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update Model Card

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  1. README.md +9 -5
README.md CHANGED
@@ -8,7 +8,11 @@ tags:
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  ---
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- # {MODEL_NAME}
 
 
 
 
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  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.
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@@ -28,7 +32,7 @@ Then you can use the model like this:
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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- model = SentenceTransformer('{MODEL_NAME}')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
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- model = AutoModel.from_pretrained('{MODEL_NAME}')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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  <!--- Describe how your model was evaluated -->
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- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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  ## Training
 
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  ---
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+ # Brendan/refpydst-100p-referredstates-referred-states
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+
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+ This model was initialized with `sentence-transformers/all-mpnet-base-v2` and then fine-tuned using a 100% few-shot split of the MultiWOZ dataset and a supervised contrastive loss. It is fine-tuned to be used as an in-context example retriever using this few-shot training set, which is provided in the linked repository. More details available [in the repo](https://github.com/jlab-nlp/RefPyDST) and paper linked within. To cite this model, please consult the citation in the [linked GithHub repository README](https://github.com/jlab-nlp/RefPyDST).
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+ The remainder of this README is automatically generated from `sentence_transformers` and is accurate, though this model is not intended as a general purpose sentence-encoder: it is expecting in-context examples from MultiWOZ to be formatted in a particular way, see the linked repo for details.
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  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.
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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+ model = SentenceTransformer('Brendan/refpydst-100p-referredstates-referred-states')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
 
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('Brendan/refpydst-100p-referredstates-referred-states')
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+ model = AutoModel.from_pretrained('Brendan/refpydst-100p-referredstates-referred-states')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
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  <!--- Describe how your model was evaluated -->
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+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Brendan/refpydst-100p-referredstates-referred-states)
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  ## Training