• 👋 Hi, I’m @Nico070707
• 👀 I’m interested in real Data Humanity to build a better world. A real dream could begin a real life
• 🌱 I’m currently learning DataScience
• 💞️ I’m looking to collaborate on Project for humanity with code
• 📫 How to reach me https://www.linkedin.com/in/nicolas-bourne-635a0033/

corpus = ["I Like Python because I can build AI applications", "I like Python because I can do data analytics","The cat sits on the ground","The cat walks on the sidewalk"] corpus_embeddings = model.encode(corpus, convert_to_tensor=True) sentence = "I like Javascript because I can build web applications" sentence_embedding = model.encode(sentence, convert_to_tensor=True) cos_scores = util.pytorch_cos_sim(sentence_embedding, corpus_embeddings)[0] top_results = np.argpartition(-cos_scores, range(top_k))[0:top_k] print("Sentence:",sentence, "\n") print("Top", top_k, "most similar sentences in corpus:") for idx in top_results(0:top_k]: print(corpus[idx], "(Score: %.4f)" % (cos_scores[idx])) sentences = ['This framework generates embeddings for each input sentence','Sentence are passed as a list of string, ','The quick brown for jumps over the lazy dog.') tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/bert-base-nli-mean-tokens") model = AutoModel.from_pretrained("sentence-transformers/bert-base-nli-mean-tokens") encoded input + tokenizer(sentences, padding=True, truncation=True, max_lengh=128, return_tensors='pt') with torch.no_grad{) : model_ouput = model(**encoded_input sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) util.pytorch_cos_sim(sentence_embeddings[1], sentence_embeddings[0],numpy()[0][0] model = SentenceTransformer('distilroberta-base-paraphrase-v1') embeddings = model.encode(sentences, convert_to_tensor=True) cosine_scores = util.pytorch_cos_sim(embeddings,embeddings) g = nx.from_numpy_matrix(cosine_scores.numpy()) centrality_scores = nx.degree_centrality(g) most_central_sentence_indices = np.argsort(centrality_scores) print("\n\nSummary:")

sentence 1 = "I Like Python because I can build AI applications" sentence 2 = "I Like Python because I can do data analytics" embedding1 = model.encode(sentence1, convert_to_tensor=True) embedding2 = model.encore(sentence2, convert_to_tensor=True) cosine scores = util.pytorch_cos_sim(embedding1, embedding2) print("Sentence 1:", sentence1) print("Sentence 2:", sentence2) print("Similarity score", cosine_scores.itrem()) sentence1 = ["I Like Python because I can build AI applications, "The cat sits on the ground"] sentence2 = [I Like Python because I can do data anaytics", "The cat walks on the sidewalk"] embedding1 = model.encode(sentence1, convert_to_tensor=True) embedding2 = model.encore(sentence2, convert_to_tensor=True) cosine scores= util.pytorch_cos_sim(embedding1, embedding2) for i in range(len(sentence1)) for j in range(len(sentence2)) print("Sentence 1:", sentences1[i]) print("Sentence 2:", sentences2[j]) print("similarity Score:", cosine_scores[i][j].item()) print()