|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
|
language: |
|
- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
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- dataset_size:1000 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: 'Revision stage: Edit the output to correct content unsupported |
|
by evidence while preserving the original content as much as possible. Initialize |
|
the revised text $y=x$. |
|
|
|
|
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(1) Per $(q_i, e_{ij})$, an agreement model (via few-shot prompting + CoT, $(y, |
|
q, e) \to {0,1}$) checks whether the evidence $e_i$ disagrees with the current |
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revised text $y$. |
|
|
|
(2) Only if a disagreement is detect, the edit model (via few-shot prompting + |
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CoT, $(y, q, e) \to \text{ new }y$) outputs a new version of $y$ that aims to |
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agree with evidence $e_{ij}$ while otherwise minimally altering $y$. |
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|
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(3) Finally only a limited number $M=5$ of evidence goes into the attribution |
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report $A$. |
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|
|
|
|
|
|
|
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|
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Fig. 12. Illustration of RARR (Retrofit Attribution using Research and Revision). |
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(Image source: Gao et al. 2022) |
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|
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When evaluating the revised text $y$, both attribution and preservation metrics |
|
matter.' |
|
sentences: |
|
- What is the impact of claim extraction on the efficiency of query generation within |
|
various tool querying methodologies? |
|
- What are the implications of integrating both attribution and preservation metrics |
|
in the assessment of a revised text for an attribution report? |
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- What impact does the calibration of large language models, as discussed in the |
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research by Kadavath et al. (2022), have on the consistency and accuracy of their |
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responses, particularly in the context of multiple choice questions? |
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- source_sentence: 'Fig. 1. Knowledge categorization of close-book QA examples based |
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on how likely the model outputs correct answers. (Image source: Gekhman et al. |
|
2024) |
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|
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Some interesting observations of the experiments, where dev set accuracy is considered |
|
a proxy for hallucinations. |
|
|
|
|
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Unknown examples are fitted substantially slower than Known. |
|
|
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The best dev performance is obtained when the LLM fits the majority of the Known |
|
training examples but only a few of the Unknown ones. The model starts to hallucinate |
|
when it learns most of the Unknown examples. |
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|
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Among Known examples, MaybeKnown cases result in better overall performance, more |
|
essential than HighlyKnown ones.' |
|
sentences: |
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- What are the implications of a language model's performance when it is primarily |
|
trained on familiar examples compared to a diverse set of unfamiliar examples, |
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and how does this relate to the phenomenon of hallucinations in language models? |
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- How can the insights gained from the evaluation framework inform the future enhancements |
|
of AI models, particularly in terms of improving factual accuracy and entity recognition? |
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- What role does the MPNet model play in evaluating the faithfulness of reasoning |
|
paths, particularly in relation to scores of entailment and contradiction? |
|
- source_sentence: 'Non-context LLM: Prompt LLM directly with <atomic-fact> True or |
|
False? without additional context. |
