Update README.md

README.md

@@ -1,7 +1,8 @@
 ---
 base_model: FacebookAI/roberta-base
 datasets:
-- SynthSTEL/styledistance_training_triplets_v2
+- SynthSTEL/styledistance_training_triplets
+- StyleDistance/synthstel
 library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
@@ -13,29 +14,47 @@ tags:
 - sentence-similarity
 widget:
 - example_title: Example 1
-  source_sentence: "The Harley School will thrive\uD83D\uDE00; so many future leaders\uD83C\
-    \uDF93 will emerge from its doors\uD83D\uDEAA."
+  source_sentence: >-
+    Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from
+    future competitions.
   sentences:
-  - "To avoid extra attorney fees\uD83D\uDCB0, they kept the entity's minutes updated⏱️;\
-    \ it proved a wise decision\uD83D\uDC4D."
-  - To avoid extra attorney fees, they kept the entity's minutes updated; it proved
-    a wise decision.
+  - >-
+    We're raising funds 2 improve our school's storage facilities and add new
+    playground equipment!
+  - >-
+    Did you hear about the Wales wing? He'll hate to withdraw due to injuries
+    from future competitions.
 - example_title: Example 2
-  source_sentence: In Segovia, I will shoulder the duty of raising suckling pigs;
-    their care will be my responsibility too.
+  source_sentence: >-
+    You planned the DesignMeets Decades of Design event; you executed it
+    perfectly.
   sentences:
-  - Despite facing routine criticism, I continue to support the display of religious
-    texts in public buildings.
-  - I'll raise suckling pigs in Segovia; I'll care for them as well.
+  - We'll find it hard to prove the thief didn't face a real threat!
+  - >-
+    You orchestrated the DesignMeets Decades of Design gathering; you actualized
+    it flawlessly.
 - example_title: Example 3
-  source_sentence: You'll play Far Cry 2 on Xbox; I prefer Far Cry 3 on PlayStation.
+  source_sentence: >-
+    Did the William Barr maintain a commitment to allow Robert Mueller to finish
+    the inquiry?
   sentences:
-  - I endured significant challenges when my partner was in prison.
-  - They'll play Far Cry 2 on Xbox; they prefer Far Cry 3 on PlayStation.
+  - >-
+    Will the artist be compiling a music album, or will there be a different
+    focus in the future?
+  - >-
+    Did William Barr maintain commitment to allow Robert Mueller to finish
+    inquiry?
+license: mit
+language:
+- en
 ---
 # Model Card
 
-[Add more information here](https://huggingface.co/templates/model-card-example)
+StyleDistance is a **style embedding model** that aims to embed texts with similar writing styles closely and different styles far apart, regardless of content. You may find this model useful for stylistic analysis of text, clustering, authorship identfication and verification tasks, and automatic style transfer evaluation.
+
+## Training Data and Variants of StyleDistance
+
+StyleDistance was contrastively trained on [SynthSTEL](https://huggingface.co/datasets/StyleDistance/synthstel), a synthetically generated dataset of positive and negative examples of 40 style features being used in text. By utilizing this synthetic dataset, StyleDistance is able to achieve stronger content-independence than other style embeddding models currently available. This particular model was purely trained on synthetic data. For a version that is trained using a combination of the synthetic dataset and a [real dataset that makes use of authorship datasets from Reddit to train style embeddings](https://aclanthology.org/2022.repl4nlp-1.26/), see this other version of [StyleDistance](https://huggingface.co/StyleDistance/styledistance). 
 
 ## Example Usage
 
@@ -43,10 +62,10 @@ widget:
 from sentence_transformers import SentenceTransformer
 from sentence_transformers.util import cos_sim
 
-model = SentenceTransformer('SynthSTEL/styledistance_synthetic_only_full_v2') # Load model
+model = SentenceTransformer('SynthSTEL/styledistance_synth_only') # Load model
 
-input = model.encode('The Harley School will thrive😀; so many future leaders🎓 will emerge from its doors🚪.')
-others = model.encode(["To avoid extra attorney fees💰, they kept the entity's minutes updated⏱️; it proved a wise decision👍.", "To avoid extra attorney fees, they kept the entity's minutes updated; it proved a wise decision."])
+input = model.encode("Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from future competitions.")
+others = model.encode(["We're raising funds 2 improve our school's storage facilities and add new playground equipment!", "Did you hear about the Wales wing? He'll hate to withdraw due to injuries from future competitions."])
 print(cos_sim(input, others))
 ```