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final/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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+ }
final/README.md ADDED
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+ ---
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+ language:
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+ - en
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+ library_name: sentence-transformers
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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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+ - dataset_size:100K<n<1M
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+ - loss:CachedMultipleNegativesRankingLoss
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+ base_model: Unbabel/xlm-roberta-comet-small
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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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+ widget:
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+ - source_sentence: There's a dock
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+ sentences:
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+ - There is a door.
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+ - the animal is running
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+ - The woman is singing.
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+ - source_sentence: The boy scowls
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+ sentences:
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+ - A boy is blowing bubbles.
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+ - He is playing a song.
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+ - They are driving cars.
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+ - source_sentence: A bird flying.
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+ sentences:
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+ - A butterfly flys freely.
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+ - A dog carries a bone.
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+ - Two dogs are playing.
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+ - source_sentence: A woman sings.
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+ sentences:
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+ - The woman is singing.
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+ - The man is in a city.
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+ - there is a man in a pool.
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+ - source_sentence: a baby smiling
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+ sentences:
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+ - A baby is unhappy.
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+ - The dog has big ears.
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+ - They are driving cars.
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+ pipeline_tag: sentence-similarity
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+ model-index:
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+ - name: SentenceTransformer based on Unbabel/xlm-roberta-comet-small
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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: all nli dev
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+ type: all-nli-dev
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.849
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+ name: Cosine Accuracy
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+ - type: dot_accuracy
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+ value: 0.163
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+ name: Dot Accuracy
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+ - type: manhattan_accuracy
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+ value: 0.837
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+ name: Manhattan Accuracy
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+ - type: euclidean_accuracy
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+ value: 0.841
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+ name: Euclidean Accuracy
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+ - type: max_accuracy
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+ value: 0.849
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+ name: Max Accuracy
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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: all nli test
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+ type: all-nli-test
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.839
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+ name: Cosine Accuracy
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+ - type: dot_accuracy
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+ value: 0.15
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+ name: Dot Accuracy
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+ - type: manhattan_accuracy
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+ value: 0.827
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+ name: Manhattan Accuracy
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+ - type: euclidean_accuracy
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+ value: 0.827
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+ name: Euclidean Accuracy
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+ - type: max_accuracy
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+ value: 0.839
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+ name: Max Accuracy
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+ ---
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+
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+ # SentenceTransformer based on Unbabel/xlm-roberta-comet-small
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Unbabel/xlm-roberta-comet-small](https://huggingface.co/Unbabel/xlm-roberta-comet-small) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [Unbabel/xlm-roberta-comet-small](https://huggingface.co/Unbabel/xlm-roberta-comet-small) <!-- at revision df568a015df5cefbf2f449314b61ce9afb0cb593 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 384 tokens
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
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+ - **Language:** en
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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: XLMRobertaModel
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+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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+ )
