try to train old orgcatorg model
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# -*- coding: utf-8 -*-
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" Original file is located at https://colab.research.google.com/drive/1Yb_fU_WBIs3a_L5G3_A_nxChrnR4Nzb1"
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learning_rate = 0.65e-4 # 0.65e-4 - 0.4e-4
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mini_batch_size = 8
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max_epochs = 200
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from funcs import save_to_file_by_address
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import json
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import os
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from pathlib import Path
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from flair.data import Corpus
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from flair.datasets import ColumnCorpus
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from flair.embeddings import TransformerWordEmbeddings
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from flair.models import SequenceTagger
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from flair.trainers import ModelTrainer
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from flair.models import SequenceTagger
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from flair.embeddings import TransformerDocumentEmbeddings
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# from funcs import remove_signs
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# from inference import main
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#model = os.getcwd() + "\\data\\final-model.pt"
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#model = os.getcwd() + "/data/HooshvareLab--distilbert-fa-zwnj-base-ner" # مدل اولیه که تست شد و تا حدود 70 درصد در آخرین آموزش خوب جواب می داد
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#model = os.getcwd() + "/data/distilbert-base-multilingual-cased-tavasi"
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# model = "HooshvareLab/bert-fa-base-uncased-ner-peyma"
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model = "PooryaPiroozfar/Flair-Persian-NER" # 111111111111111
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#model = "Helsinki-NLP/opus-mt-tc-big-fa-itc" # خطا میدهد، برای نر آموزش ندیده
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#model = "zedfum/arman-longformer-8k-finetuned-ensani" # خطا میدهد، برای نر آموزش ندیده
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#model = "AliGhiasvand86/gisha_qa" # خطا میدهد، برای نر آموزش ندیده
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## ---------------------------------------------------------
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## --- آخرین کار مورد استفاده در سامانه قانون یار از این آموزش دیده است
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#model = "orgcatorg/xlm-v-base-ner" # بهترین توکنایزر فارسی ***********************
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## ---------------------------------------------------------
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# from transformers import AutoModel
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# print('1sdfsdf')
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# model = AutoModel.from_pretrained("/home/gpu/HFHOME/hub/models--orgcatorg--xlm-v-base-ner")
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#model = "pourmand1376/NER_Farsi" #
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#model = "HooshvareLab/bert-base-parsbert-ner-uncased" # **** خوب جواب داد
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#model = "SeyedAli/Persian-Text-NER-Bert-V1" # ***** خیلی خوب جواب داد
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#model = "HooshvareLab/bert-base-parsbert-peymaner-uncased" # جالب نبود!
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#model = "HooshvareLab/bert-base-parsbert-armanner-uncased" # جالب نبود!
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#model = "HooshvareLab/bert-base-parsbert-ner-uncased" # جالب نبود!
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print(model)
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print('#'*50)
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print('#'*50)
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#!pip install 'flair==0.10'
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# define columns
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columns = {0 : 'text', 1 : 'ner'}
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# directory where the data resides
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data_folder = './data/'
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# initializing the corpuscorpus = ColumnCorpus(data_folder, columns, train_file='peyma_train.txt', sequence_length=512)
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#اسم دیتاست اینجا تنظیم شود
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corpus = ColumnCorpus(data_folder, columns,
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#train_file = 'peyma_train.txt')
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train_file = 'DATASET.txt') # qavanin 36K tokens
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#train_file = 'law_dataset.txt',
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#test_file = 'test_gold.txt',
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#dev_file = 'dev split 2.txt'
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#max_sentence_length=500
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#)
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# tag to predict
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tag_type = 'ner'
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# make tag dictionary from the corpus
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tag_dictionary = corpus.make_label_dictionary(label_type=tag_type)
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#xlm-roberta-large
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# embeddings = TransformerWordEmbeddings(model='HooshvareLab/distilbert-fa-zwnj-base-ner',
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embeddings = TransformerWordEmbeddings(model= model,
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layers="-1",
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subtoken_pooling="first",
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# pooling='mean',
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fine_tune=True,
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use_context=True,
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from_tf=True,
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allow_long_sentences=True
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# model_max_length=512,
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)
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print('model read successfully !')
