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Decius
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sft_model_project

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Files changed (14) hide show
  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +583 -3
  4. config.json +26 -0
  5. config_sentence_transformers.json +10 -0
  6. config_setfit.json +18 -0
  7. model.safetensors +3 -0
  8. model_head.pkl +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. special_tokens_map.json +51 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +64 -0
  14. unigram.json +3 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ unigram.json filter=lfs diff=lfs merge=lfs -text
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 CHANGED
@@ -1,3 +1,583 @@
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- ---
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- license: unknown
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: Modifier l’adresse du magasin
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+ - text: أحذف المنتج من المخزن
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+ - text: I want to change the store address
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+ - text: I want to create a website that sells books
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+ - text: What stores do we have?
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.9743589743589743
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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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:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 128 tokens
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+ - **Number of Classes:** 13 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | delete_category | <ul><li>'Supprimer une catégorie'</li><li>'I want to delete a product category'</li><li>'Remove a category from the list'</li></ul> |
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+ | delete_product | <ul><li>'I want to delete the red t-shirt'</li><li>'Remove this item from inventory'</li><li>'Supprimer un produit'</li></ul> |
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+ | greet-who_are_you | <ul><li>'how can you help me'</li><li>"pourquoi j'ai besoin de toi"</li><li>'je ne te comprends pas'</li></ul> |
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+ | create_website | <ul><li>'أريد إنشاء موقع إلكتروني لمتجر الملابس الخاص بي'</li><li>'ساعدني في تصميم موقع أعمالي الخاصة بالتدريب الرياضي'</li><li>'ساعدني في إنشاء موقع لمطعمي'</li></ul> |
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+ | read_category | <ul><li>'Can I see all the categories?'</li><li>'What categories are available?'</li><li>'Affiche-moi les catégories'</li></ul> |
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+ | update_store | <ul><li>'Update store information'</li><li>'Modify the store contact details'</li><li>'Je veux changer les coordonnées du magasin'</li></ul> |
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+ | update_category | <ul><li>'Je veux changer le nom d’une catégorie'</li><li>'Can I rename a category?'</li><li>'Update the category name to something else'</li></ul> |
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+ | greet-good_bye | <ul><li>'See you later'</li><li>'A plus tard'</li><li>'stop'</li></ul> |
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+ | update_product | <ul><li>'I want to change product details'</li><li>'Je veux modifier un produit'</li><li>'Edit the product information'</li></ul> |
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+ | read_product | <ul><li>'Can I see the available items?'</li><li>'List the products'</li><li>'Affiche tous les produits'</li></ul> |
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+ | delete_store | <ul><li>'Remove store number 3'</li><li>'Supprimer un magasin'</li><li>'Can I delete an existing store?'</li></ul> |
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+ | read_store | <ul><li>'Quels sont les magasins disponibles ?'</li><li>'List all registered stores'</li><li>'Show me the list of stores'</li></ul> |
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+ | greet-hi | <ul><li>'Hello buddy'</li><li>'Salut'</li><li>'Hey'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.9744 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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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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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("Decius/sft_model_project")
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+ # Run inference
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+ preds = model("أحذف المنتج من المخزن")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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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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+ <!--
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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 Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 1 | 5.7846 | 13 |
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+
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+ | Label | Training Sample Count |
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+ |:------------------|:----------------------|
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+ | greet-hi | 5 |
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+ | greet-who_are_you | 7 |
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+ | greet-good_bye | 5 |
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+ | create_website | 21 |
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+ | read_category | 3 |
