Major update to README.md for model card

README.md

@@ -11,31 +11,30 @@ tags:
 - loss:ContrastiveLoss
 base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
 widget:
-- source_sentence: مانوئلا دی سنتا
+- source_sentence: Russell Jurney
   sentences:
-  - Renko Kitagawa
-  - هانس هيرمان وير
-  - Ди Чента, Мануэла
-- source_sentence: يورى جافريلوف
+  - Russell H. Jurney
+  - Russ Jurney
+  - Русс Джерни
+- source_sentence: Ben Lorica
   sentences:
-  - Wiktor Pinczuk
-  - Natalia Germanovna DIRKS
-  - Світлана Євгенівна Савицька
-- source_sentence: Џуди Колинс
+  - Benjamin Lorica
+  - 罗瑞卡
+  - 罗睿姬
+- source_sentence: Yevgeny Prigozhin
   sentences:
-  - Collins
-  - Aisha Muhammed Abdul Salam
-  - Phonic Boy On Dope
-- source_sentence: ויליאם בלייר
+  - Евге́ний Ви́кторович Приго́жин
+  - Y. Prighozhin
+- source_sentence: M.R. James
   sentences:
-  - The Hon. Mr Justice Blair
-  - Queen Ingrid of Denmark
-  - Herman van Rompuy
-- source_sentence: Saif al-Arab GADAFI
+  - Montague Rhodes James
+  - J.R. James
+  - Mr. James
+- source_sentence: Muhammad Ali
   sentences:
-  - Максім Недасекаў
-  - Mervyn Allister King
-  - Paul d. scully-power
+  - مُحَمَّد عَلِيّ
+  - Mohammed Ali
+  - Sonny Liston
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
@@ -48,7 +47,7 @@ metrics:
 - cosine_ap
 - cosine_mcc
 model-index:
-- name: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-name-matcher-original
+- name: Graphlet-AI/eridu
   results:
   - task:
       type: binary-classification
@@ -83,9 +82,9 @@ model-index:
       name: Cosine Mcc
 ---
 
-# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-name-matcher-original
+# Graphlet-AI/eridu
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) for person and company name matching using the [Open Sanctions matcher training data](https://www.opensanctions.org/docs/pairs/). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used as part of a deep, fuzzy entity resolution process.
 
 ## Model Details
 
@@ -101,9 +100,9 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [s
 
 ### 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)
+- **Documentation:** [Graphlet-AI/eridu Documentation](https://github.com/Graphlet-AI/eridu)
+- **Repository:** [Graphlet-AI/eridu on GitHub](https://github.com/Graphlet-AI/eridu)
+- **Hugging Face:** [Graphlet-AI/eridu on Hugging Face](https://huggingface.co/Graphlet-AI/eridu)
 
 ### Full Model Architecture
 
@@ -129,14 +128,15 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'Saif al-Arab GADAFI',
-    'Максім Недасекаў',
-    'Mervyn Allister King',
+model = SentenceTransformer("Graphlet-AI/eridu")
+
+names = [
+    "Russell Jurney",
+    "Russ Jurney",
+    "Русс Джерни",
 ]
-embeddings = model.encode(sentences)
+
+embeddings = model.encode(names)
 print(embeddings.shape)
 # [3, 384]
 
@@ -144,6 +144,11 @@ print(embeddings.shape)
 similarities = model.similarity(embeddings, embeddings)
 print(similarities.shape)
 # [3, 3]
+
+print(similarities.numpy())
+# [[0.9999999  0.99406826 0.99406105]
+#  [0.9940683  1.         0.9969202 ]
+#  [0.99406105 0.9969202  1.        ]]
 ```
 
 <!--
@@ -157,7 +162,7 @@ print(similarities.shape)
 <!--
 ### Downstream Usage (Sentence Transformers)
 
-You can finetune this model on your own dataset.
+You can fine-tune this model on your own dataset.
 
 <details><summary>Click to expand</summary>