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""" |
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Created on Fri Jun 30 08:47:31 2023 |
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@author: fujidai |
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""" |
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import torch |
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from sentence_transformers import SentenceTransformer, InputExample, losses,models |
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from sentence_transformers import SentenceTransformer, SentencesDataset, LoggingHandler, losses |
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from sentence_transformers.readers import InputExample |
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from torch.utils.data import DataLoader |
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from transformers import AutoTokenizer |
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from sentence_transformers.SentenceTransformer import SentenceTransformer |
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import torch |
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import torch.nn.functional as F |
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import numpy as np |
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from sentence_transformers import SentenceTransformer, util |
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word_embedding_model = models.Transformer('/paraphrase-multilingual-mpnet-base-v2', max_seq_length=512) |
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pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension()) |
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model = SentenceTransformer(modules=[word_embedding_model, pooling_model],device='mps') |
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print(model) |
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with open('/cos-sim_pseudo-pseudo.txt', 'r') as f: |
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raberu = f.read() |
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raberu_lines = raberu.splitlines() |
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data = [] |
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for i in range(len(raberu_lines)): |
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data.append(float(raberu_lines[i])) |
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with open('/cos-sim_pseudo.txt', 'r') as f: |
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raberu2 = f.read() |
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raberu2_lines = raberu2.splitlines() |
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data2 = [] |
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for i in range(len(raberu2_lines)): |
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data2.append(float(raberu2_lines[i])) |
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with open('/en-origin.txt', 'r') as f: |
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left = f.read() |
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left_lines = left.splitlines() |
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with open('/en-pseudo-pseudo.txt', 'r') as f: |
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senter = f.read() |
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senter_lines = senter.splitlines() |
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with open('/en-pseudo.txt', 'r') as f: |
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right = f.read() |
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right_lines = right.splitlines() |
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train_examples = [] |
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for i in range(len(left_lines)): |
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pair=[] |
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pair.append(left_lines[i]) |
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pair.append(senter_lines[i]) |
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pair.append(right_lines[i]) |
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absolutely=abs(data[i]-data2[i]) |
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example = InputExample(texts=pair, label=absolutely) |
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train_examples.append(example) |
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print(len(train_examples)) |
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device = torch.device('mps') |
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import torch.nn.functional as F |
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train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=8) |
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train_loss = losses.MarginMSELoss(model=model,similarity_fct=F.cosine_similarity) |
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model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=100, warmup_steps=100,show_progress_bar=True, |
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checkpoint_path='checkpoint_savename',checkpoint_save_steps=9370, |
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save_best_model=True) |
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model.save("save_name") |
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''' |
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''' |
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