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+ | update_category | 3 |
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+ | delete_category | 3 |
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+ | read_product | 3 |
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+ | update_product | 3 |
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+ | delete_product | 3 |
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+ | read_store | 3 |
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+ | update_store | 3 |
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+ | delete_store | 3 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (4, 4)
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+ - num_epochs: (4, 4)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - l2_weight: 0.01
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+ - seed: 42
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+ - evaluation_strategy: epoch
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0011 | 1 | 0.1475 | - |
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+ | 0.0111 | 10 | 0.1345 | - |
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+ | 0.0222 | 20 | 0.0807 | - |
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+ | 0.0333 | 30 | 0.0943 | - |
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+ | 0.0444 | 40 | 0.0785 | - |
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+ | 0.0555 | 50 | 0.1016 | - |
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+ | 0.0666 | 60 | 0.0756 | - |
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+ | 0.0777 | 70 | 0.0775 | - |
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+ | 0.0888 | 80 | 0.0368 | - |
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+ | 0.0999 | 90 | 0.0635 | - |
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+ | 0.1110 | 100 | 0.0395 | - |
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+ | 0.1221 | 110 | 0.0279 | - |
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+ | 0.1332 | 120 | 0.0217 | - |
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+ | 0.1443 | 130 | 0.0254 | - |
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+ | 0.1554 | 140 | 0.0406 | - |
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+ | 0.1665 | 150 | 0.0143 | - |
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+ | 0.1776 | 160 | 0.0482 | - |
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+ | 0.1887 | 170 | 0.042 | - |
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+ | 0.1998 | 180 | 0.0286 | - |
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+ | 0.2109 | 190 | 0.012 | - |
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+ | 0.2220 | 200 | 0.0258 | - |
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+ | 0.2331 | 210 | 0.0193 | - |
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+ | 0.2442 | 220 | 0.0126 | - |
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+ | 0.2553 | 230 | 0.0342 | - |
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+ | 0.2664 | 240 | 0.0238 | - |
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+ | 0.2775 | 250 | 0.0111 | - |
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+ | 0.2886 | 260 | 0.0101 | - |
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+ | 0.2997 | 270 | 0.0099 | - |
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+ | 0.3108 | 280 | 0.0208 | - |
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+ | 0.3219 | 290 | 0.0089 | - |
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+ | 0.3330 | 300 | 0.0276 | - |
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+ | 0.3441 | 310 | 0.0099 | - |
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+ | 0.3552 | 320 | 0.0191 | - |
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+ | 0.3663 | 330 | 0.0199 | - |
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+ | 0.3774 | 340 | 0.0095 | - |
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+ | 0.3885 | 350 | 0.0142 | - |
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+ | 0.3996 | 360 | 0.0083 | - |
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+ | 0.4107 | 370 | 0.0079 | - |
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+ | 0.4218 | 380 | 0.0072 | - |
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+ | 0.4329 | 390 | 0.0098 | - |
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+ | 0.4440 | 400 | 0.01 | - |
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+ | 0.4550 | 410 | 0.0084 | - |
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+ | 0.4661 | 420 | 0.0024 | - |
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+ | 0.4772 | 430 | 0.0176 | - |
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+ | 0.4883 | 440 | 0.0068 | - |
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+ | 0.4994 | 450 | 0.0209 | - |
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+ | 0.5105 | 460 | 0.0038 | - |
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+ | 0.5216 | 470 | 0.0063 | - |
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+ | 0.5327 | 480 | 0.034 | - |
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+ | 0.5438 | 490 | 0.0191 | - |
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+ | 0.5549 | 500 | 0.0159 | - |
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+ | 0.5660 | 510 | 0.0088 | - |
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+ | 0.5771 | 520 | 0.0032 | - |
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+ | 0.5882 | 530 | 0.0045 | - |
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+ | 0.5993 | 540 | 0.0192 | - |
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+ | 0.6104 | 550 | 0.0123 | - |
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+ | 0.6215 | 560 | 0.0048 | - |
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+ | 0.6326 | 570 | 0.0068 | - |