|
|
|
Retrieval→LLM: Prompt with $k$ related passages retrieved from the knowledge source |
|
as context. |
|
|
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Nonparametric probability (NP)): Compute the average likelihood of tokens in the |
|
atomic fact by a masked LM and use that to make a prediction. |
|
|
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Retrieval→LLM + NP: Ensemble of two methods. |
|
|
|
|
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Some interesting observations on model hallucination behavior: |
|
|
|
|
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Error rates are higher for rarer entities in the task of biography generation. |
|
|
|
Error rates are higher for facts mentioned later in the generation. |
|
|
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Using retrieval to ground the model generation significantly helps reduce hallucination.' |
|
sentences: |
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- What methods does the model employ to generate impactful, non-standard verification |
|
questions that enhance the fact-checking process? |
|
- What impact does the timing of fact presentation in AI outputs have on the likelihood |
|
of generating inaccuracies? |
|
- What are the benefits of using the 'Factor+revise' strategy in enhancing the reliability |
|
of verification processes in few-shot learning, particularly when it comes to |
|
identifying inconsistencies? |
|
- source_sentence: 'Research stage: Find related documents as evidence. |
|
|
|
|
|
(1) First use a query generation model (via few-shot prompting, $x \to {q_1, \dots, |
|
q_N}$) to construct a set of search queries ${q_1, \dots, q_N}$ to verify all |
|
aspects of each sentence. |
|
|
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(2) Run Google search, $K=5$ results per query $q_i$. |
|
|
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(3) Utilize a pretrained query-document relevance model to assign relevance scores |
|
and only retain one most relevant $J=1$ document $e_{i1}, \dots, e_{iJ}$ per query |
|
$q_i$. |
|
|
|
|
|
|
|
Revision stage: Edit the output to correct content unsupported by evidence while |
|
preserving the original content as much as possible. Initialize the revised text |
|
$y=x$.' |
|
sentences: |
|
- In what ways does the process of generating queries facilitate the verification |
|
of content accuracy, particularly through the lens of evidence-based editing methodologies? |
|
- What role do attribution and preservation metrics play in assessing the quality |
|
of revised texts, and how might these factors influence the success of the Evidence |
|
Disagreement Detection process? |
|
- What are the practical ways to utilize the F1 @ K metric for assessing how well |
|
FacTool identifies factual inaccuracies in various fields? |
|
- source_sentence: '(1) Joint: join with step 2, where the few-shot examples are structured |
|
as (response, verification questions, verification answers); The drawback is that |
|
the original response is in the context, so the model may repeat similar hallucination. |
|
|
|
(2) 2-step: separate the verification planning and execution steps, such as the |
|
original response doesn’t impact |
|
|
|
(3) Factored: each verification question is answered separately. Say, if a long-form |
|
base generation results in multiple verification questions, we would answer each |
|
question one-by-one. |
|
|
|
(4) Factor+revise: adding a “cross-checking” step after factored verification |
|
execution, conditioned on both the baseline response and the verification question |
|
and answer. It detects inconsistency. |
|
|
|
|
|
|
|
Final output: Generate the final, refined output. The output gets revised at this |
|
step if any inconsistency is discovered.' |
|
sentences: |
|
- What are the key challenges associated with using a pre-training dataset for world |
|
knowledge, particularly in maintaining the factual accuracy of the outputs generated |
|
by the model? |
|
- What obstacles arise when depending on the pre-training dataset in the context |
|
of extrinsic hallucination affecting model outputs? |
|
- In what ways does the 'Factor+revise' method enhance the reliability of responses |
|
when compared to the 'Joint' and '2-step' methods used for cross-checking? |
|
model-index: |
|
