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cl-nagoya-sup-simcse-ja-nss-v_1_0_7_5

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:197418
loss:CategoricalContrastiveLoss
Model card Files
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Add new SentenceTransformer model

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Files changed (10) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +418 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer_config.json +64 -0
  10. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
5
+ "pooling_mode_max_tokens": false,
6
+ "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 +1,418 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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:197418
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+ - loss:CategoricalContrastiveLoss
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+ widget:
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+ - source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。
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+ sentences:
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+ - 科目：コンクリート。名称：多目的ﾎｰﾙ浮き床ｺﾝｸﾘｰﾄ。
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+ - 科目：コンクリート。名称：ｼﾝﾀﾞｰｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表 0038。
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+ - 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。
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+ - source_sentence: 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設。
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+ sentences:
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+ - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC30+ΔS（構造体補正）S18　粗骨材20高性能AE減水剤。備考：刊-ｺﾝ 3018K免震層上部ｺﾝ。
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+ - 科目：コンクリート。名称：多目的ﾎｰﾙ間柱基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表 0041。
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+ - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
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+ - source_sentence: 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。
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+ sentences:
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+ - 科目：コンクリート。名称：擁壁部ｺﾝｸﾘｰﾄ打設手間。
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+ - 科目：タイル。名称：床ﾀｲﾙK。
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+ - 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表 0039。
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+ - source_sentence: 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ打設手間。
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+ sentences:
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+ - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC33+ΔS（構造体補正）S15粗骨材20高性能AE減水剤･防水剤入。備考：刊-ｺﾝ
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+ 3315KB基礎部ｺﾝ。
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+ - 科目：コンクリート。名称：機械基礎ｺﾝｸﾘｰﾄ。摘要：Fc24 S18粗骨材20。備考：代価表 0123。
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+ - 科目：コンクリート。名称：土間ｺﾝｸﾘｰﾄ。
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+ - source_sentence: 科目：コンクリート。名称：基礎部ﾏｽｺﾝｸﾘｰﾄ。
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+ sentences:
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+ - 科目：コンクリート。名称：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。摘要：FC24 S18粗骨材20 高性能AE減水剤。備考：代価表 0108。
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+ - 科目：タイル。名称：階段段鼻ﾉﾝｽﾘｯﾌﾟ役物ﾀｲﾙ。
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+ - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S15粗骨材基礎部。備考：代価表 0054。
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ ---
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+
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+ # SentenceTransformer
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-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:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
68
+ )
69
+ ```
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+
71
+ ## Usage
72
+
73
+ ### Direct Usage (Sentence Transformers)
74
+
75
+ First install the Sentence Transformers library:
76
+
77
+ ```bash
78
+ pip install -U sentence-transformers
79
+ ```
80
+
81
+ Then you can load this model and run inference.
82
+ ```python
83
+ from sentence_transformers import SentenceTransformer
84
+
85
+ # Download from the 🤗 Hub
86
+ model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_7_5")
87
+ # Run inference
88
+ sentences = [
89
+ '科目：コンクリート。名称：基礎部ﾏｽｺﾝｸﾘｰﾄ。',
90
+ '科目：コンクリート。名称：ｵｲﾙﾀﾝｸ基礎ｺﾝｸﾘｰﾄ。摘要：FC24 S18粗骨材20 高性能AE減水剤。備考：代価表 0108。',
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+ '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S15粗骨材基礎部。備考：代価表 0054。',
92
+ ]
93
+ embeddings = model.encode(sentences)
94
+ print(embeddings.shape)
95
+ # [3, 768]
96
+
97
+ # Get the similarity scores for the embeddings
98
+ similarities = model.similarity(embeddings, embeddings)
99
+ print(similarities.shape)
100
+ # [3, 3]
101
+ ```
102
+
103
+ <!--
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+ ### Direct Usage (Transformers)
105
+
106
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
109
+ -->
