Simplify models for sentence similarity
Make models for sentence similarity consistent with other scenarios.
@@ -52,7 +52,7 @@ The following is a summary of the commonly used NLP scenarios covered in the rep
|Named Entity Recognition |BERT| Named entity recognition (NER) is the task of classifying words or key phrases of a text into predefined entities of interest. |English|
|Entailment |BERT, XLNet, RoBERTa| Textual entailment is the task of classifying the binary relation between two natural-language texts, *text* and *hypothesis*, to determine if the *text* agrees with the *hypothesis* or not. |English|
|Question Answering |BiDAF, BERT, XLNet| Question answering (QA) is the task of retrieving or generating a valid answer for a given query in natural language, provided with a passage related to the query. |English|
-|Sentence Similarity |Representation: TF-IDF, Word Embeddings, Doc Embeddings<br>Metrics: Cosine Similarity, Word Mover's Distance<br>Models: BERT, GenSen| Sentence similarity is the process of computing a similarity score given a pair of text documents. |English|
+|Sentence Similarity |BERT, GenSen| Sentence similarity is the process of computing a similarity score given a pair of text documents. |English|
|Embeddings| Word2Vec<br>fastText<br>GloVe| Embedding is the process of converting a word or a piece of text to a continuous vector space of real number, usually, in low dimension.|English|
## Getting Started