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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.
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("sentence_transformers_model_id")
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+ # Run inference
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+ sentences = [
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+ 'a baby smiling',
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+ 'A baby is unhappy.',
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+ 'The dog has big ears.',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 384]
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
161
+ <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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+
184
+ ### Metrics
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+
186
+ #### Triplet
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+ * Dataset: `all-nli-dev`
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+ * 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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+ |:-------------------|:----------|
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+ | cosine_accuracy | 0.849 |
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+ | dot_accuracy | 0.163 |
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+ | manhattan_accuracy | 0.837 |
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+ | euclidean_accuracy | 0.841 |
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+ | **max_accuracy** | **0.849** |
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+
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+ #### Triplet
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+ * Dataset: `all-nli-test`
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+ * 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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+ |:-------------------|:----------|
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+ | cosine_accuracy | 0.839 |
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+ | dot_accuracy | 0.15 |
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+ | manhattan_accuracy | 0.827 |
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+ | euclidean_accuracy | 0.827 |
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+ | **max_accuracy** | **0.839** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### sentence-transformers/all-nli
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+
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+ * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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+ * Size: 100,000 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: 7 tokens</li><li>mean: 10.9 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.62 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.76 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>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
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+ | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
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+ | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
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+ * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
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+ ```json
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+ {
245
+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
247
+ }
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+ ```
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+
250
+ ### Evaluation Dataset
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+
252
+ #### sentence-transformers/all-nli
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+
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+ * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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+ * Size: 1,000 evaluation 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: 6 tokens</li><li>mean: 20.31 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 32 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive | negative |
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+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
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+ | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
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+ | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
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+ | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
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+ * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
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+ ```json
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+ {
271
+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
273
+ }
274
+ ```
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+
276
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
279
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 16
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+ - `per_device_eval_batch_size`: 16
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+ - `num_train_epochs`: 1
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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>
289
+
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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`: 16
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+ - `per_device_eval_batch_size`: 16
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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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+ - `learning_rate`: 5e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 1
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 0
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `restore_callback_states_from_checkpoint`: False
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: 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
367
+ - `push_to_hub`: False
368
+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
371
+ - `hub_private_repo`: False
372
+ - `hub_always_push`: False
373
+ - `gradient_checkpointing`: False
374
+ - `gradient_checkpointing_kwargs`: None
375
+ - `include_inputs_for_metrics`: False
376
+ - `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
383
+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
387
+ - `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
392
+ - `include_num_input_tokens_seen`: False
393
+ - `neftune_noise_alpha`: None
394
+ - `optim_target_modules`: None
395
+ - `batch_eval_metrics`: False
