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| import torch | |
| import pickle | |
| ''' | |
| a = [1,2,3] | |
| b = [4,5,6] | |
| at = torch.tensor([a,a]) | |
| bt = torch.tensor([b,b]) | |
| with open('serialize_test.pkl', "ab") as f: | |
| pickle.dump(at,f) | |
| pickle.dump(bt,f) | |
| with open('serialize_test.pkl', "rb") as f: | |
| print(pickle.load(f)) | |
| print(pickle.load(f)) | |
| ''' | |
| def loadFromDiskRaw(batch_number, filename='embeddings.pkl'): | |
| count = 0 | |
| with open(filename, "rb") as f: | |
| while count < batch_number: | |
| stored_data = pickle.load(f) | |
| print(stored_data.size()) | |
| print(stored_data[0][:15]) | |
| count += 1 | |
| return stored_data | |
| output_embeddings_file = 'data/preprocessed/DBpedia_shortened_abstracts_hu_embeddings.pkl' | |
| loadFromDiskRaw(3, output_embeddings_file) | |