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+
111
+ <!--
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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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+
118
+ </details>
119
+ -->
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+
121
+ <!--
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+ ### Out-of-Scope Use
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+
124
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
125
+ -->
126
+
127
+ <!--
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+ ## Bias, Risks and Limitations
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+
130
+ *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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+
133
+ <!--
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+ ### Recommendations
135
+
136
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
137
+ -->
138
+
139
+ ## Training Details
140
+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+ * Size: 197,418 training samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | label |
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+ |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------|
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+ | type | string | string | int |
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+ | details | <ul><li>min: 11 tokens</li><li>mean: 13.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 31.5 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~61.50%</li><li>1: ~5.60%</li><li>2: ~32.90%</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | label |
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+ |:-----------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|:---------------|
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+ | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ﾎﾟﾝﾌﾟ圧送。</code> | <code>1</code> |
156
+ | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1 市場捨てｺﾝ。</code> | <code>0</code> |
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+ | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：躯体 ﾎﾟﾝﾌﾟ打設100m3/回以上 S15～S18標準階高 圧送費､基本料別途。備考：B0-434215 No.1 市場地上部ｺﾝ（1F）。</code> | <code>0</code> |
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+ * Loss: <code>sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss</code>
159
+
160
+ ### Training Hyperparameters
161
+ #### Non-Default Hyperparameters
162
+
163
+ - `per_device_train_batch_size`: 256
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+ - `per_device_eval_batch_size`: 256
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+ - `learning_rate`: 1e-05
166
+ - `weight_decay`: 0.01
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+ - `num_train_epochs`: 20
168
+ - `warmup_ratio`: 0.2
169
+ - `fp16`: True
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+
171
+ #### All Hyperparameters
172
+ <details><summary>Click to expand</summary>
173
+
174
+ - `overwrite_output_dir`: False
175
+ - `do_predict`: False
176
+ - `eval_strategy`: no
177
+ - `prediction_loss_only`: True
178
+ - `per_device_train_batch_size`: 256
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+ - `per_device_eval_batch_size`: 256
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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
183
+ - `eval_accumulation_steps`: None
184
+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 1e-05
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+ - `weight_decay`: 0.01
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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`: 20
192
+ - `max_steps`: -1
193
+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
195
+ - `warmup_ratio`: 0.2
196
+ - `warmup_steps`: 0
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
199
+ - `log_on_each_node`: True
200
+ - `logging_nan_inf_filter`: True
201
+ - `save_safetensors`: True
202
+ - `save_on_each_node`: False
203
+ - `save_only_model`: False
204
+ - `restore_callback_states_from_checkpoint`: False
205
+ - `no_cuda`: False
206
+ - `use_cpu`: False
207
+ - `use_mps_device`: False
208
+ - `seed`: 42
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+ - `data_seed`: None
210
+ - `jit_mode_eval`: False
211
+ - `use_ipex`: False
212
+ - `bf16`: False
213
+ - `fp16`: True
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+ - `fp16_opt_level`: O1
215
+ - `half_precision_backend`: auto
216
+ - `bf16_full_eval`: False
217
+ - `fp16_full_eval`: False
218
+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
221
+ - `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
225
+ - `dataloader_num_workers`: 0
226
+ - `dataloader_prefetch_factor`: None
227
+ - `past_index`: -1
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+ - `disable_tqdm`: False
229
+ - `remove_unused_columns`: True
230
+ - `label_names`: None
231
+ - `load_best_model_at_end`: False
232
+ - `ignore_data_skip`: False
233
+ - `fsdp`: []
234
+ - `fsdp_min_num_params`: 0
235
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
236
+ - `tp_size`: 0
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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
245
+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
248