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print('#'*50)
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print('#'*50)
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try:
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tagger = SequenceTagger(hidden_size=256,
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embeddings=embeddings,
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tag_dictionary= tag_dictionary,
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tag_type='ner',
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use_crf=False,
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use_rnn=False,
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reproject_embeddings=False
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)
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except Exception as e:
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print(str(e.args[0]))
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exit()
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from flair.trainers import ModelTrainer
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try:
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trainer = ModelTrainer(tagger, corpus)
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#resources/taggers/sota-ner-flert
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# trainer.fine_tune('./taggers',
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# learning_rate=2.0e-6,
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# mini_batch_size=16,
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# # mini_batch_chunk_size=1, # remove this parameter to speed up computation if you have a big GPU
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# max_epochs=20
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# )
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except Exception as e:
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print(str(e.args[0]))
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exit()
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try:
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result = trainer.fine_tune('./taggers',
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learning_rate= learning_rate,
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mini_batch_size= mini_batch_size,
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max_epochs= max_epochs
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)
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except Exception as e:
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print(str(e.args[0]))
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exit()
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try:
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# Save the model's state dictionary (configuration + weights)
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#model_state_dict_path = Path('./trained/best-model.pt') # Assuming best model is saved here
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#tagger.save(model_state_dict_path)
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# Optionally, save additional hyperparameters to a separate file (e.g., training.json)
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hyperparameters = {
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"learning_rate": learning_rate,
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"mini_batch_size": mini_batch_size,
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"max_epochs": max_epochs,
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}
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with open('./trained/training.json', 'w') as f:
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json.dump(hyperparameters, f, indent=4)
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except Exception as e:
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exit()
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try:
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from train_log_plotter import plot_diagram
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plot_diagram()
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except:
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print('log diagram failed due to error!')
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train_result = f'''************************************************\n
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##### TRAIN RESULT #####
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F1 Score: {result}
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n'''
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# اجرای اینفرنس جهت ارزیابی مدل
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# time = datetime.datetime.now()
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# tagger.save('./trained/trained-model ' + str(time) + '.pt')
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print('#'*70)
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print( ' ********** fine-tune operation finished ********** ')
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import datetime
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operation_time = datetime.datetime.now()
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print(f' ********** {operation_time} ********** ')
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print('#'*70)
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# ###################################################
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# ارزیابی مدل آموزش دیده
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try:
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from evaluate_model import do_evaluate
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print(' Try to evaluating the trained model! ')
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evaluate_result = do_evaluate()
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print(' Evaluating finished! ')
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except Exception as e:
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print('do_evaluate function failed')
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evaluate_result = f"do_evaluate function failed!\nerror massage:\n{str(e.args[0])}"
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final_result = f"""Model Name: {model}
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Fine-Tune Parameters: {hyperparameters}
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{train_result}
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{evaluate_result}\n
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Fine_Tune time: {operation_time}
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------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------\n
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"""
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save_to_file_by_address('./data/train_log.txt', final_result)
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print(' Saving results finished! ')
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# ###################################################
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# تست مدل بر یک مقدار مشخص شده
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print(' Try to test trained model! ')
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from inference import inference_main
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inference_main(model,'')
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print(' Testing model finished! ')
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67
temp.py
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temp.py
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from flair.data import Corpus
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from flair.datasets import ColumnCorpus
