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.env

@@ -0,0 +1,5 @@
+URL="http://backend_inference:8000/predict"
+PORT="8090"
+HOST="localhost"
+QA_MODEL="qa_model"
+QA_TOKENIZER="qa_tokenizer"

docker-compose.yml

@@ -0,0 +1,34 @@
+version: '3.3'
+services:
+  backend:
+    #build: ./backend
+    image: backend:test
+    container_name: backend_inference
+    ports:
+      - 8090:8090
+    networks:
+      - semantic #dopis svoj nazov taskov
+    volumes:
+      - ./.env:/app/.env
+      - ./qa_model:/app/qa_model
+      - ./qa_tokenizer:/app/qa_tokenizer
+
+    restart: always
+
+  frontend:
+    #build: ./frontend
+    image: streamlit:dev
+    container_name: streamlit
+    ports:
+      - 8501:8501
+    depends_on:
+      - backend
+    links:
+      - backend
+    networks:
+      - semantic
+    restart: always
+    volumes:
+      - ./.env:/app/.env
+networks:
+  semantic:

train.py

@@ -37,13 +37,20 @@ Q_LEN = 256   # Question Length
 T_LEN = 32    # Target Length
 BATCH_SIZE = 4  #dávka dát
 print("Model succesfully loaded")
+from datasets import load_dataset
 
-path_train = '/home/omasta/T5_JUPYTER/skquad-221017/train-v1.json' 
+dataset = load_dataset("squad_v2")
-
+print(dataset["train"][0])
+#path_train = '/home/omasta/T5_JUPYTER/skquad-221017/train-v1.json' 
+path_train = "poquad-train.json"
 with open(path_train) as f:
     data = json.load(f)
 
 
+def nahradit_znaky(retezec):
+    novy_retezec = retezec.replace('[', ' ').replace(']', ' ')
+    return novy_retezec
+
 def prepare_data(data):
     articles = []
     for article in data["data"]:
@@ -60,15 +67,28 @@ def prepare_data(data):
                 articles.append(inputs)
 
     return articles
+def prep_data(data):
+    arcs = list()
+    for i in range(len(data)):
+        questions=data[i]["question"]
+        try:
+            answer = nahradit_znaky(', '.join(data[i]["answers"]["text"]))
+        except KeyError:
+            continue
+        context = data[i]["context"]
+        inputs = {"input":context+"<sep>"+questions,"answer":answer}
+        arcs.append(inputs)
+    return arcs
 
-prepared_data = prepare_data(data)
+#print(dataset["train"][0]["answers"]["text"])
+
+prepared_data=prep_data(dataset["train"])
+#prepared_data = prepare_data(data)
 print(prepared_data[0])
 
 #Dataframe
 data = pd.DataFrame(prepared_data)
 
-
-
 class QA_Dataset(Dataset):
     def __init__(self, tokenizer, dataframe, q_len, t_len):
         self.tokenizer = tokenizer
@@ -113,18 +133,13 @@ train_loader = DataLoader(qa_dataset, batch_size=BATCH_SIZE, sampler=train_sampl
 val_loader = DataLoader(qa_dataset, batch_size=BATCH_SIZE, sampler=val_sampler)
 print("Loaders working fine")
 
-
-
-
 ### TRAINING  (46MINS ACCORDING THE V1_DATA)
-
-
 train_loss = 0
 val_loss = 0
 train_batch_count = 0
 val_batch_count = 0
 
-for epoch in range(4):
+for epoch in range(2):
     MODEL.train()
     for batch in tqdm(train_loader, desc="Training batches"):
         input_ids = batch["input_ids"].to(DEVICE)
@@ -171,5 +186,5 @@ for epoch in range(4):
 print("Training done succesfully")
 
 ## SAVE FINE_TUNED MODEL
-MODEL.save_pretrained("qa_model_mT5_small")
+MODEL.save_pretrained("qa_model_mT5_english")
-TOKENIZER.save_pretrained('qa_tokenizer_mT5_small')
+TOKENIZER.save_pretrained('qa_tokenizer_mT5_english')

usecase.py

@@ -11,6 +11,11 @@ import warnings
 warnings.filterwarnings("ignore")
 ##13/03/23 added 
 from rouge import Rouge
+from tqdm import tqdm
+from datasets import load_dataset
+import re
+##CUSTOM ROUGE METRIC - NEW TODO:
+
 
 # Názov modelu
 DEVICE ='cuda:0'
@@ -22,9 +27,9 @@ DEVICE ='cuda:0'
 #tokenizer_dir = "/home/omasta/T5_JUPYTER/qa_tokenizer"
 
 #mT5 SMALL MODEL
-model_name = 'mT5_SMALL'
+model_name = 'qa_model'
-model_dir = '/home/omasta/T5_JUPYTER/qa_model_mT5_small'
+model_dir = '/home/omasta/T5_JUPYTER/qa_model_mT5_polish'
-tokenizer_dir = '/home/omasta/T5_JUPYTER/qa_tokenizer_mT5_small'
+tokenizer_dir = '/home/omasta/T5_JUPYTER/qa_tokenizer_mT5_polish'
 
 #Načítanie modelu z adresára
 MODEL = T5ForConditionalGeneration.from_pretrained(model_dir, from_tf=False, return_dict=True).to(DEVICE)
@@ -35,9 +40,14 @@ Q_LEN = 512
 TOKENIZER.add_tokens('<sep>')
 MODEL.resize_token_embeddings(len(TOKENIZER))
 
