218550432983
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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+
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+ 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.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
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+ - **Maximum Sequence Length:** 256 tokens
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+ - **Output Dimensionality:** 384 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
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+ (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})
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+ (2): Normalize()
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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
129
+
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+ ```bash
131
+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can load this model and run inference.
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("aleynahukmet/all-MiniLM-L6-v2-8-layers")
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+ # Run inference
141
+ sentences = [
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+ 'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.',
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+ 'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.',
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+ 'the guy is dead',
145
+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 384]
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+
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+ #### Semantic Similarity
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+
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+ * Datasets: `sts-dev` and `sts-test`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | sts-dev | sts-test |
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+ |:--------------------|:-----------|:----------|
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+ | pearson_cosine | 0.8649 | 0.8203 |
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+ | **spearman_cosine** | **0.8649** | **0.819** |
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+
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+ #### Knowledge Distillation
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+
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+ * Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
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+
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+ | Metric | Value |
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+ |:-----------------|:------------|
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+ | **negative_mse** | **-0.0245** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 9,014,210 training samples
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+ * Columns: <code>sentence</code> and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence | label |
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+ |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
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+ | type | string | list |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
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+ * Samples:
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+ | sentence | label |
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+ |:---------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>A person on a horse jumps over a broken down airplane.</code> | <code>[-0.009216307662427425, 0.003964003175497055, 0.04029734805226326, 0.0030935262329876423, -0.03516044840216637, ...]</code> |
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+ | <code>Children smiling and waving at camera</code> | <code>[-0.03215238079428673, 0.06086821109056473, 0.013251038268208504, -0.017755677923560143, 0.07927625626325607, ...]</code> |
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+ | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>[-0.020561737939715385, -0.03641558438539505, -0.039370208978652954, -0.0975518748164177, 0.005307587794959545, ...]</code> |
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+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
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+
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+ ### Evaluation Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 10,000 evaluation samples
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+ * Columns: <code>sentence</code> and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence | label |
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+ |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
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+ | type | string | list |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
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+ * Samples:
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+ | sentence | label |
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+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>Two women are embracing while holding to go packages.</code> | <code>[-0.007923883385956287, -0.024198176339268684, 0.034445445984601974, 0.036053989082574844, -0.06740871071815491, ...]</code> |
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+ | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>[-0.08869566023349762, 0.02789478376507759, 0.060685668140649796, -0.02580258436501026, 0.008359752595424652, ...]</code> |
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+ | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>[0.027255145832896233, 0.07622072845697403, 0.025504805147647858, -0.0542026124894619, -0.052822694182395935, ...]</code> |
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+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
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+
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+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
258
+
259
+ - `eval_strategy`: steps
260
+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `learning_rate`: 0.0001
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+ - `num_train_epochs`: 1
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
270
+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 0.0001
