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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Vietnamese[[:space:]]truthful[[:space:]]QA[[:space:]]results.xlsx filter=lfs diff=lfs merge=lfs -text

Vietnamese truthful QA results.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dfe1ae3ab2868fcb79e8670167bd820feb3143be59910db29816e02d3f2601c0
+size 294085

eval_truthful_vi.py

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+import pandas as pd
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import jsonlines
+import sys
+from tqdm.auto import tqdm
+# --- Configuration ---
+MODEL_NAME = sys.argv[1] 
+INPUT_FILENAME = "./Vietnamese truthful QA results.xlsx"
+OUTPUT_FILENAME = sys.argv[2] 
+MAX_NEW_TOKENS = 512 # The maximum number of new tokens to generate for each answer.
+
+writer = jsonlines.open(OUTPUT_FILENAME, "w")
+# 1. Load data from an XLSX file
+try:
+    df = pd.read_excel(INPUT_FILENAME)
+except FileNotFoundError:
+    print(f"Error: The file '{INPUT_FILENAME}' was not found.")
+    print("Please make sure your XLSX file is in the same directory as the script.")
+    exit()
+except Exception as e:
+    print(f"An error occurred while reading the Excel file: {e}")
+    exit()
+
+# 2. Select Relevant Columns and validate
+if "Question" not in df.columns or "Ground truth" not in df.columns:
+    print("Error: Required columns 'Question' and/or 'Ground truth' not found.")
+    print(f"Available columns are: {list(df.columns)}")
+    exit()
+
+df_processed = df[["Question", "Ground truth"]].copy()
+
+# 3. Load Model and Tokenizer
+print(f"Loading model '{MODEL_NAME}' and tokenizer...")
+# Set up device (use GPU if available, otherwise CPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# Load the tokenizer and model from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16, attn_implementation='flash_attention_2')
+model.to(device) # Move the model to the selected device
+
+# Set pad token if it's not set (GPT-2 doesn't have a default pad token)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+    model.config.pad_token_id = model.config.eos_token_id
+
+print("Model and tokenizer loaded successfully.")
+
+# 4. Generate Answers using the Model
+answers = []
+out_dict = []
+total_questions = len(df_processed)
+print(f"Generating answers for {total_questions} questions...")
+
+for i, question in tqdm(enumerate(df_processed["Question"])):
+    # Encode the question text into token IDs
+    # input_ids = tokenizer.encode(question, return_tensors='pt').to(device)
+    messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": question}
+    ]
+    input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    input_ids = tokenizer([input], return_tensors='pt').to(model.device)
+    # Generate text using the model
+    # do_sample=False makes the output deterministic (no randomness)
+    output_sequences = model.generate(
+        **input_ids, 
+        max_new_tokens=MAX_NEW_TOKENS,
+        do_sample=False,
+        pad_token_id=tokenizer.pad_token_id
+    )
+
+    # Decode the generated token IDs back to a string
+    # The output includes the original prompt, so we need to remove it.
+    full_text = tokenizer.decode(output_sequences[0][input_ids['input_ids'].shape[1]:], skip_special_tokens=True)
+    answer = full_text.strip()
+    gold = df['Ground truth'][i] 
+    answers.append(answer)
+    print(f"Processed question {i + 1}/{total_questions}\nAnswer: {answer}\nGold: {gold}")
+    writer.write({
+        "question": question,
+        "answer": answer,
+        "gold": gold
+    })
+