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+ | 0.6437 | 580 | 0.0036 | - |
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+ | 0.6548 | 590 | 0.0123 | - |
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+ | 0.6659 | 600 | 0.0104 | - |
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+ | 0.6770 | 610 | 0.0023 | - |
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+ | 0.6881 | 620 | 0.0062 | - |
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+ | 0.6992 | 630 | 0.0048 | - |
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+ | 0.7103 | 640 | 0.0063 | - |
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+ | 0.7214 | 650 | 0.0012 | - |
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+ | 0.7325 | 660 | 0.0026 | - |
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+ | 0.7436 | 670 | 0.0136 | - |
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+ | 0.7547 | 680 | 0.0144 | - |
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+ | 0.7658 | 690 | 0.0045 | - |
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+ | 0.7769 | 700 | 0.0013 | - |
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+ | 0.7880 | 710 | 0.0058 | - |
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+ | 0.7991 | 720 | 0.0056 | - |
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+ | 0.8102 | 730 | 0.004 | - |
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+ | 0.8213 | 740 | 0.0023 | - |
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+ | 0.8324 | 750 | 0.0047 | - |
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+ | 0.8435 | 760 | 0.001 | - |
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+ | 0.8546 | 770 | 0.0028 | - |
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+ | 0.8657 | 780 | 0.0042 | - |
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+ | 0.8768 | 790 | 0.0016 | - |
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+ | 0.8879 | 800 | 0.002 | - |
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+ | 0.8990 | 810 | 0.0004 | - |
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+ | 0.9101 | 820 | 0.0034 | - |
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+ | 0.9212 | 830 | 0.0016 | - |
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+ | 0.9323 | 840 | 0.0076 | - |
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+ | 0.9434 | 850 | 0.0021 | - |
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+ | 0.9545 | 860 | 0.0027 | - |
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+ | 0.9656 | 870 | 0.0017 | - |
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+ | 0.9767 | 880 | 0.0024 | - |
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+ | 0.9878 | 890 | 0.0014 | - |
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+ | 0.9989 | 900 | 0.0015 | - |
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+ | 1.0 | 901 | - | 0.0316 |
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+ | 1.0100 | 910 | 0.0014 | - |
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+ | 1.0211 | 920 | 0.0009 | - |
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+ | 1.0322 | 930 | 0.0015 | - |
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+ | 1.0433 | 940 | 0.0023 | - |
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+ | 1.0544 | 950 | 0.0004 | - |
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+ | 1.0655 | 960 | 0.0006 | - |
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+ | 1.0766 | 970 | 0.001 | - |
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+ | 1.0877 | 980 | 0.0005 | - |
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+ | 1.0988 | 990 | 0.0044 | - |
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+ | 1.1099 | 1000 | 0.0011 | - |
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+ | 1.1210 | 1010 | 0.0008 | - |
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+ | 1.1321 | 1020 | 0.0008 | - |
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+ | 1.1432 | 1030 | 0.0007 | - |
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+ | 1.1543 | 1040 | 0.0004 | - |
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+ | 1.1654 | 1050 | 0.0009 | - |
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+ | 1.1765 | 1060 | 0.0017 | - |
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+ | 1.1876 | 1070 | 0.002 | - |
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+ | 1.1987 | 1080 | 0.0008 | - |
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+ | 1.2098 | 1090 | 0.002 | - |
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+ | 1.2209 | 1100 | 0.0005 | - |
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+ | 1.2320 | 1110 | 0.0012 | - |
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+ | 1.2431 | 1120 | 0.002 | - |
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+ | 1.2542 | 1130 | 0.0012 | - |
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+ | 1.2653 | 1140 | 0.0025 | - |
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+ | 1.2764 | 1150 | 0.0008 | - |
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+ | 1.2875 | 1160 | 0.0009 | - |
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+ | 1.2986 | 1170 | 0.0011 | - |
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+ | 1.3097 | 1180 | 0.0004 | - |
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+ | 1.3208 | 1190 | 0.001 | - |
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+ | 1.3319 | 1200 | 0.0008 | - |
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+ | 1.3430 | 1210 | 0.0005 | - |
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+ | 1.3541 | 1220 | 0.0006 | - |
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+ | 1.3651 | 1230 | 0.0007 | - |
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+ | 1.3762 | 1240 | 0.0009 | - |
