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wwydmanski
/
arctic-pubmed-v0.1

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:10053
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +730 -0
  4. config.json +28 -0
  5. config_sentence_transformers.json +12 -0
  6. model.safetensors +3 -0
  7. modules.json +20 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +51 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +61 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ 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
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 1024,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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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 +1,730 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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:10053
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: Snowflake/snowflake-arctic-embed-l-v2.0
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+ widget:
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+ - source_sentence: Fluorescence quenching of tryptophan residues
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+ sentences:
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+ - 'Fluorescence of buried tyrosine residues in proteins. '
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+ - 'A fluorescence quenching study of tryptophanyl residues of (Ca2+ + Mg2+)-ATPase
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+ from sarcoplasmic reticulum. '
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+ - 'Some hormonal influences on the acetylation of sulfanilamide in vivo. '
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+ - source_sentence: Human migration to the Americas
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+ sentences:
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+ - 'Homo sapiens in the Americas. Overview of the earliest human expansion in the
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+ New World. '
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+ - 'Profiles of College Drinkers Defined by Alcohol Behaviors at the Week Level:
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+ Replication Across Semesters and Prospective Associations With Hazardous Drinking
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+ and Dependence-Related Symptoms. '
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+ - 'Human migration. '
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+ - source_sentence: Human Mobility Prediction
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+ sentences:
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+ - 'Human mobility prediction from region functions with taxi trajectories. '
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+ - 'Understanding Human Mobility from Twitter. '
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+ - 'Ovarian cancer gene therapy using HPV-16 pseudovirion carrying the HSV-tk gene. '
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+ - source_sentence: Nevirapine Resistance
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+ sentences:
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+ - 'Nevirapine toxicity. '
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+ - 'Recognizing rhenium. '
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+ - 'Update on nevirapine: quest for a niche. '
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+ - source_sentence: EHL tendon reconstruction
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+ sentences:
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+ - 'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two
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+ Case Reports. '
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+ - 'Flexor tendon reconstruction. '
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+ - 'Noble gases and neuroprotection: summary of current evidence. '
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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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+ - cosine_accuracy
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+ - dot_accuracy
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+ - manhattan_accuracy
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+ - euclidean_accuracy
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+ - max_accuracy
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+ model-index:
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+ - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0
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+ results:
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+ - task:
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+ type: triplet
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+ name: Triplet
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+ dataset:
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+ name: triplet dev
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+ type: triplet-dev
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.932
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+ name: Cosine Accuracy
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+ - type: dot_accuracy
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+ value: 0.066
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+ name: Dot Accuracy
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+ - type: manhattan_accuracy
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+ value: 0.933
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+ name: Manhattan Accuracy
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+ - type: euclidean_accuracy
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+ value: 0.932
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+ name: Euclidean Accuracy
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+ - type: max_accuracy
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+ value: 0.933
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+ name: Max Accuracy
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+ ---
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+
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+ # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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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:** [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) <!-- at revision 7f311bb640ad3babc0a4e3a8873240dcba44c9d2 -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Output Dimensionality:** 1024 tokens
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - json
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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': 8192, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction
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+ (1): Pooling({'word_embedding_dimension': 1024, '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})
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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:
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+
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+ ```bash
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+ 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.
120
+ ```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("sentence_transformers_model_id")
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+ # Run inference
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+ sentences = [
127
+ 'EHL tendon reconstruction',
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+ 'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two Case Reports. ',
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+ 'Flexor tendon reconstruction. ',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 1024]
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+
135
+ # Get the similarity scores for the embeddings
136
+ similarities = model.similarity(embeddings, embeddings)
137
+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
141
+ <!--
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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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+ #### Triplet
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+ * Dataset: `triplet-dev`
171
+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:----------|
175
+ | **cosine_accuracy** | **0.932** |
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+ | dot_accuracy | 0.066 |
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+ | manhattan_accuracy | 0.933 |
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+ | euclidean_accuracy | 0.932 |
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+ | max_accuracy | 0.933 |
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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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+ -->
186
+
187
+ <!--
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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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+
195
+ ### Training Dataset
196
+
197
+ #### json
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+
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+ * Dataset: json
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+ * Size: 10,053 training samples
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+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive | negative |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 10.55 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.45 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.1 tokens</li><li>max: 55 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive | negative |
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+ |:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|
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+ | <code>COM-induced secretome changes in U937 monocytes</code> | <code>Characterization of calcium oxalate crystal-induced changes in the secretome of U937 human monocytes. </code> | <code>Monocytes. </code> |
