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Feature Vector = (guide + metier + electrotechnique + v3 + hot) / 5 This results in a single vector (assuming 100-dimensional space for simplicity):
def generate_feature(phrase): tokens = word_tokenize(phrase) # Assume a pre-trained Word2Vec model model = Word2Vec.load("path/to/model") features = [] for token in tokens: if token in model.wv: features.append(model.wv[token]) if features: feature_vector = np.mean(features, axis=0) return feature_vector else: return np.zeros(100) # Return zeros if no features found
# Assuming necessary NLTK data is downloaded
The CSIR-UGC NET (Council of Scientific and Industrial Research - University Grants Commission National Eligibility Test) Life Sciences is the branch of science that deals with the study of living organisms, their structure, function, growth, origin, evolution, and interaction with the environment.
Feature Vector = (guide + metier + electrotechnique + v3 + hot) / 5 This results in a single vector (assuming 100-dimensional space for simplicity):
def generate_feature(phrase): tokens = word_tokenize(phrase) # Assume a pre-trained Word2Vec model model = Word2Vec.load("path/to/model") features = [] for token in tokens: if token in model.wv: features.append(model.wv[token]) if features: feature_vector = np.mean(features, axis=0) return feature_vector else: return np.zeros(100) # Return zeros if no features found
# Assuming necessary NLTK data is downloaded
