Update README.md

README.md

@@ -1,16 +1,25 @@
 ---
-pipeline_tag: sentence-similarity
+language: en
+license: mit
+library_name: sentence-transformers
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
-
+datasets:
+- Salesforce/dialogstudio
+pipeline_tag: sentence-similarity
+base_model:
+- google-bert/bert-base-uncased
 ---
 
-# sergioburdisso/dialog2flow-single-bert-base
 
-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.
+# Dialog2Flow single target (BERT-base)
+
+This is the original **D2F$_{single}$** 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.
+
+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.
 
 <!--- Describe your model here -->
 
@@ -26,7 +35,7 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["your phone please", "okay may i have your telephone number please"]
 
 model = SentenceTransformer('sergioburdisso/dialog2flow-single-bert-base')
 embeddings = model.encode(sentences)
@@ -51,7 +60,7 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ['your phone please', 'okay may i have your telephone number please']
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('sergioburdisso/dialog2flow-single-bert-base')
@@ -71,21 +80,23 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
+## Training
+The model was trained with the parameters:
 
+**DataLoader**:
 
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sergioburdisso/dialog2flow-single-bert-base)
+`torch.utils.data.dataloader.DataLoader` of length 363506 with parameters:
+```
+{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
 
+**Loss**:
 
-## Training
-The model was trained with the parameters:
+`spretrainer.losses.LabeledContrastiveLoss.LabeledContrastiveLoss` 
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 24615 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 49478 with parameters:
 ```
 {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
@@ -98,7 +109,7 @@ Parameters of the fit()-Method:
 ```
 {
     "epochs": 15,
-    "evaluation_steps": 246,
+    "evaluation_steps": 164,
     "evaluator": [
         "spretrainer.evaluation.FewShotClassificationEvaluator.FewShotClassificationEvaluator"
     ],
@@ -124,4 +135,22 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+
+```bibtex
+@inproceedings{burdisso-etal-2024-dialog2flow,
+    title = "Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction",
+    author = "Burdisso, Sergio  and
+      Madikeri, Srikanth  and
+      Motlicek, Petr",
+    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2024",
+    address = "Miami",
+    publisher = "Association for Computational Linguistics",
+}
+```
+
+## License
+
+Copyright (c) 2024 [Idiap Research Institute](https://www.idiap.ch/).
+MIT License.