Initial updates

augment_data/generate_questions.py

@@ -8,13 +8,15 @@
 from typing import List
 import argparse
 import json
+import re
 from nltk.tokenize import sent_tokenize, word_tokenize
 
 
 SYSTEM_PROMPT = 'You are a helpful AI assistant with expertise on EU politics.'
-INSTRUCTION = 'This is a statement by an MEP of the {} political group in the European Parliament: "{}".\n\n What do you think was the question asked? The question should start as "What is your opinion on..." and should not be longer than 16 words.'
+INSTRUCTION = 'This is a public statement by an MEP of the {} political group in the European Parliament on {} for the debate titled "{}":\n\n"{}"\n\nWhat do you think was the question asked? The question should start as "What is your opinion on..." and should not be longer than 16 words.'
 ASSISTANT_START = 'The question asked was likely: "What is your opinion on '
 
+
 def truncate_text(text, max_length):
     ''' Truncate text to the maximum length '''
     sentences = sent_tokenize(text)
@@ -29,15 +31,29 @@ def truncate_text(text, max_length):
             break
     return truncated_text
 
+
+def date_iso_in_text(text):
+    ''' Extarct date in ISO formatand generate full text date '''
+    date =text.split('-')
+    year = date[0]
+    month = date[1]
+    day = date[2]
+    # Numerical month to text month
+    month = 'January' if month == '01' else 'February' if month == '02' else 'March' if month == '03' else 'April' if month == '04' else 'May' if month == '05' else 'June' if month == '06' else 'July' if month == '07' else 'August' if month == '08' else 'September' if month == '09' else 'October' if month == '10' else 'November' if month == '11' else 'December'
+    # Generate full text date
+    full_text_date = f'{day}th of {month} {year}'
+    return full_text_date
+
+
 def main():
     ''' set default hyperparams in default_hyperparams.py '''
     parser = argparse.ArgumentParser()
 
     # Required arguments
     parser.add_argument('--model_name', default='meta-llama/Meta-Llama-3.1-8B-Instruct', help='Model name in HF Hub')
     parser.add_argument('--max_length', default=64, type=int, help='Maximum length of the generated text')
-    parser.add_argument('--parties', default=['PPE', 'S&D', 'GUE/NGL'], type=List, help='List of party names to consider when filtering')
-    parser.add_argument('--debug', default=True, type=bool, help='Whether to use debug mode')
+    parser.add_argument('--parties', default=['S&D'], type=List, help='List of party names to consider when filtering')
+    parser.add_argument('--debug', default=False, type=bool, help='Whether to use debug mode')
     config = parser.parse_args()
 
     # Load eu-elections dataset
@@ -46,11 +62,14 @@ def main():
     if config.debug:
         print('Debugging mode activated')
         config.model_name = 'gpt2'
+        tokenizer_name = 'meta-llama/Meta-Llama-3.1-8B-Instruct'
         config.quant = False
         config.max_length = 8
+    else:
+        tokenizer_name = config.model
 
     # Load tokenizer and model
-    tokenizer = AutoTokenizer.from_pretrained(config.model_name, token=True)
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, token=True)
 
     # Compute free memory for each GPU
     if torch.cuda.is_available():
@@ -91,22 +110,26 @@ def main():
         for example in tqdm.tqdm(dataset):
             if example['speaker_party'] not in config.parties:
                 continue
+            text = example['text'] if example['translated_text'] is None else example['translated_text']
             try:
                 # Truncate the text to the maximum length
-                if len(example['text'].split(' ')) < 100:
+                if len(text.split(' ')) < 100:
                     continue
-                elif len(example['text'].split(' ')) > 256:
-                    truncated_text = truncate_text(example['text'], 256)
+                elif len(text.split(' ')) > 256:
+                    truncated_text = truncate_text(text, 256)
                 else:
-                    truncated_text = example['text']
+                    truncated_text = text
 
                 # Print the instruction
+                example['debate_title'] = re.split('(\(debate\)|Video of)', example['debate_title'])[0].strip()
+                example['debate_title'] = re.split('\(', example['debate_title'], maxsplit=1)[0].strip()
+                example['full_date'] = date_iso_in_text(example['date'])
                 annotation_request = tokenizer.apply_chat_template(
                     conversation=[{"role": "system", "content": SYSTEM_PROMPT},
-                                  {"role": "user", "content": INSTRUCTION.format(example['speaker_party'], truncated_text.strip())}],
+                                  {"role": "user", "content": INSTRUCTION.format(example['speaker_party'], example['full_date'], example['debate_title'], truncated_text.strip())}],
                     tokenize=False, add_generation_prompt=False)
                 annotation_request += ASSISTANT_START
-                print('INSTRUCTION:\n', annotation_request.split('user<|end_header_id|>\n\n ')[1].split('<|eot_id|><|start_header_id|>assistant<|end_header_id|>')[0].strip())
+                print('INSTRUCTION:\n', annotation_request.split('user<|end_header_id|>\n\n')[1].split('<|eot_id|><|start_header_id|>assistant<|end_header_id|>')[0].strip())
                 # Get the response from the chatbot
                 responses = pipeline(
                     annotation_request,

finetune_llms/finetune_llms.py

@@ -54,7 +54,7 @@ def main():
     parser.add_argument('--seed', default=42, type=int)
     parser.add_argument('--output_extension', default='sd-2014', help='Output extension for output directory')
     parser.add_argument('--pseudo_qa', default=True, type=bool, help='Whether to turn the text into a pseudo question')
-    parser.add_argument('--debug', default=True, type=bool, help='Whether to infer summaries')
+    parser.add_argument('--debug', default=False, type=bool, help='Whether to use debug mode')
     param_config = parser.parse_args()
 
     # Setup logging
@@ -73,8 +73,11 @@ def main():
     if param_config.debug:
         print('Debugging mode activated')
         param_config.model_name = 'gpt2'
+        tokenizer_name = 'meta-llama/Meta-Llama-3.1-8B-Instruct'
         param_config.quant = False
         param_config.max_length = 8
+    else:
+        tokenizer_name = param_config.model
 
     # Fix parties' list
     param_config.party_names = param_config.party_names.split(',') if param_config.party_names is not None else None
@@ -112,7 +115,7 @@ def main():
     model.config.use_cache = False
     model.config.pretraining_tp = 1
 
-    tokenizer = AutoTokenizer.from_pretrained(param_config.model_name)
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
     tokenizer.pad_token = tokenizer.eos_token
     tokenizer.padding_side = "right"
 

run_scripts/finetune_llms.sh

@@ -17,5 +17,4 @@ MODEL_PATH='meta-llama/Meta-Llama-3.1-8B-Instruct'
 export PYTHONPATH=.
 
 python ./finetune_llms/finetune_llms.py \
-  --model_name ${MODEL_PATH} \
-  --debug false
+  --model_name ${MODEL_PATH}

run_scripts/generate_questions.sh

@@ -18,5 +18,4 @@ export PYTHONPATH=.
 
 python ./augment_data/generate_questions.py \
   --model_name ${MODEL_PATH} \
-  --max_length 64 \
-  --debug false
+  --max_length 64