diff --git a/Flair_NER/train.py b/Flair_NER/train.py
new file mode 100644
index 0000000..721437f
--- /dev/null
+++ b/Flair_NER/train.py
@@ -0,0 +1,194 @@
+# -*- coding: utf-8 -*-
+" Original file is located at https://colab.research.google.com/drive/1Yb_fU_WBIs3a_L5G3_A_nxChrnR4Nzb1"
+
+learning_rate   = 0.65e-4 # 0.65e-4 - 0.4e-4
+mini_batch_size = 8
+max_epochs      = 200
+
+from funcs import save_to_file_by_address
+import json
+import os
+from pathlib import Path
+from flair.data import Corpus
+from flair.datasets import ColumnCorpus
+from flair.embeddings import TransformerWordEmbeddings
+from flair.models import SequenceTagger
+from flair.trainers import ModelTrainer
+from flair.models import SequenceTagger
+from flair.embeddings import TransformerDocumentEmbeddings
+
+# from funcs import remove_signs
+# from inference import main
+#model = os.getcwd() + "\\data\\final-model.pt"
+#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
+#model = os.getcwd() + "/data/distilbert-base-multilingual-cased-tavasi"
+# model = "HooshvareLab/bert-fa-base-uncased-ner-peyma"
+model = "PooryaPiroozfar/Flair-Persian-NER" # 111111111111111
+#model = "Helsinki-NLP/opus-mt-tc-big-fa-itc" # خطا میدهد، برای نر آموزش ندیده
+#model = "zedfum/arman-longformer-8k-finetuned-ensani" # خطا میدهد، برای نر آموزش ندیده
+#model = "AliGhiasvand86/gisha_qa" # خطا میدهد، برای نر آموزش ندیده
+
+## ---------------------------------------------------------
+## --- آخرین کار مورد استفاده در سامانه قانون یار از این آموزش دیده است
+#model = "orgcatorg/xlm-v-base-ner" # بهترین توکنایزر فارسی ***********************
+## ---------------------------------------------------------
+
+
+# from transformers import AutoModel
+# print('1sdfsdf')
+# model = AutoModel.from_pretrained("/home/gpu/HFHOME/hub/models--orgcatorg--xlm-v-base-ner")
+
+#model = "pourmand1376/NER_Farsi" # 
+#model = "HooshvareLab/bert-base-parsbert-ner-uncased"   # ****  خوب جواب داد
+#model = "SeyedAli/Persian-Text-NER-Bert-V1"             # ***** خیلی خوب جواب داد
+#model = "HooshvareLab/bert-base-parsbert-peymaner-uncased" # جالب نبود!
+#model = "HooshvareLab/bert-base-parsbert-armanner-uncased" # جالب نبود!
+#model = "HooshvareLab/bert-base-parsbert-ner-uncased" # جالب نبود!
+print(model)
+print('#'*50)
+print('#'*50)
+
+#!pip install 'flair==0.10'
+
+# define columns
+columns = {0 : 'text', 1 : 'ner'}
+# directory where the data resides
+data_folder = './data/'
+# initializing the corpuscorpus = ColumnCorpus(data_folder, columns, train_file='peyma_train.txt', sequence_length=512)
+#اسم دیتاست اینجا تنظیم شود 
+corpus = ColumnCorpus(data_folder, columns,
+                            #train_file = 'peyma_train.txt')
+                              train_file = 'DATASET.txt') # qavanin 36K tokens
+                              #train_file = 'law_dataset.txt',
+                              #test_file = 'test_gold.txt',
+                              #dev_file = 'dev split 2.txt'
+                              #max_sentence_length=500
+                              #)
+
+# tag to predict
+tag_type = 'ner'
+# make tag dictionary from the corpus
+tag_dictionary = corpus.make_label_dictionary(label_type=tag_type)
+
+#xlm-roberta-large
+# embeddings = TransformerWordEmbeddings(model='HooshvareLab/distilbert-fa-zwnj-base-ner',
+embeddings = TransformerWordEmbeddings(model= model,
+                                       layers="-1",
+                                       subtoken_pooling="first",
+                                    #    pooling='mean',
+                                       fine_tune=True,
+                                       use_context=True,
+                                       from_tf=True,
+                                       allow_long_sentences=True
+                                    #    model_max_length=512,
+                                       )
+
+print('model read successfully !')