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+ | 1.3873 | 1250 | 0.0008 | - |
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+ | 1.3984 | 1260 | 0.0009 | - |
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+ | 1.4095 | 1270 | 0.0009 | - |
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+ | 1.4206 | 1280 | 0.0008 | - |
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+ | 1.4317 | 1290 | 0.0007 | - |
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+ | 1.4428 | 1300 | 0.001 | - |
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+ | 1.4539 | 1310 | 0.0004 | - |
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+ | 1.4650 | 1320 | 0.0004 | - |
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+ | 1.4761 | 1330 | 0.0008 | - |
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+ | 1.4872 | 1340 | 0.0003 | - |
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+ | 1.4983 | 1350 | 0.0004 | - |
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+ | 1.5094 | 1360 | 0.0096 | - |
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+ | 1.5205 | 1370 | 0.001 | - |
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+ | 1.5316 | 1380 | 0.0006 | - |
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+ | 1.5427 | 1390 | 0.0015 | - |
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+ | 1.5538 | 1400 | 0.0008 | - |
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+ | 1.5649 | 1410 | 0.0006 | - |
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+ | 1.5760 | 1420 | 0.0007 | - |
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+ | 1.5871 | 1430 | 0.0009 | - |
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+ | 1.5982 | 1440 | 0.0004 | - |
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+ | 1.6093 | 1450 | 0.0013 | - |
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+ | 1.6204 | 1460 | 0.0007 | - |
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+ | 1.6315 | 1470 | 0.0004 | - |
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+ | 1.6426 | 1480 | 0.0005 | - |
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+ | 1.6537 | 1490 | 0.0006 | - |
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+ | 1.6648 | 1500 | 0.0008 | - |
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+ | 1.6759 | 1510 | 0.0007 | - |
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+ | 1.6870 | 1520 | 0.0005 | - |
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+ | 1.6981 | 1530 | 0.0004 | - |
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+ | 1.7092 | 1540 | 0.0005 | - |
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+ | 1.7203 | 1550 | 0.0007 | - |
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+ | 1.7314 | 1560 | 0.0006 | - |
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+ | 1.7425 | 1570 | 0.0004 | - |
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+ | 1.7536 | 1580 | 0.0006 | - |
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+ | 1.7647 | 1590 | 0.0005 | - |
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+ | 1.7758 | 1600 | 0.0006 | - |
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+ | 1.7869 | 1610 | 0.0011 | - |
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+ | 1.7980 | 1620 | 0.0007 | - |
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+ | 1.8091 | 1630 | 0.0005 | - |
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+ | 1.8202 | 1640 | 0.0005 | - |
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+ | 1.8313 | 1650 | 0.0003 | - |
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+ | 1.8424 | 1660 | 0.0004 | - |
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+ | 1.8535 | 1670 | 0.0006 | - |
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+ | 1.8646 | 1680 | 0.0005 | - |
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+ | 1.8757 | 1690 | 0.0006 | - |
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+ | 1.8868 | 1700 | 0.0004 | - |
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+ | 1.8979 | 1710 | 0.0004 | - |
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+ | 1.9090 | 1720 | 0.0002 | - |
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+ | 1.9201 | 1730 | 0.0005 | - |
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+ | 1.9312 | 1740 | 0.0005 | - |
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+ | 1.9423 | 1750 | 0.001 | - |
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+ | 1.9534 | 1760 | 0.0006 | - |
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+ | 1.9645 | 1770 | 0.001 | - |
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+ | 4.0 | 3604 | - | 0.0237 |
541
+
542
+ ### Framework Versions
543
+ - Python: 3.11.11
544
+ - SetFit: 1.1.0
545
+ - Sentence Transformers: 3.4.1
546
+ - Transformers: 4.44.0
547
+ - PyTorch: 2.6.0+cu124
548
+ - Datasets: 3.5.0
549
+ - Tokenizers: 0.19.1
550
+
551
+ ## Citation
552
+
553
+ ### BibTeX
554
+ ```bibtex
555
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
556
+ doi = {10.48550/ARXIV.2209.11055},
557
+ url = {https://arxiv.org/abs/2209.11055},
558
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
559
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
560
+ title = {Efficient Few-Shot Learning Without Prompts},
561
+ publisher = {arXiv},
562
+ year = {2022},
563
+ copyright = {Creative Commons Attribution 4.0 International}
564
+ }
565
+ ```
566
+
567
+ <!--
568
+ ## Glossary
569
+
570
+ *Clearly define terms in order to be accessible across audiences.*
571
+ -->
572
+
573
+ <!--
574
+ ## Model Card Authors
575
+
576
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
577
+ -->
578
+
579
+ <!--
580
+ ## Model Card Contact
581
+
582
+ *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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+ -->
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