+ - `ddp_broadcast_buffers`: False
249
+ - `dataloader_pin_memory`: True
250
+ - `dataloader_persistent_workers`: False
251
+ - `skip_memory_metrics`: True
252
+ - `use_legacy_prediction_loop`: False
253
+ - `push_to_hub`: False
254
+ - `resume_from_checkpoint`: None
255
+ - `hub_model_id`: None
256
+ - `hub_strategy`: every_save
257
+ - `hub_private_repo`: None
258
+ - `hub_always_push`: False
259
+ - `gradient_checkpointing`: False
260
+ - `gradient_checkpointing_kwargs`: None
261
+ - `include_inputs_for_metrics`: False
262
+ - `include_for_metrics`: []
263
+ - `eval_do_concat_batches`: True
264
+ - `fp16_backend`: auto
265
+ - `push_to_hub_model_id`: None
266
+ - `push_to_hub_organization`: None
267
+ - `mp_parameters`:
268
+ - `auto_find_batch_size`: False
269
+ - `full_determinism`: False
270
+ - `torchdynamo`: None
271
+ - `ray_scope`: last
272
+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
274
+ - `torch_compile_backend`: None
275
+ - `torch_compile_mode`: None
276
+ - `include_tokens_per_second`: False
277
+ - `include_num_input_tokens_seen`: False
278
+ - `neftune_noise_alpha`: None
279
+ - `optim_target_modules`: None
280
+ - `batch_eval_metrics`: False
281
+ - `eval_on_start`: False
282
+ - `use_liger_kernel`: False
283
+ - `eval_use_gather_object`: False
284
+ - `average_tokens_across_devices`: False
285
+ - `prompts`: None
286
+ - `batch_sampler`: batch_sampler
287
+ - `multi_dataset_batch_sampler`: proportional
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+
289
+ </details>
290
+
291
+ ### Training Logs
292
+ | Epoch | Step | Training Loss |
293
+ |:------:|:----:|:-------------:|
294
+ | 0.0648 | 50 | 0.2993 |
295
+ | 0.1295 | 100 | 0.1925 |
296
+ | 0.1943 | 150 | 0.1197 |
297
+ | 0.2591 | 200 | 0.1054 |
298
+ | 0.3238 | 250 | 0.0849 |
299
+ | 0.3886 | 300 | 0.0854 |
300
+ | 0.4534 | 350 | 0.0716 |
301
+ | 0.5181 | 400 | 0.0659 |
302
+ | 0.5829 | 450 | 0.0641 |
303
+ | 0.6477 | 500 | 0.0641 |
304
+ | 0.7124 | 550 | 0.0619 |
305
+ | 0.7772 | 600 | 0.0589 |
306
+ | 0.8420 | 650 | 0.0564 |
307
+ | 0.9067 | 700 | 0.0506 |
308
+ | 0.9715 | 750 | 0.0513 |
309
+ | 1.0363 | 800 | 0.0473 |
310
+ | 1.1010 | 850 | 0.0451 |
311
+ | 1.1658 | 900 | 0.044 |
312
+ | 1.2306 | 950 | 0.0418 |
313
+ | 1.2953 | 1000 | 0.042 |
314
+ | 1.3601 | 1050 | 0.0337 |
315
+ | 1.4249 | 1100 | 0.0337 |
316
+ | 1.4896 | 1150 | 0.0354 |
317
+ | 1.5544 | 1200 | 0.0353 |
318
+ | 1.6192 | 1250 | 0.0353 |
319
+ | 1.6839 | 1300 | 0.0323 |
320
+ | 1.7487 | 1350 | 0.0297 |
321
+ | 1.8135 | 1400 | 0.0331 |
322
+ | 1.8782 | 1450 | 0.0303 |
323
+ | 1.9430 | 1500 | 0.0286 |
324
+ | 2.0078 | 1550 | 0.0265 |
325
+ | 2.0725 | 1600 | 0.0257 |
326
+ | 2.1373 | 1650 | 0.0195 |
327
+ | 2.2021 | 1700 | 0.0225 |
328
+ | 2.2668 | 1750 | 0.0206 |
329
+ | 2.3316 | 1800 | 0.0231 |
330
+ | 2.3964 | 1850 | 0.0225 |
331
+ | 2.4611 | 1900 | 0.0203 |
332
+ | 2.5259 | 1950 | 0.0207 |
333
+ | 2.5907 | 2000 | 0.02 |
334
+ | 2.6554 | 2050 | 0.0181 |
335
+ | 2.7202 | 2100 | 0.0202 |
336
+ | 2.7850 | 2150 | 0.0187 |
337
+ | 2.8497 | 2200 | 0.0192 |
338
+ | 2.9145 | 2250 | 0.0168 |
339
+ | 2.9793 | 2300 | 0.0162 |
340
+ | 3.0440 | 2350 | 0.0159 |
341
+ | 3.1088 | 2400 | 0.0145 |
342
+ | 3.1736 | 2450 | 0.0134 |
343
+ | 3.2383 | 2500 | 0.0138 |
344
+ | 3.3031 | 2550 | 0.0125 |
345
+ | 3.3679 | 2600 | 0.0132 |
346
+ | 3.4326 | 2650 | 0.0122 |
347
+ | 3.4974 | 2700 | 0.0133 |
348
+ | 3.5622 | 2750 | 0.0127 |
349
+ | 3.6269 | 2800 | 0.0125 |
350
+ | 3.6917 | 2850 | 0.0107 |
351
+ | 3.7565 | 2900 | 0.0114 |
352
+ | 3.8212 | 2950 | 0.0104 |
353
+ | 3.8860 | 3000 | 0.0107 |
354
+ | 3.9508 | 3050 | 0.0112 |
355
+ | 4.0155 | 3100 | 0.0084 |
356
+ | 4.0803 | 3150 | 0.0086 |
357
+ | 4.1451 | 3200 | 0.0077 |
358
+ | 4.2098 | 3250 | 0.0098 |
359
+ | 4.2746 | 3300 | 0.0068 |
360
+ | 4.3394 | 3350 | 0.0082 |
361
+ | 4.4041 | 3400 | 0.0064 |
362
+ | 4.4689 | 3450 | 0.0083 |
363
+ | 4.5337 | 3500 | 0.0065 |
364
+ | 4.5984 | 3550 | 0.0067 |
365
+ | 4.6632 | 3600 | 0.0074 |
366
+ | 4.7280 | 3650 | 0.0078 |
367
+ | 4.7927 | 3700 | 0.0072 |
368
+ | 4.8575 | 3750 | 0.0077 |
369
+ | 4.9223 | 3800 | 0.007 |
370
+ | 4.9870 | 3850 | 0.0067 |
371
+ | 5.0518 | 3900 | 0.0057 |
372
+ | 5.1166 | 3950 | 0.0054 |
373
+ | 5.1813 | 4000 | 0.0046 |
374
+
375
+
376
+ ### Framework Versions
377
+ - Python: 3.11.12
378
+ - Sentence Transformers: 4.1.0
379
+ - Transformers: 4.51.3
380
+ - PyTorch: 2.6.0+cu124
381
+ - Accelerate: 1.6.0
382
+ - Datasets: 2.14.4
383
+ - Tokenizers: 0.21.1
384
+
385
+ ## Citation
386
+
387
+ ### BibTeX
388
+
389
+ #### Sentence Transformers
390
+ ```bibtex
391
+ @inproceedings{reimers-2019-sentence-bert,
392
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
393
+ author = "Reimers, Nils and Gurevych, Iryna",
394
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
395
+ month = "11",
396
+ year = "2019",
397
+ publisher = "Association for Computational Linguistics",
398
+ url = "https://arxiv.org/abs/1908.10084",
399
+ }
400
+ ```
401
+
402
+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
406
+ -->
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+
408
+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
413
+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
418
+ -->
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