- name: BGE base Financial Matryoshka |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8802083333333334 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.984375 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9947916666666666 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9947916666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8802083333333334 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.328125 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19895833333333335 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09947916666666667 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8802083333333334 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.984375 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9947916666666666 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9947916666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9495062223081544 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9337673611111109 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.934240845959596 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8854166666666666 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.984375 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9947916666666666 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8854166666666666 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.328125 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19895833333333335 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8854166666666666 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.984375 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9947916666666666 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9536782535355709 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.937818287037037 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.937818287037037 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9010416666666666 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.984375 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9010416666666666 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.328125 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19999999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9010416666666666 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.984375 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9587563670488631 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9446180555555554 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9446180555555556 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.90625 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.984375 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.90625 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.328125 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19999999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.90625 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.984375 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9609068566179642 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9474826388888888 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.947482638888889 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.890625 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.984375 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.890625 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.328125 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19999999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.890625 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.984375 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9551401340175182 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9396701388888888 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.939670138888889 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(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}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("joshuapb/fine-tuned-matryoshka-1000") |
|
# Run inference |
|
sentences = [ |
|
'(1) Joint: join with step 2, where the few-shot examples are structured as (response, verification questions, verification answers); The drawback is that the original response is in the context, so the model may repeat similar hallucination.\n(2) 2-step: separate the verification planning and execution steps, such as the original response doesn’t impact\n(3) Factored: each verification question is answered separately. Say, if a long-form base generation results in multiple verification questions, we would answer each question one-by-one.\n(4) Factor+revise: adding a “cross-checking” step after factored verification execution, conditioned on both the baseline response and the verification question and answer. It detects inconsistency.\n\n\nFinal output: Generate the final, refined output. The output gets revised at this step if any inconsistency is discovered.', |
|
