Update README.md

README.md

@@ -1,63 +1,64 @@
 ---
 pipeline_tag: sentence-similarity
+license: apache-2.0
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
+datasets:
+- clips/mfaq
 ---
 
-# clips/mfaq
+# MFAQ
 
-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.
+This is a FAQ retrieval model, it ranks potential answers according to a given question. It was trained using the [MFAQ dataset](https://huggingface.co/datasets/clips/mfaq).
 
-<!--- Describe your model here -->
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+## Installation
 
 ```
 pip install -U sentence-transformers
 ```
 
-Then you can use the model like this:
+## Usage
+You can use MFAQ with sentence-transformers or directly with a HuggingFace model. 
+In both cases, questions need to be prepended with `<Q>`, and answers with `<A>`.
 
+#### Sentence Transformers
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+
+question = "<Q>How many models can I host on HuggingFace?"
+answer_1 = "<A>All plans come with unlimited private models and datasets."
+answer_2 = "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem."
+answer_3 = "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."
 
 model = SentenceTransformer('clips/mfaq')
-embeddings = model.encode(sentences)
+embeddings = model.encode([question, answer_1, answer_3, answer_3])
 print(embeddings)
 ```
 
-
-
-## 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 right pooling-operation on-top of the contextualized word embeddings.
+#### HuggingFace Transfoormers
 
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch
 
-
-#Mean Pooling - Take attention mask into account for correct averaging
 def mean_pooling(model_output, attention_mask):
     token_embeddings = model_output[0] #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)
 
+question = "<Q>How many models can I host on HuggingFace?"
+answer_1 = "<A>All plans come with unlimited private models and datasets."
+answer_2 = "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem."
+answer_3 = "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."
 
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('clips/mfaq')
 model = AutoModel.from_pretrained('clips/mfaq')
 
 # Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+encoded_input = tokenizer([question, answer_1, answer_3, answer_3], padding=True, truncation=True, return_tensors='pt')
 
 # Compute token embeddings
 with torch.no_grad():
@@ -65,29 +66,4 @@ with torch.no_grad():
 
 # Perform pooling. In this case, max pooling.
 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-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=clips/mfaq)
-
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (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 -->
+```