+print('#'*50)
+print('#'*50)
+try:
+    tagger = SequenceTagger(hidden_size=256,
+                            embeddings=embeddings,
+                            tag_dictionary= tag_dictionary,
+                            tag_type='ner',
+                            use_crf=False,
+                            use_rnn=False,
+                            reproject_embeddings=False
+                            )
+except Exception as e:
+    print(str(e.args[0]))
+    exit()
+    
+
+from flair.trainers import ModelTrainer
+try:
+
+    trainer = ModelTrainer(tagger, corpus)
+    #resources/taggers/sota-ner-flert
+    # trainer.fine_tune('./taggers',
+    #                   learning_rate=2.0e-6,
+    #                   mini_batch_size=16,
+    #                 #   mini_batch_chunk_size=1,  # remove this parameter to speed up computation if you have a big GPU
+    #                   max_epochs=20
+    #                   )
+except Exception as e:
+    print(str(e.args[0]))
+    exit()
+    
+try:
+    result = trainer.fine_tune('./taggers',
+                               learning_rate= learning_rate,
+                               mini_batch_size= mini_batch_size,
+                               max_epochs= max_epochs
+                               )
+except Exception as e:
+    print(str(e.args[0]))
+    exit()
+try:
+    # Save the model's state dictionary (configuration + weights)
+    #model_state_dict_path = Path('./trained/best-model.pt')  # Assuming best model is saved here
+    #tagger.save(model_state_dict_path)
+
+    # Optionally, save additional hyperparameters to a separate file (e.g., training.json)
+    hyperparameters = {
+        "learning_rate": learning_rate,
+        "mini_batch_size": mini_batch_size,
+        "max_epochs": max_epochs,
+    }
+
+    with open('./trained/training.json', 'w') as f:
+        json.dump(hyperparameters, f, indent=4)
+except Exception as e:
+
+    exit()
+
+try:
+    from train_log_plotter import plot_diagram
+    plot_diagram()
+except:
+    print('log diagram failed due to error!')
+
+train_result = f'''************************************************\n
+##### TRAIN RESULT #####
+F1 Score: {result}
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n'''
+
+# اجرای اینفرنس جهت ارزیابی مدل
+# time = datetime.datetime.now()
+# tagger.save('./trained/trained-model ' + str(time) + '.pt')
+print('#'*70)
+print( '           ********** fine-tune operation finished **********           ')
+import datetime
+operation_time = datetime.datetime.now()
+print(f'           **********  {operation_time}  **********           ')
+print('#'*70)
+
+# ###################################################
+# ارزیابی مدل آموزش دیده
+try:
+    from evaluate_model import do_evaluate
+    print(' Try to evaluating the trained model! ')
+    evaluate_result = do_evaluate()
+    print(' Evaluating finished! ')
+except Exception as e:
+    print('do_evaluate function failed')
+    evaluate_result = f"do_evaluate function failed!\nerror massage:\n{str(e.args[0])}"
+    
+final_result = f"""Model Name: {model}
+Fine-Tune Parameters: {hyperparameters}
+{train_result}
+{evaluate_result}\n
+Fine_Tune time: {operation_time}
+------------------------------------------------------------------------------------
+------------------------------------------------------------------------------------\n
+"""
+save_to_file_by_address('./data/train_log.txt', final_result)
+print(' Saving results finished! ')
+# ###################################################
+# تست مدل بر یک مقدار مشخص شده
+print(' Try to test trained model! ')
+from inference import inference_main
+inference_main(model,'')
+print(' Testing model finished! ')
+
+