"In what ways does the 'Factor+revise' method enhance the reliability of responses when compared to the 'Joint' and '2-step' methods used for cross-checking?", |
|
'What obstacles arise when depending on the pre-training dataset in the context of extrinsic hallucination affecting model outputs?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8802 | |
|
| cosine_accuracy@3 | 0.9844 | |
|
| cosine_accuracy@5 | 0.9948 | |
|
| cosine_accuracy@10 | 0.9948 | |
|
| cosine_precision@1 | 0.8802 | |
|
| cosine_precision@3 | 0.3281 | |
|
| cosine_precision@5 | 0.199 | |
|
| cosine_precision@10 | 0.0995 | |
|
| cosine_recall@1 | 0.8802 | |
|
| cosine_recall@3 | 0.9844 | |
|
| cosine_recall@5 | 0.9948 | |
|
| cosine_recall@10 | 0.9948 | |
|
| cosine_ndcg@10 | 0.9495 | |
|
| cosine_mrr@10 | 0.9338 | |
|
| **cosine_map@100** | **0.9342** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8854 | |
|
| cosine_accuracy@3 | 0.9844 | |
|
| cosine_accuracy@5 | 0.9948 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8854 | |
|
| cosine_precision@3 | 0.3281 | |
|
| cosine_precision@5 | 0.199 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8854 | |
|
| cosine_recall@3 | 0.9844 | |
|
| cosine_recall@5 | 0.9948 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9537 | |
|
| cosine_mrr@10 | 0.9378 | |
|
| **cosine_map@100** | **0.9378** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.901 | |
|
| cosine_accuracy@3 | 0.9844 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.901 | |
|
| cosine_precision@3 | 0.3281 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.901 | |
|
| cosine_recall@3 | 0.9844 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9588 | |
|
| cosine_mrr@10 | 0.9446 | |
|
| **cosine_map@100** | **0.9446** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.9062 | |
|
| cosine_accuracy@3 | 0.9844 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.9062 | |
|
| cosine_precision@3 | 0.3281 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.9062 | |
|
| cosine_recall@3 | 0.9844 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9609 | |
|
| cosine_mrr@10 | 0.9475 | |
|
| **cosine_map@100** | **0.9475** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8906 | |
|
| cosine_accuracy@3 | 0.9844 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8906 | |
|
| cosine_precision@3 | 0.3281 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8906 | |
|
| cosine_recall@3 | 0.9844 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9551 | |
|
| cosine_mrr@10 | 0.9397 | |
|
| **cosine_map@100** | **0.9397** | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_eval_batch_size`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 5 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `load_best_model_at_end`: True |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 8 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 5 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.04 | 5 | 4.9678 | - | - | - | - | - | |
|
| 0.08 | 10 | 4.6482 | - | - | - | - | - | |
|
| 0.12 | 15 | 5.0735 | - | - | - | - | - | |
|
| 0.16 | 20 | 4.0336 | - | - | - | - | - | |
|
| 0.2 | 25 | 3.7572 | - | - | - | - | - | |
|
| 0.24 | 30 | 4.3054 | - | - | - | - | - | |
|
| 0.28 | 35 | 2.6705 | - | - | - | - | - | |
|
| 0.32 | 40 | 3.1929 | - | - | - | - | - | |
|
| 0.36 | 45 | 3.1139 | - | - | - | - | - | |
|
| 0.4 | 50 | 2.5219 | - | - | - | - | - | |
|
| 0.44 | 55 | 3.1847 | - | - | - | - | - | |
|
| 0.48 | 60 | 2.2306 | - | - | - | - | - | |
|
| 0.52 | 65 | 2.251 | - | - | - | - | - | |
|
| 0.56 | 70 | 2.2432 | - | - | - | - | - | |
|
| 0.6 | 75 | 2.7462 | - | - | - | - | - | |
|
| 0.64 | 80 | 2.9992 | - | - | - | - | - | |
|
| 0.68 | 85 | 2.338 | - | - | - | - | - | |
|
| 0.72 | 90 | 2.0169 | - | - | - | - | - | |
|
| 0.76 | 95 | 1.257 | - | - | - | - | - | |
|
| 0.8 | 100 | 1.5015 | - | - | - | - | - | |
|
| 0.84 | 105 | 1.9198 | - | - | - | - | - | |
|
| 0.88 | 110 | 2.2154 | - | - | - | - | - | |
|
| 0.92 | 115 | 2.4026 | - | - | - | - | - | |
|
| 0.96 | 120 | 1.911 | - | - | - | - | - | |
|
| 1.0 | 125 | 2.079 | 0.9151 | 0.9098 | 0.9220 | 0.8788 | 0.9251 | |
|
| 1.04 | 130 | 1.4704 | - | - | - | - | - | |
|
| 1.08 | 135 | 0.7323 | - | - | - | - | - | |
|
| 1.12 | 140 | 0.6308 | - | - | - | - | - | |
|
| 1.16 | 145 | 0.4655 | - | - | - | - | - | |
|
| 1.2 | 150 | 1.0186 | - | - | - | - | - | |
|
| 1.24 | 155 | 1.1408 | - | - | - | - | - | |
|
| 1.28 | 160 | 1.965 | - | - | - | - | - | |
|
| 1.32 | 165 | 1.5987 | - | - | - | - | - | |
|
| 1.3600 | 170 | 3.288 | - | - | - | - | - | |
|
| 1.4 | 175 | 1.632 | - | - | - | - | - | |
|
| 1.44 | 180 | 1.0376 | - | - | - | - | - | |
|
| 1.48 | 185 | 0.9466 | - | - | - | - | - | |
|