396
+ - `batch_sampler`: no_duplicates
397
+ - `multi_dataset_batch_sampler`: proportional
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+
399
+ </details>
400
+
401
+ ### Training Logs
402
+ | Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
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+ |:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
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+ | 0 | 0 | - | - | 0.541 | - |
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+ | 0.016 | 100 | 3.5308 | 3.1817 | 0.558 | - |
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+ | 0.032 | 200 | 3.2784 | 3.0406 | 0.597 | - |
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+ | 0.048 | 300 | 3.113 | 2.7572 | 0.635 | - |
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+ | 0.064 | 400 | 2.8296 | 2.4646 | 0.68 | - |
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+ | 0.08 | 500 | 2.631 | 2.3583 | 0.676 | - |
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+ | 0.096 | 600 | 2.3247 | 2.1394 | 0.706 | - |
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+ | 0.112 | 700 | 2.2211 | 2.0201 | 0.711 | - |
412
+ | 0.128 | 800 | 2.1263 | 1.9560 | 0.757 | - |
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+ | 0.144 | 900 | 2.2105 | 1.9074 | 0.748 | - |
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+ | 0.16 | 1000 | 2.0637 | 1.9289 | 0.728 | - |
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+ | 0.176 | 1100 | 2.1772 | 1.8796 | 0.741 | - |
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+ | 0.192 | 1200 | 2.1518 | 1.8346 | 0.761 | - |
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+ | 0.208 | 1300 | 1.728 | 1.8213 | 0.765 | - |
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+ | 0.224 | 1400 | 1.8101 | 1.6321 | 0.772 | - |
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+ | 0.24 | 1500 | 1.7516 | 1.5669 | 0.793 | - |
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+ | 0.256 | 1600 | 1.4988 | 1.5538 | 0.8 | - |
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+ | 0.272 | 1700 | 1.6695 | 1.5462 | 0.803 | - |
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+ | 0.288 | 1800 | 1.5971 | 1.5499 | 0.783 | - |
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+ | 0.304 | 1900 | 1.5614 | 1.5047 | 0.788 | - |
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+ | 0.32 | 2000 | 1.522 | 1.4957 | 0.794 | - |
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+ | 0.336 | 2100 | 1.3624 | 1.4153 | 0.814 | - |
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+ | 0.352 | 2200 | 1.4773 | 1.4169 | 0.809 | - |
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+ | 0.368 | 2300 | 1.6066 | 1.3697 | 0.813 | - |
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+ | 0.384 | 2400 | 1.5106 | 1.3203 | 0.819 | - |
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+ | 0.4 | 2500 | 1.4783 | 1.3417 | 0.817 | - |
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+ | 0.416 | 2600 | 1.3696 | 1.2650 | 0.824 | - |
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+ | 0.432 | 2700 | 1.5115 | 1.2779 | 0.829 | - |
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+ | 0.448 | 2800 | 1.4834 | 1.2668 | 0.834 | - |
433
+ | 0.464 | 2900 | 1.4823 | 1.2621 | 0.836 | - |
434
+ | 0.48 | 3000 | 1.4163 | 1.2465 | 0.837 | - |
435
+ | 0.496 | 3100 | 1.4232 | 1.2475 | 0.837 | - |
436
+ | 0.512 | 3200 | 1.2193 | 1.1975 | 0.838 | - |
437
+ | 0.528 | 3300 | 1.2569 | 1.1816 | 0.838 | - |
438
+ | 0.544 | 3400 | 1.2988 | 1.1936 | 0.839 | - |
439
+ | 0.56 | 3500 | 1.5068 | 1.2213 | 0.835 | - |
440
+ | 0.576 | 3600 | 1.3022 | 1.1799 | 0.842 | - |
441
+ | 0.592 | 3700 | 1.3823 | 1.1910 | 0.831 | - |
442
+ | 0.608 | 3800 | 1.4224 | 1.1786 | 0.834 | - |
443
+ | 0.624 | 3900 | 1.3765 | 1.1541 | 0.843 | - |
444
+ | 0.64 | 4000 | 1.4987 | 1.1365 | 0.844 | - |
445
+ | 0.656 | 4100 | 1.7525 | 1.1394 | 0.843 | - |
446
+ | 0.672 | 4200 | 1.6013 | 1.1178 | 0.841 | - |
447
+ | 0.688 | 4300 | 1.3326 | 1.0959 | 0.846 | - |
448
+ | 0.704 | 4400 | 1.355 | 1.0757 | 0.848 | - |
449
+ | 0.72 | 4500 | 1.2834 | 1.0681 | 0.846 | - |
450
+ | 0.736 | 4600 | 1.2939 | 1.0696 | 0.85 | - |
451
+ | 0.752 | 4700 | 1.4069 | 1.0645 | 0.848 | - |
452
+ | 0.768 | 4800 | 1.4503 | 1.0609 | 0.849 | - |
453
+ | 0.784 | 4900 | 1.2833 | 1.0587 | 0.847 | - |
454
+ | 0.8 | 5000 | 1.3321 | 1.0563 | 0.849 | - |
455
+ | 0.816 | 5100 | 1.3006 | 1.0539 | 0.847 | - |
456
+ | 0.832 | 5200 | 1.4332 | 1.0527 | 0.847 | - |
457
+ | 0.848 | 5300 | 1.3101 | 1.0505 | 0.848 | - |
458
+ | 0.864 | 5400 | 1.3658 | 1.0523 | 0.849 | - |
459
+ | 0.88 | 5500 | 1.353 | 1.0520 | 0.849 | - |
460
+ | 0.896 | 5600 | 1.2429 | 1.0521 | 0.848 | - |
461
+ | 0.912 | 5700 | 1.3512 | 1.0505 | 0.848 | - |
462
+ | 0.928 | 5800 | 1.2995 | 1.0501 | 0.848 | - |
463
+ | 0.944 | 5900 | 1.3514 | 1.0491 | 0.849 | - |
464
+ | 0.96 | 6000 | 1.3976 | 1.0490 | 0.848 | - |
465
+ | 0.976 | 6100 | 1.2112 | 1.0487 | 0.848 | - |
466
+ | 0.992 | 6200 | 0.0033 | 1.0492 | 0.849 | - |
467
+ | 1.0 | 6250 | - | - | - | 0.839 |
468
+
469
+
470
+ ### Framework Versions
471
+ - Python: 3.9.10
472
+ - Sentence Transformers: 3.0.0
473
+ - Transformers: 4.41.2
474
+ - PyTorch: 2.3.0+cu121
475
+ - Accelerate: 0.26.1
476
+ - Datasets: 2.16.1
477
+ - Tokenizers: 0.19.1
478
+
479
+ ## Citation
480
+
481
+ ### BibTeX
482
+
483
+ #### Sentence Transformers
484
+ ```bibtex
485
+ @inproceedings{reimers-2019-sentence-bert,
486
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
487
+ author = "Reimers, Nils and Gurevych, Iryna",
488
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
489
+ month = "11",
490
+ year = "2019",
491
+ publisher = "Association for Computational Linguistics",
492
+ url = "https://arxiv.org/abs/1908.10084",
493
+ }
494
+ ```
495
+
496
+ #### CachedMultipleNegativesRankingLoss
497
+ ```bibtex
498
+ @misc{gao2021scaling,
499
+ title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
500
+ author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
501
+ year={2021},
502
+ eprint={2101.06983},
503
+ archivePrefix={arXiv},
504
+ primaryClass={cs.LG}
505
+ }
506
+ ```
507
+
508
+ <!--
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+ ## Glossary
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+
511
+ *Clearly define terms in order to be accessible across audiences.*
512
+ -->
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+
514
+ <!--
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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.*
518
+ -->
519
+
520
+ <!--
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+ ## Model Card Contact
522
+
523
+ *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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