Update README.md

README.md

@@ -5,12 +5,19 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+language: 
+  - en
+license: mit
+datasets:
+- snli
+- stsb
 ---
 
-# {MODEL_NAME}
+# MPNet NLI and STS
+
+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. It uses the [jamescalam/mpnet-snli-negatives](https://huggingface.co/jamescalam/mpnet-snli-negatives) model as a starting point, and is fine-tuned further on the **S**emantic **T**extual **S**imilarity **b**enchmark (STSb) dataset. Returning evaluation scores of ~0.9 cosine Pearson correlation using the STSb test set.
 
-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.
+Find more info from [James Briggs on YouTube](https://youtube.com/c/jamesbriggs) or in the [**free** NLP for Semantic Search ebook](https://pinecone.io/learn/nlp).
 
 <!--- Describe your model here -->
 
@@ -28,7 +35,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('jamescalam/mpnet-nli-sts')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -54,8 +61,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('jamescalam/mpnet-nli-sts')
+model = AutoModel.from_pretrained('jamescalam/mpnet-nli-sts')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -72,14 +79,6 @@ print(sentence_embeddings)
 ```
 
 
-
-## 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={MODEL_NAME})
-
-
 ## Training
 The model was trained with the parameters:
 
@@ -112,7 +111,6 @@ Parameters of the fit()-Method:
 }
 ```
 
-
 ## Full Model Architecture
 ```
 SentenceTransformer(
@@ -120,7 +118,3 @@ SentenceTransformer(
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
 )
 ```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->