+def nahradit_znaky(retezec):
+    novy_retezec = retezec.replace('[', ' ').replace(']', ' ')
+    return novy_retezec
+
+
 def predict_answer(data, ref_answer=None,random=None):
     predictions=[]
-    for i in data:
+    for i in tqdm(data,desc="predicting"):
         inputs = TOKENIZER(i['input'], max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True)
         input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0)
         attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0)
@@ -47,14 +57,14 @@ def predict_answer(data, ref_answer=None,random=None):
         #print(ref_answer)
         if ref_answer:
             # Load the Bleu metric
-            bleu = evaluate.load("google_bleu")
+            #bleu = evaluate.load("google_bleu")
             #print('debug')
             #precision = list(precision_score(ref_answer, predicted_answer))
             #recall = list(recall_score(ref_answer, predicted_answer))
             #f1 = list(f1_score(ref_answer, predicted_answer))
-            score = bleu.compute(predictions=[predicted_answer],
+            #score = bleu.compute(predictions=[predicted_answer],
-                           	 references=[ref_answer])
+            #               	 references=[ref_answer])
-            predictions.append({'prediction':predicted_answer,'ref_answer':ref_answer,'score':score['google_bleu']})
+            predictions.append({'prediction':predicted_answer,'ref_answer':ref_answer})
     return predictions
 
 def prepare_data(data):
@@ -66,19 +76,29 @@ def prepare_data(data):
                 answer = qa["answers"][0]["text"]
                 inputs = {"input": paragraph["context"]+ "<sep>" + question, "answer": answer}
                 articles.append(inputs)
-
     return articles
 
-dev_data_path = '/home/omasta/T5_JUPYTER/skquad-221017/dev-v1.json'
+def prepare_polish_data(data):
-with open(dev_data_path,'r') as f:
+    arcs = list()
-	data=json.load(f)
+    for i in range(len(data)):
-#print('data imported')
+        questions=data[i]["question"]
+        try:
+            answer = nahradit_znaky(', '.join(data[i]["answers"]["text"]))
+        except KeyError:
+            continue
+        context = data[i]["context"]
+        inputs = {"input":context+"<sep>"+questions,"answer":answer}
+        arcs.append(inputs)
+    return arcs
 
-dev_data = prepare_data(data)
+
+#dataset = load_dataset("clarin-pl/poquad")
+dataset = load_dataset("squad_v2")
+dev_data = prepare_polish_data(dataset["validation"])
 
 #print('data prepared')
 print(f'Number of dev samples {len(dev_data)}')
-print(dev_data[0])
+#print(dev_data[0])
 bleu_score = []
 precisions=[]
 f1_scores=[]
@@ -88,10 +108,9 @@ rouge_2 = []
 #X = 150
 evaluate = predict_answer(dev_data)
 rouge = Rouge()
-for item in evaluate:
+for item in tqdm(evaluate,desc="evaluating"):
-    bleu_score.append(item['score'])
     try:
-        #scores = rouge.get_scores(item['prediction'], item['ref_answer'], avg=True)
+        scores = rouge.get_scores(item['prediction'], item['ref_answer'])
         precision=precision_score(list(item['ref_answer']), list(item['prediction']),average='macro')
         recall=recall_score(list(item['ref_answer']), list(item['prediction']),average='macro')
         f1=f1_score(list(item['ref_answer']), list(item['prediction']),average='macro')
@@ -119,21 +138,22 @@ def rouge_eval(dict_x):
 print(f'VYHODNOTENIE  VYSLEDKOV : ------------------------')
 #print(evaluate)
 #bleu_score_total = statistics.mean(bleu_score)
-#recall_score_total= statistics.mean(recall_scores)
+recall_score_total= statistics.mean(recall_scores)
-#f1_score_total = statistics.mean(f1_scores)
+f1_score_total = statistics.mean(f1_scores)
-#precision_total = statistics.mean(precisions)
+precision_total = statistics.mean(precisions)
 #print(f'Bleu_score of model {model_name} : ',bleu_score_total)
-#print(f'Recall of model {model_name}: ',recall_score_total)
+print(f'Recall of model {model_name}: ',recall_score_total)
-#print(f'F1 of model {model_name} : ', f1_score_total)
+print(f'F1 of model {model_name} : ', f1_score_total)
-#print(f'Precision of model {model_name}: :',precision_total)
+print(f'Precision of model {model_name}: :',precision_total)
-#print(rouge_eval(evaluate))
+print(model_dir)
+print(rouge_eval(evaluate))
 print(f'{model_name} results')
 rouge_scores = rouge_eval(evaluate)
 rouge_values = [score[0]['rouge-1']['f'] for score in rouge_scores]
 mean_rouge_score = statistics.mean(rouge_values)
-print(f'Rouge:{mean_rouge_score}')
+print(f'Rouge mean score:{mean_rouge_score}')
 
 rouge2_values = [score[0]['rouge-2']['f'] for score in rouge_scores]
 mean_rouge_score =statistics.mean(rouge2_values)
-print(f'Rouge-2:{mean_rouge_score}')
+print(f'Rouge-2 mean score:{mean_rouge_score}')