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+ | <code>Metamaterials</code> | <code>Sound attenuation optimization using metaporous materials tuned on exceptional points. </code> | <code>Metamaterials: A cat's eye for all directions. </code> |
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+ | <code>Pediatric Parasitology</code> | <code>Parasitic infections among school age children 6 to 11-years-of-age in the Eastern province. </code> | <code>[DIALOGUE ON PEDIATRIC PARASITOLOGY]. </code> |
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+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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+ ```json
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+ {
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+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
218
+ }
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+ ```
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+
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+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
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+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `learning_rate`: 0.001
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+ - `num_train_epochs`: 1
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+ - `lr_scheduler_type`: cosine_with_restarts
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+ - `warmup_ratio`: 0.1
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+ - `bf16`: True
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+ - `batch_sampler`: no_duplicates
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
236
+
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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`: 32
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+ - `per_device_eval_batch_size`: 32
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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`: 0.001
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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`: cosine_with_restarts
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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`: True
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+ - `fp16`: False
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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`: False
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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
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
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+ - `gradient_checkpointing`: False
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+ - `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
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+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
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+ - `auto_find_batch_size`: False
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+ - `full_determinism`: False
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+ - `torchdynamo`: None
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+ - `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
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+ - `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
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+ - `batch_eval_metrics`: False
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+ - `eval_on_start`: False
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+ - `use_liger_kernel`: False
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+ - `eval_use_gather_object`: False
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+ - `batch_sampler`: no_duplicates
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+ - `multi_dataset_batch_sampler`: proportional
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+
350
+ </details>
351
+
352
+ ### Training Logs
353
+ <details><summary>Click to expand</summary>
354
+
355
+ | Epoch | Step | Training Loss | triplet-dev_cosine_accuracy |
356
+ |:------:|:----:|:-------------:|:---------------------------:|
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+ | 0 | 0 | - | 0.58 |
358
+ | 0.0032 | 1 | 1.3744 | - |
359
+ | 0.0063 | 2 | 1.2294 | - |
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+ | 0.0095 | 3 | 1.2058 | - |
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+ | 0.0127 | 4 | 1.2122 | - |
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+ | 0.0159 | 5 | 1.5614 | - |
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+ | 0.0190 | 6 | 1.2478 | - |
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+ | 0.0222 | 7 | 0.7569 | - |
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+ | 0.0254 | 8 | 0.734 | - |
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+ | 0.0286 | 9 | 1.16 | - |
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+ | 0.0317 | 10 | 0.5651 | 0.711 |
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+ | 0.0349 | 11 | 1.1555 | - |
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+ | 0.0381 | 12 | 0.7146 | - |
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+ | 0.0413 | 13 | 0.5214 | - |
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+ | 0.0444 | 14 | 0.6155 | - |
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+ | 0.0476 | 15 | 0.6998 | - |
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+ | 0.0508 | 16 | 1.3517 | - |
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+ | 0.0540 | 17 | 0.6028 | - |
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+ | 0.0571 | 18 | 0.7165 | - |
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+ | 0.0603 | 19 | 0.712 | - |
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+ | 0.0635 | 20 | 0.6988 | 0.86 |
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+ | 0.0667 | 21 | 0.6056 | - |
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+ | 0.0698 | 22 | 0.6244 | - |
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+ | 0.0730 | 23 | 0.8347 | - |
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+ | 0.0762 | 24 | 0.5033 | - |
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+ | 0.0794 | 25 | 0.9254 | - |
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+ | 0.0825 | 26 | 0.3873 | - |
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+ | 0.0857 | 27 | 0.5664 | - |
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+ | 0.0889 | 28 | 0.6109 | - |
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+ | 0.0921 | 29 | 0.4903 | - |
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+ | 0.0952 | 30 | 0.696 | 0.874 |
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+ | 0.0984 | 31 | 1.1626 | - |
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+ | 0.1016 | 32 | 0.3114 | - |
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+ | 0.1048 | 33 | 0.8082 | - |
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+ | 0.1079 | 34 | 0.9072 | - |
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+ | 0.1111 | 35 | 0.3157 | - |
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+ | 0.1143 | 36 | 0.8342 | - |
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+ | 0.1175 | 37 | 0.3941 | - |
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+ | 0.1206 | 38 | 0.4069 | - |
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+ | 0.1238 | 39 | 0.4186 | - |
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+ | 0.1270 | 40 | 0.4617 | 0.902 |
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+ | 0.1302 | 41 | 0.5417 | - |
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+ | 0.1333 | 42 | 0.4168 | - |
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+ | 0.1365 | 43 | 0.702 | - |
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+ | 0.1397 | 44 | 0.7394 | - |
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+ | 0.1429 | 45 | 0.4674 | - |
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+ | 0.1460 | 46 | 0.6938 | - |
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+ | 0.1492 | 47 | 0.5182 | - |
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+ | 0.1524 | 48 | 0.3144 | - |
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+ | 0.1556 | 49 | 0.3902 | - |
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+ | 0.1587 | 50 | 0.4609 | 0.906 |
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+ | 0.1619 | 51 | 0.5452 | - |
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+ | 0.1651 | 52 | 0.2095 | - |
410
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671
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672
+ | 1.0 | 315 | 0.0322 | 0.932 |
673
+
674
+ </details>
675
+
676
+ ### Framework Versions
677
+ - Python: 3.9.19
678
+ - Sentence Transformers: 3.1.1
679
+ - Transformers: 4.45.2
680
+ - PyTorch: 2.5.0
681
+ - Accelerate: 1.0.1
682
+ - Datasets: 2.19.0
683
+ - Tokenizers: 0.20.3
684
+
685
+ ## Citation
686
+
687
+ ### BibTeX
688
+
689
+ #### Sentence Transformers
690
+ ```bibtex
691
+ @inproceedings{reimers-2019-sentence-bert,
692
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
693
+ author = "Reimers, Nils and Gurevych, Iryna",
694
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
695
+ month = "11",
696
+ year = "2019",
697
+ publisher = "Association for Computational Linguistics",
698
+ url = "https://arxiv.org/abs/1908.10084",
699
+ }
700
+ ```
701
+
702
+ #### MultipleNegativesRankingLoss
703
+ ```bibtex
704
+ @misc{henderson2017efficient,
705
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
706
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
707
+ year={2017},
708
+ eprint={1705.00652},
709
+ archivePrefix={arXiv},
710
+ primaryClass={cs.CL}
711
+ }
712
+ ```
713
+
714
+ <!--
715
+ ## Glossary
716
+
717
+ *Clearly define terms in order to be accessible across audiences.*
718
+ -->
719
+
720
+ <!--
721
+ ## Model Card Authors
722
+
723
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
724
+ -->
725
+
726
+ <!--
727
+ ## Model Card Contact
728
+
729
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
730
+ -->
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tokenizer_config.json ADDED
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+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "special": true
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+ "single_word": false,
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+ "unk_token": "<unk>"
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+ }