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  ---
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- pipeline_tag: sentence-similarity
 
 
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  tags:
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  - sentence-transformers
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- - feature-extraction
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  - sentence-similarity
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- - transformers
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-
 
 
 
 
 
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  ---
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- # sergioburdisso/dialog2flow-joint-dse-base
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- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
 
 
 
 
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  <!--- Describe your model here -->
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  ```python
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  from sentence_transformers import SentenceTransformer
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- sentences = ["This is an example sentence", "Each sentence is converted"]
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  model = SentenceTransformer('sergioburdisso/dialog2flow-joint-dse-base')
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  embeddings = model.encode(sentences)
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  # Sentences we want sentence embeddings for
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- sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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  tokenizer = AutoTokenizer.from_pretrained('sergioburdisso/dialog2flow-joint-dse-base')
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  print(sentence_embeddings)
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  ```
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-
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-
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- ## Evaluation Results
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-
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- <!--- Describe how your model was evaluated -->
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-
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- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sergioburdisso/dialog2flow-joint-dse-base)
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-
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-
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  ## Training
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  The model was trained with the parameters:
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  ## Citing & Authors
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- <!--- Describe where people can find more information -->
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language: en
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+ license: mit
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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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+ - task-oriented-dialogues
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+ - dialog-flow
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+ datasets:
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+ - Salesforce/dialogstudio
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+ pipeline_tag: sentence-similarity
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+ base_model:
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+ - aws-ai/dse-bert-base
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  ---
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+ # Dialog2Flow joint target (DSE-base)
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+
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+ This a variation of the **D2F$_{joint}$** model introduced in the paper ["Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction"](https://publications.idiap.ch/attachments/papers/2024/Burdisso_EMNLP2024_2024.pdf) published in the EMNLP 2024 main conference.
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+ This version uses DSE-base as the backbone model which yields to an increase in performance as compared to the vanilla version using BERT-base as the backbone (results reported in Appendix C).
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+
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+ Implementation-wise, this is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or search.
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  <!--- Describe your model here -->
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  ```python
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  from sentence_transformers import SentenceTransformer
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+ sentences = ["your phone please", "okay may i have your telephone number please"]
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  model = SentenceTransformer('sergioburdisso/dialog2flow-joint-dse-base')
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  embeddings = model.encode(sentences)
 
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  # Sentences we want sentence embeddings for
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+ sentences = ['your phone please', 'okay may i have your telephone number please']
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  # Load model from HuggingFace Hub
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  tokenizer = AutoTokenizer.from_pretrained('sergioburdisso/dialog2flow-joint-dse-base')
 
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  print(sentence_embeddings)
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  ```
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  ## Training
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  The model was trained with the parameters:
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  ## Citing & Authors
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+
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+ ```bibtex
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+ @inproceedings{burdisso-etal-2024-dialog2flow,
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+ title = "Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction",
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+ author = "Burdisso, Sergio and
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+ Madikeri, Srikanth and
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+ Motlicek, Petr",
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+ booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
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+ month = nov,
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+ year = "2024",
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+ address = "Miami",
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+ publisher = "Association for Computational Linguistics",
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+ }
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+ ```
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+
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+ ## License
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+
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+ Copyright (c) 2024 [Idiap Research Institute](https://www.idiap.ch/).
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+ MIT License.