Question: I am making a project like this one here: https://www.youtube.com/watch?v=dovB8uSUUXE&feature=youtu.be but i am facing trouble because i need to check the similarity between the sentences for example: if the user said: 'the person wear red T-shirt' instead of 'the boy wear red T-shirt' I want a method to check the similarity between these two sentences without having to check the similarity between each word is there a way to do this in python? I am trying to find a way to check the similarity between two sentences.

Answer: Most of there libraries below should be good choice for semantic similarity comparison. You can skip direct word comparison by generating word, or sentence vectors using pretrained models from these libraries. Sentence similarity with Spacy Required models must be loaded first. For using en_core_web_md use python -m spacy download en_core_web_md to download. For using en_core_web_lg use python -m spacy download en_core_web_lg. The large model is around ~830mb as writing and quite slow, so medium one can be a good choice. https://spacy.io/usage/vectors-similarity/ Code: import spacy nlp = spacy.load("en_core_web_lg") #nlp = spacy.load("en_core_web_md") doc1 = nlp(u'the person wear red T-shirt') doc2 = nlp(u'this person is walking') doc3 = nlp(u'the boy wear red T-shirt') print(doc1.similarity(doc2)) print(doc1.similarity(doc3)) print(doc2.similarity(doc3))  Output: 0.7003971105290047 0.9671912343259517 0.6121211244876517  Sentence similarity with Sentence Transformers https://github.com/UKPLab/sentence-transformers https://www.sbert.net/docs/usage/semantic_textual_similarity.html Install with pip install -U sentence-transformers. This one generates sentence embedding. Code: from sentence_transformers import SentenceTransformer model = SentenceTransformer('distilbert-base-nli-mean-tokens') sentences = [ 'the person wear red T-shirt', 'this person is walking', 'the boy wear red T-shirt' ] sentence_embeddings = model.encode(sentences) for sentence, embedding in zip(sentences, sentence_embeddings): print("Sentence:", sentence) print("Embedding:", embedding) print("")  Output: Sentence: the person wear red T-shirt Embedding: [ 1.31643847e-01 -4.20616418e-01 ... 8.13076794e-01 -4.64620918e-01] Sentence: this person is walking Embedding: [-3.52878094e-01 -5.04286848e-02 ... -2.36091137e-01 -6.77282438e-02] Sentence: the boy wear red T-shirt Embedding: [-2.36365378e-01 -8.49713564e-01 ... 1.06414437e+00 -2.70157874e-01]  Now embedding vector can be used to calculate various similarity metrics. Code: from sentence_transformers import SentenceTransformer, util print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[1])) print(util.pytorch_cos_sim(sentence_embeddings[0], sentence_embeddings[2])) print(util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[2]))  Output: tensor([[0.4644]]) tensor([[0.9070]]) tensor([[0.3276]])  Same thing with scipy and pytorch, Code: from scipy.spatial import distance print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1])) print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[2])) print(1 - distance.cosine(sentence_embeddings[1], sentence_embeddings[2]))  Output: 0.4643629193305969 0.9069876074790955 0.3275738060474396  Code: import torch.nn cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6) b = torch.from_numpy(sentence_embeddings) print(cos(b[0], b[1])) print(cos(b[0], b[2])) print(cos(b[1], b[2]))  Output: tensor(0.4644) tensor(0.9070) tensor(0.3276)  Sentence similarity with TFHub Universal Sentence Encoder https://tfhub.dev/google/universal-sentence-encoder/4 https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb Model is very large for this one around 1GB and seems slower than others. This also generates embeddings for sentences. Code: import tensorflow_hub as hub embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4") embeddings = embed([ "the person wear red T-shirt", "this person is walking", "the boy wear red T-shirt" ]) print(embeddings)  Output: tf.Tensor( [[ 0.063188 0.07063895 -0.05998802 ... -0.01409875 0.01863449 0.01505797] [-0.06786212 0.01993554 0.03236153 ... 0.05772103 0.01787272 0.01740014] [ 0.05379306 0.07613157 -0.05256693 ... -0.01256405 0.0213196 -0.00262441]], shape=(3, 512), dtype=float32)  Code: from scipy.spatial import distance print(1 - distance.cosine(embeddings[0], embeddings[1])) print(1 - distance.cosine(embeddings[0], embeddings[2])) print(1 - distance.cosine(embeddings[1], embeddings[2]))  Output: 0.15320375561714172 0.8592830896377563 0.09080004692077637  Other Sentence Embedding Libraries https://github.com/facebookresearch/InferSent https://github.com/Tiiiger/bert_score This illustration shows the method,  Resources How to compute the similarity between two text documents? https://en.wikipedia.org/wiki/Cosine_similarity#Angular_distance_and_similarity https://towardsdatascience.com/word-distance-between-word-embeddings-cc3e9cf1d632 https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.spatial.distance.cosine.html https://www.tensorflow.org/api_docs/python/tf/keras/losses/CosineSimilarity https://nlp.town/blog/sentence-similarity/