| 1.52 | 190 | 1.0106 | - | - | - | - | - | |
|
| 1.56 | 195 | 1.4875 | - | - | - | - | - | |
|
| 1.6 | 200 | 1.314 | - | - | - | - | - | |
|
| 1.6400 | 205 | 1.3022 | - | - | - | - | - | |
|
| 1.6800 | 210 | 1.5312 | - | - | - | - | - | |
|
| 1.72 | 215 | 1.7982 | - | - | - | - | - | |
|
| 1.76 | 220 | 1.7962 | - | - | - | - | - | |
|
| 1.8 | 225 | 1.5788 | - | - | - | - | - | |
|
| 1.8400 | 230 | 1.152 | - | - | - | - | - | |
|
| 1.88 | 235 | 2.0556 | - | - | - | - | - | |
|
| 1.92 | 240 | 1.3165 | - | - | - | - | - | |
|
| 1.96 | 245 | 0.6941 | - | - | - | - | - | |
|
| **2.0** | **250** | **1.2239** | **0.9404** | **0.944** | **0.9427** | **0.9327** | **0.9424** | |
|
| 2.04 | 255 | 1.0423 | - | - | - | - | - | |
|
| 2.08 | 260 | 0.8893 | - | - | - | - | - | |
|
| 2.12 | 265 | 1.2859 | - | - | - | - | - | |
|
| 2.16 | 270 | 1.4505 | - | - | - | - | - | |
|
| 2.2 | 275 | 0.2728 | - | - | - | - | - | |
|
| 2.24 | 280 | 0.6588 | - | - | - | - | - | |
|
| 2.2800 | 285 | 0.8014 | - | - | - | - | - | |
|
| 2.32 | 290 | 0.3053 | - | - | - | - | - | |
|
| 2.36 | 295 | 1.4289 | - | - | - | - | - | |
|
| 2.4 | 300 | 1.1458 | - | - | - | - | - | |
|
| 2.44 | 305 | 0.6987 | - | - | - | - | - | |
|
| 2.48 | 310 | 1.3389 | - | - | - | - | - | |
|
| 2.52 | 315 | 1.2991 | - | - | - | - | - | |
|
| 2.56 | 320 | 1.8088 | - | - | - | - | - | |
|
| 2.6 | 325 | 0.4242 | - | - | - | - | - | |
|
| 2.64 | 330 | 1.5873 | - | - | - | - | - | |
|
| 2.68 | 335 | 1.3873 | - | - | - | - | - | |
|
| 2.7200 | 340 | 1.4297 | - | - | - | - | - | |
|
| 2.76 | 345 | 2.0637 | - | - | - | - | - | |
|
| 2.8 | 350 | 1.1252 | - | - | - | - | - | |
|
| 2.84 | 355 | 0.367 | - | - | - | - | - | |
|
| 2.88 | 360 | 1.7606 | - | - | - | - | - | |
|
| 2.92 | 365 | 1.196 | - | - | - | - | - | |
|
| 2.96 | 370 | 1.8827 | - | - | - | - | - | |
|
| 3.0 | 375 | 0.6822 | 0.9494 | 0.9479 | 0.9336 | 0.9414 | 0.9405 | |
|
| 3.04 | 380 | 0.4954 | - | - | - | - | - | |
|
| 3.08 | 385 | 0.1717 | - | - | - | - | - | |
|
| 3.12 | 390 | 0.7435 | - | - | - | - | - | |
|
| 3.16 | 395 | 1.4323 | - | - | - | - | - | |
|
| 3.2 | 400 | 1.1207 | - | - | - | - | - | |
|
| 3.24 | 405 | 1.9009 | - | - | - | - | - | |
|
| 3.2800 | 410 | 1.6706 | - | - | - | - | - | |
|
| 3.32 | 415 | 0.8378 | - | - | - | - | - | |
|
| 3.36 | 420 | 1.0911 | - | - | - | - | - | |
|
| 3.4 | 425 | 0.6565 | - | - | - | - | - | |
|
| 3.44 | 430 | 1.0302 | - | - | - | - | - | |
|
| 3.48 | 435 | 0.6425 | - | - | - | - | - | |
|
| 3.52 | 440 | 1.1472 | - | - | - | - | - | |
|
| 3.56 | 445 | 1.996 | - | - | - | - | - | |
|
| 3.6 | 450 | 1.5308 | - | - | - | - | - | |
|
| 3.64 | 455 | 0.7427 | - | - | - | - | - | |
|
| 3.68 | 460 | 1.4596 | - | - | - | - | - | |
|
| 3.7200 | 465 | 1.1984 | - | - | - | - | - | |
|
| 3.76 | 470 | 0.7601 | - | - | - | - | - | |
|
| 3.8 | 475 | 1.3544 | - | - | - | - | - | |
|
| 3.84 | 480 | 1.6655 | - | - | - | - | - | |
|
| 3.88 | 485 | 1.2596 | - | - | - | - | - | |
|
| 3.92 | 490 | 0.9451 | - | - | - | - | - | |
|
| 3.96 | 495 | 0.7079 | - | - | - | - | - | |
|
| 4.0 | 500 | 1.3471 | 0.9453 | 0.9446 | 0.9404 | 0.9371 | 0.9335 | |
|
| 4.04 | 505 | 0.4583 | - | - | - | - | - | |
|
| 4.08 | 510 | 1.288 | - | - | - | - | - | |
|
| 4.12 | 515 | 1.6946 | - | - | - | - | - | |
|
| 4.16 | 520 | 1.1239 | - | - | - | - | - | |
|
| 4.2 | 525 | 1.1026 | - | - | - | - | - | |
|
| 4.24 | 530 | 1.4121 | - | - | - | - | - | |
|
| 4.28 | 535 | 1.7113 | - | - | - | - | - | |
|
| 4.32 | 540 | 0.8389 | - | - | - | - | - | |
|
| 4.36 | 545 | 0.3117 | - | - | - | - | - | |
|
| 4.4 | 550 | 0.3144 | - | - | - | - | - | |
|
| 4.44 | 555 | 1.4694 | - | - | - | - | - | |
|
| 4.48 | 560 | 1.3233 | - | - | - | - | - | |
|
| 4.52 | 565 | 0.792 | - | - | - | - | - | |
|
| 4.5600 | 570 | 0.4881 | - | - | - | - | - | |
|
| 4.6 | 575 | 0.5097 | - | - | - | - | - | |
|
| 4.64 | 580 | 1.6377 | - | - | - | - | - | |
|
| 4.68 | 585 | 0.7273 | - | - | - | - | - | |
|
| 4.72 | 590 | 1.5464 | - | - | - | - | - | |
|
| 4.76 | 595 | 1.4392 | - | - | - | - | - | |
|
| 4.8 | 600 | 1.4384 | - | - | - | - | - | |
|
| 4.84 | 605 | 0.6375 | - | - | - | - | - | |
|
| 4.88 | 610 | 1.0528 | - | - | - | - | - | |
|
| 4.92 | 615 | 0.0276 | - | - | - | - | - | |
|
| 4.96 | 620 | 0.9604 | - | - | - | - | - | |
|
| 5.0 | 625 | 0.7219 | 0.9475 | 0.9446 | 0.9378 | 0.9397 | 0.9342 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.3.1+cu121 |
|
- Accelerate: 0.32.1 |
|
- Datasets: 2.21.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
|
year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
|
``` |
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|
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