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 1
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 0
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `restore_callback_states_from_checkpoint`: False
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: True
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: True
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
349
+ - `push_to_hub`: False
350
+ - `resume_from_checkpoint`: None
351
+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
353
+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
355
+ - `gradient_checkpointing`: False
356
+ - `gradient_checkpointing_kwargs`: None
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+ - `include_inputs_for_metrics`: False
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+ - `eval_do_concat_batches`: True
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+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
361
+ - `push_to_hub_organization`: None
362
+ - `mp_parameters`:
363
+ - `auto_find_batch_size`: False
364
+ - `full_determinism`: False
365
+ - `torchdynamo`: None
366
+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
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+ - `torch_compile_backend`: None
370
+ - `torch_compile_mode`: None
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+ - `dispatch_batches`: None
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+ - `split_batches`: None
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+ - `include_tokens_per_second`: False
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+ - `include_num_input_tokens_seen`: False
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+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
377
+ - `batch_eval_metrics`: False
378
+ - `eval_on_start`: False
379
+ - `eval_use_gather_object`: False
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+ - `prompts`: None
381
+ - `batch_sampler`: batch_sampler
382
+ - `multi_dataset_batch_sampler`: proportional
383
+
384
+ </details>
385
+
386
+ ### Training Logs
387
+ <details><summary>Click to expand</summary>
388
+
389
+ | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | negative_mse | sts-test_spearman_cosine |
390
+ |:---------:|:----------:|:-------------:|:---------------:|:-----------------------:|:------------:|:------------------------:|
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+ | 0 | 0 | - | - | 0.7048 | -0.3846 | - |
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+ | 0.0071 | 1000 | 0.0032 | - | - | - | - |
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+ | 0.0142 | 2000 | 0.0023 | - | - | - | - |
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+ | 0.0213 | 3000 | 0.0019 | - | - | - | - |
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+ | 0.0284 | 4000 | 0.0017 | - | - | - | - |
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+ | 0.0355 | 5000 | 0.0015 | 0.0013 | 0.8149 | -0.1309 | - |
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+ | 0.0426 | 6000 | 0.0014 | - | - | - | - |
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+ | 0.0497 | 7000 | 0.0012 | - | - | - | - |
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+ | 0.0568 | 8000 | 0.0011 | - | - | - | - |
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+ | 0.0639 | 9000 | 0.001 | - | - | - | - |
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+ | 0.0710 | 10000 | 0.001 | 0.0008 | 0.8495 | -0.0754 | - |
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+ | 0.0781 | 11000 | 0.0009 | - | - | - | - |
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+ | 0.0852 | 12000 | 0.0008 | - | - | - | - |
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+ | 0.0923 | 13000 | 0.0008 | - | - | - | - |
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+ | 0.0994 | 14000 | 0.0007 | - | - | - | - |
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+ | 0.1065 | 15000 | 0.0007 | 0.0005 | 0.8569 | -0.0528 | - |
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+ | 0.1136 | 16000 | 0.0007 | - | - | - | - |
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+ | 0.1207 | 17000 | 0.0007 | - | - | - | - |
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+ | 0.1278 | 18000 | 0.0006 | - | - | - | - |
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+ | 0.1349 | 19000 | 0.0006 | - | - | - | - |
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+ | 0.1420 | 20000 | 0.0006 | 0.0004 | 0.8589 | -0.0438 | - |
412
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+ | **0.994** | **140000** | **0.0004** | **0.0002** | **0.8649** | **-0.0245** | **-** |
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+ | 1.0 | 140848 | - | - | - | - | 0.8190 |
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+
534
+ * The bold row denotes the saved checkpoint.
535
+ </details>
536
+
537
+ ### Framework Versions
538
+ - Python: 3.12.4
539
+ - Sentence Transformers: 3.3.1
540
+ - Transformers: 4.44.2
541
+ - PyTorch: 2.4.1+cu121
542
+ - Accelerate: 1.0.1
543
+ - Datasets: 2.19.0
544
+ - Tokenizers: 0.19.1
545
+
546
+ ## Citation
547
+
548
+ ### BibTeX
549
+
550
+ #### Sentence Transformers
551
+ ```bibtex
552
+ @inproceedings{reimers-2019-sentence-bert,
553
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
554
+ author = "Reimers, Nils and Gurevych, Iryna",
555
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
556
+ month = "11",
557
+ year = "2019",
558
+ publisher = "Association for Computational Linguistics",
559
+ url = "https://arxiv.org/abs/1908.10084",
560
+ }
561
+ ```
562
+
563
+ #### MSELoss
564
+ ```bibtex
565
+ @inproceedings{reimers-2020-multilingual-sentence-bert,
566
+ title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
567
+ author = "Reimers, Nils and Gurevych, Iryna",
568
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
569
+ month = "11",
570
+ year = "2020",
571
+ publisher = "Association for Computational Linguistics",
572
+ url = "https://arxiv.org/abs/2004.09813",
573
+ }
574
+ ```
575
+
576
+ <!--
577
+ ## Glossary
578
+
579
+ *Clearly define terms in order to be accessible across audiences.*
580
+ -->
581
+
582
+ <!--
583
+ ## Model Card Authors
584
+
585
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
586
+ -->
587
+
588
+ <!--
589
+ ## Model Card Contact
590
+
591
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
592
+ -->
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