Update README.md

README.md

@@ -11,14 +11,13 @@ datasets:
 - Salesforce/dialogstudio
 pipeline_tag: sentence-similarity
 base_model:
-- aws-ai/dse-bert-base
+- google-bert/bert-base-uncased
 ---
 
 
-# Dialog2Flow single target (DSE-base)
+# Dialog2Flow joint target (BERT-base)
 
-This a variation of the **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.
-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).
+This is the original **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.
 
 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.
 
@@ -38,7 +37,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["your phone please", "okay may i have your telephone number please"]
 
-model = SentenceTransformer('sergioburdisso/dialog2flow-single-dse-base')
+model = SentenceTransformer('sergioburdisso/dialog2flow-joint-bert-base')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -64,8 +63,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['your phone please', 'okay may i have your telephone number please']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('sergioburdisso/dialog2flow-single-dse-base')
-model = AutoModel.from_pretrained('sergioburdisso/dialog2flow-single-dse-base')
+tokenizer = AutoTokenizer.from_pretrained('sergioburdisso/dialog2flow-joint-bert-base')
+model = AutoModel.from_pretrained('sergioburdisso/dialog2flow-joint-bert-base')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -154,4 +153,4 @@ SentenceTransformer(
 ## License
 
 Copyright (c) 2024 [Idiap Research Institute](https://www.idiap.ch/).
-MIT License.
+MIT License.