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from flair.embeddings import FlairEmbeddings, WordEmbeddings, StackedEmbeddings
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from flair.models import SequenceTagger
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from flair.trainers import ModelTrainer
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from flair.data import Dictionary
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from flair.models import SequenceTagger
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# Path to your new dataset
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data_folder = './data' # Folder containing your dataset
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train_file = 'DATASET140402_no_aref.txt' # qavanin 36K tokens
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test_file = 'test_ds_new.txt' # test 110 sections - 6.7K
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# Column format for your dataset (adjust as necessary)
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# For example: 0 = text, 1 = NER tags
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columns = {0: 'text', 1: 'ner'}
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# Load the corpus
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corpus = ColumnCorpus(
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data_folder=data_folder,
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column_format=columns,
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train_file=train_file,
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test_file=test_file
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)
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print(corpus)
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# Define the tag dictionary (new NER tags)
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tag_type = 'ner'
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tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
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# Load the existing model (mdl.pt)
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tagger = SequenceTagger.load("/home/gpu/tnlp/jokar/Models/catorg/14030906_before_aitools_ds_finetune/final-model.pt")
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# Define embeddings (you can modify this as needed)
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embedding_types = [
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WordEmbeddings('glove'), # Pre-trained GloVe embeddings
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FlairEmbeddings('news-forward'), # Forward Flair embeddings
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FlairEmbeddings('news-backward') # Backward Flair embeddings
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]
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embeddings = StackedEmbeddings(embeddings=embedding_types)
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# Create a new tagger using the updated tag dictionary
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new_tagger = SequenceTagger(
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hidden_size=256, # Size of the hidden layer, adjust as needed
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embeddings=embeddings,
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tag_dictionary=tag_dictionary,
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tag_type=tag_type,
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use_crf=True,
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)
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# Transfer the weights from the old model to the new tagger
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new_tagger.load_state_dict(tagger.state_dict(), strict=False)
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# Train the model with the new dataset
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trainer = ModelTrainer(new_tagger, corpus)
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# Start training
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trainer.train('./output', # Output folder for the model
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learning_rate=0.1,
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mini_batch_size=32,
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max_epochs=10) # Adjust parameters as needed
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# Save the fine-tuned model
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new_tagger.save('./trained/fine_tuned_mdl.pt')
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14
train.py
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train.py
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# -*- coding: utf-8 -*-
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" Original file is located at https://colab.research.google.com/drive/1Yb_fU_WBIs3a_L5G3_A_nxChrnR4Nzb1"
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learning_rate = 0.65e-4 # 0.65e-4 - 0.4e-4
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mini_batch_size = 8
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max_epochs = 200
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max_epochs = 100
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from funcs import save_to_file_by_address
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import json
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@ -23,7 +20,9 @@ from flair.embeddings import TransformerDocumentEmbeddings
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#model = os.getcwd() + "/data/HooshvareLab--distilbert-fa-zwnj-base-ner" # مدل اولیه که تست شد و تا حدود 70 درصد در آخرین آموزش خوب جواب می داد
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#model = os.getcwd() + "/data/distilbert-base-multilingual-cased-tavasi"
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# model = "HooshvareLab/bert-fa-base-uncased-ner-peyma"
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model = "PooryaPiroozfar/Flair-Persian-NER" # 111111111111111
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# model = "PooryaPiroozfar/Flair-Persian-NER" # 111111111111111
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model_ad = "/home/gpu/tnlp/jokar/Models/catorg/14030906_before_aitools_ds_finetune/final-model.pt"
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model = SequenceTagger.load(model_ad)
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#model = "Helsinki-NLP/opus-mt-tc-big-fa-itc" # خطا میدهد، برای نر آموزش ندیده
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#model = "zedfum/arman-longformer-8k-finetuned-ensani" # خطا میدهد، برای نر آموزش ندیده
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#model = "AliGhiasvand86/gisha_qa" # خطا میدهد، برای نر آموزش ندیده
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#اسم دیتاست اینجا تنظیم شود
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corpus = ColumnCorpus(data_folder, columns,
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#train_file = 'peyma_train.txt')
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train_file = 'DATASET.txt') # qavanin 36K tokens
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#train_file = 'law_dataset.txt',
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#test_file = 'test_gold.txt',
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train_file = 'DATASET140402_no_aref.txt', # qavanin 36K tokens
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test_file = 'test_ds_new.txt',) # test 110 sections - 6.7K
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#dev_file = 'dev split 2.txt'
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#max_sentence_length=500
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#)
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