PBusienei
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Nashville_Analytics_Summit_conference_helper

@@ -7,22 +7,22 @@ tags:
 ---
 # multi-qa-MiniLM-L6-cos-v1
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
 ## Usage (Sentence-Transformers)
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 ```
 pip install -U sentence-transformers
 ```
-Then you can use the model like this:
 ```python
 from sentence_transformers import SentenceTransformer, util
-query = "How many people live in London?"
-docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 #Load the model
 model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
@@ -47,16 +47,19 @@ for doc, score in doc_score_pairs:
 ## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch
 import torch.nn.functional as F
 #Mean Pooling - Take average of all tokens
 def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings
     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
@@ -80,8 +83,8 @@ def encode(texts):
 # Sentences we want sentence embeddings for
-query = "How many people live in London?"
-docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
@@ -126,14 +129,11 @@ Note: When loaded with `sentence-transformers`, this model produces normalized e
 The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
 contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
-We developped this model during the
-[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
-organized by Hugging Face. We developped this model as part of the project:
-[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
 ## Intended uses
-Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
 Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
@@ -141,11 +141,11 @@ Note that there is a limit of 512 word pieces: Text longer than that will be tru
 ## Training procedure
-The full training script is accessible in this current repository: `train_script.py`.
 ### Pre-training
-We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
 #### Training
@@ -156,24 +156,3 @@ The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/
-| Dataset                    | Number of training tuples  |
-|--------------------------------------------------------|:--------------------------:|
-| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers |  77,427,422 |
-| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges  | 25,316,456 |
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges  |  21,396,559 |
-| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine |  17,579,773 |
-| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet  | 3,012,496 |
-| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839
-| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
-| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
-| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
-| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
-| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
-| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
-| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
-| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset |  87,599 |
-| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
-| **Total** | **214,988,242** |

 ---
 # multi-qa-MiniLM-L6-cos-v1
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources.
 ## Usage (Sentence-Transformers)
+The usage of this model is easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 ```
 pip install -U sentence-transformers
 ```
+Thus the model can be used as:
 ```python
 from sentence_transformers import SentenceTransformer, util
+query = "Health Analytics?"
+docs = ["The output is 3 top most similar sessions from the summit"]
 #Load the model
 model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
 ## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can take the following steps:
+1. Pass input through the transformer model,
+2. Apply the correct pooling-operation on-top of the contextualized word embeddings.
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch
 import torch.nn.functional as F
 #Mean Pooling - Take average of all tokens
 def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output.last_hidden_state #The first element of model_output containing all token embeddings
     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 # Sentences we want sentence embeddings for
+query = "Health Analytics?"
+docs = ["The output is 3 top most similar sessions from the summit"]
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
 The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
 contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
 ## Intended uses
+The model is intended to be used for semantic search at Nashville Analytics Summit: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
 Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
 ## Training procedure
+The full training script is accessible in: `train_script.py`.
 ### Pre-training
+The pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model.
 #### Training