Upload distillation.py

distillation.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jun 17 16:20:22 2023
+
+@author: fujidai
+"""
+
+
+from sentence_transformers import SentenceTransformer, LoggingHandler, models, evaluation, losses
+import torch
+from torch.utils.data import DataLoader
+from sentence_transformers.datasets import ParallelSentencesDataset
+from datetime import datetime
+
+import os
+import logging
+import sentence_transformers.util
+import csv
+import gzip
+from tqdm.autonotebook import tqdm
+import numpy as np
+import zipfile
+import io
+
+logging.basicConfig(format='%(asctime)s - %(message)s',
+                    datefmt='%Y-%m-%d %H:%M:%S',
+                    level=logging.INFO,
+                    handlers=[LoggingHandler()])
+logger = logging.getLogger(__name__)
+
+
+teacher_model_name = 'teacher_finetune.py　で作成したモデル'   #Our monolingual teacher model, we want to convert to multiple languages
+#teacher_model_name = '/Users/fujidai/TED2020_data/tisikizyouryu/bert-large-nli-mean-tokens'   #Our monolingual teacher model, we want to convert to multiple languages
+
+student_model_name = '完成2-MarginMSELoss-finetuning-6-30'       #Multilingual base model we use to imitate the teacher model
+#student_model_name = '/Users/fujidai/sinTED/完成2-MarginMSELoss-finetuning-6-30'
+
+
+max_seq_length = 128                #Student model max. lengths for inputs (number of word pieces)
+train_batch_size = 64               #Batch size for training
+inference_batch_size = 64           #Batch size at inference
+max_sentences_per_language = 500000 #Maximum number of  parallel sentences for training
+train_max_sentence_length = 250     #Maximum length (characters) for parallel training sentences
+
+num_epochs = 100                       #Train for x epochs
+num_warmup_steps = 10000             #Warumup steps
+
+num_evaluation_steps = 1000          #Evaluate performance after every xxxx steps
+dev_sentences = 1000                 #Number of parallel sentences to be used for development
+
+
+######## Start the extension of the teacher model to multiple languages ########
+logger.info("Load teacher model")
+teacher_model = SentenceTransformer(teacher_model_name,device='mps')
+
+
+logger.info("Create student model from scratch")
+
+word_embedding_model = models.Transformer(student_model_name, max_seq_length=max_seq_length)
+# Apply mean pooling to get one fixed sized sentence vector
+pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())#denseで次元数を７６８にする次元数をいじる
+student_model = SentenceTransformer(modules=[word_embedding_model, pooling_model],device='mps')
+
+print(teacher_model)
+print(student_model)
+
+
+from sentence_transformers.datasets import ParallelSentencesDataset
+
+train_data = ParallelSentencesDataset(student_model=student_model, teacher_model=teacher_model)
+train_data.load_data('output-100000-karanasi.txt')#日本語英語をタブで繋げたやつ
+#train_data.load_data('/Users/fujidai/TED2020_data/wmt21/output-100.txt')#日本語英語をタブで繋げたやつ
+
+#train_data.load_data('/Users/fujidai/TED2020_data/data/tuikazumi/en-ja/TED2020.en-ja.en')
+train_dataloader = DataLoader(train_data, shuffle=True, batch_size=train_batch_size)
+train_loss = losses.MSELoss(model=student_model)
+
+print(train_data)
+
+
+#50000_all-MiniLM-L6-v2__paraphrase-distilroberta-base-v2_epoch-1
+
+# Train the model
+print('az')
+student_model.fit(train_objectives=[(train_dataloader, train_loss)],
+          epochs=num_epochs,
+          #device=device,
+          warmup_steps=num_warmup_steps,
+          evaluation_steps=num_evaluation_steps,
+          #output_path='best_paraphrase-mpnet-base-v2__xlm-roberta-base_epoch-3',
+          #save_best_model=True,
+          optimizer_params= {'lr': 2e-5, 'eps': 1e-6},
+          checkpoint_path='checkpoint_savename',
+          checkpoint_save_steps=2344
+          )
+
+student_model.save('savename')
+
+
+
+
+
+
+
+
+
+
+
+#