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evaluation/README.md

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+
+### Commands for running inference
+
+```console
+python run_generation_vllm.py --model-id nvidia/ChatQA-1.5-8B --eval-dataset DATASET_NAME --data-folder PATH_TO_YOUR_DATA --output-folder PATH_TO_OUTPUT_FOLDER
+```
+
+### Commands for calculating scores for generated outputs
+```console
+python get_scores.py
+```

evaluation/arguments.py

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+
+import argparse
+import os
+
+def get_args():
+    parser = argparse.ArgumentParser(description="ChatQA-HF")
+
+    ## model
+    parser.add_argument('--model-id', type=str, default='', help='model id')
+
+    ## dataset path
+    parser.add_argument('--data-folder', type=str, default='', help='path to the datafolder of ConvRAG')
+    parser.add_argument('--output-folder', type=str, default='', help='path to the datafolder of ConvRAG')
+    parser.add_argument('--eval-dataset', type=str, default='')
+    parser.add_argument('--doc2dial-path', type=str, default='doc2dial/test.json')
+    parser.add_argument('--convfinqa-path', type=str, default='convfinqa/dev.json')
+    parser.add_argument('--quac-path', type=str, default='quac/test.json')
+    parser.add_argument('--qrecc-path', type=str, default='qrecc/test.json')
+    parser.add_argument('--doqa-cooking-path', type=str, default='doqa/test_cooking.json')
+    parser.add_argument('--doqa-travel-path', type=str, default='doqa/test_travel.json')
+    parser.add_argument('--doqa-movies-path', type=str, default='doqa/test_movies.json')
+    parser.add_argument('--coqa-path', type=str, default='coqa/dev.json')
+    parser.add_argument('--hybridial-path', type=str, default='hybridial/test.json')
+    parser.add_argument('--sqa-path', type=str, default='sqa/test.json')
+    parser.add_argument('--topiocqa-path', type=str, default='topiocqa/dev.json')
+    parser.add_argument('--inscit-path', type=str, default='inscit/dev.json')
+
+    ## others
+    parser.add_argument('--out-seq-len', type=int, default=64)
+    parser.add_argument('--num-ctx', type=int, default=5)
+    parser.add_argument('--max-tokens', type=int, default=64)
+
+    args = parser.parse_args()
+
+    return args

evaluation/dataset.py

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+
+import json
+
+
+def load_data(datapath):
+    print("loading data from %s" % datapath)
+    with open(datapath, "r") as f:
+        data_list = json.load(f)
+
+    return data_list
+
+
+def reformat_question(turn_list, dataset_name):
+
+    ## only take the lastest 7 turns
+    turn_list = turn_list[-7:]
+    assert turn_list[-1]['role'] == 'user'
+
+    long_answer_dataset_list = ["doc2dial", "quac", "qrecc", "inscit", "doqa_movies", "doqa_travel", "doqa_cooking", "hybridial", "convfinqa"]
+    long_and_short_dataset_list = ["topiocqa"]
+    entity_dataset_list = ["sqa"]
+    short_dataset_list = ["coqa"]
+
+    if dataset_name in long_answer_dataset_list:
+        for item in turn_list:
+            if item['role'] == 'user':
+                ## only needs to add it on the first user turn
+                item['content'] = 'Please give a full and complete answer for the question. ' + item['content']
+                break
+    
+    elif dataset_name in long_and_short_dataset_list:
+        turn_list[-1]['content'] = "Answer the following question with a short span, or a full and complete answer. " + turn_list[-1]['content']
+
+    elif dataset_name in entity_dataset_list:
+        turn_list[-1]['content'] = "Answer the following question with one or a list of items. " + turn_list[-1]['content']
+
+    elif dataset_name in short_dataset_list:
+        turn_list[-1]['content'] = "Answer the following question with a short span. The answer needs to be just in a few words. " + turn_list[-1]['content']
+
+    else:
+        raise Exception("please input a correct dataset name!")
+    
+    question = ""
+    for item in turn_list:
+        if item["role"] == "user":
+            question += "User: " + item["content"] + "\n\n"
+        else:
+            assert item["role"] == "assistant"
+            question += "Assistant: " + item["content"] + "\n\n"
+    
+    question += "Assistant:"
+    
+    return question
+
+
+def get_inputs(data_list, dataset_name, tokenizer, num_ctx, max_output_len, max_seq_length=4096):
+
+    system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context."
+
+    prompt_list = []
+    for item in data_list:
+        turn_list = item['messages']
+        question_formatted = reformat_question(turn_list, dataset_name)
+
+        ctx_list = ["title: " + ctx["title"] + ", source: " + ctx["text"] for ctx in item['ctxs'][:num_ctx]]
+        context = "\n\n".join(ctx_list)
+
+        context_tokens = tokenizer.encode(context)
+        question_tokens = tokenizer.encode(question_formatted)
+        system_tokens = tokenizer.encode(system)
+
+        if len(context_tokens) + len(question_tokens) + len(system_tokens) + max_output_len >= max_seq_length:
+            context_tokens = context_tokens[:max_seq_length - max_output_len - len(question_tokens) - len(system_tokens)]
+            context = tokenizer.decode(context_tokens, skip_special_tokens=True)
+
+        model_input = system + "\n\n" + context + "\n\n" + question_formatted
+
+        prompt_list.append(model_input)
+    
+    return prompt_list
+

evaluation/evaluation_utils.py

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+
+
+## a index list of the sample where the correct context is found in the top-5 retrieved contexts
+quac_correct_retrieved_instance_idx_list = [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126, 127, 128, 129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 169, 170, 171, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 216, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 243, 245, 246, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 284, 285, 287, 289, 290, 291, 292, 293, 294, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 341, 342, 344, 345, 346, 347, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 362, 363, 364, 365, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 415, 417, 419, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 440, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 466, 467, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 484, 485, 486, 488, 489, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 509, 510, 511, 512, 514, 515, 518, 519, 520, 521, 522, 523, 524, 527, 528, 529, 530, 531, 532, 533, 534, 535, 538, 539, 540, 541, 542, 543, 544, 547, 548, 549, 550, 551, 552, 554, 555, 557, 558, 560, 561, 562, 563, 564, 565, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 608, 609, 610, 611, 612, 613, 614, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 637, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 652, 653, 655, 656, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 692, 693, 695, 699, 700, 701, 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5369, 5370, 5371, 5372, 5373, 5374, 5375, 5376, 5377, 5378, 5379, 5380, 5381, 5382, 5383, 5384, 5386, 5389, 5390, 5393, 5394, 5395, 5396, 5397, 5399, 5400, 5401, 5402, 5404, 5405, 5408, 5412, 5413, 5414, 5415, 5416, 5417, 5418, 5419, 5421, 5422, 5423, 5424, 5425, 5426, 5427, 5429, 5430, 5431, 5432, 5433, 5434, 5435, 5436, 5439, 5441, 5442, 5443, 5444, 5445, 5446, 5447, 5449, 5450, 5451, 5452, 5453, 5454, 5455, 5456, 5457, 5458, 5459, 5460, 5461, 5462, 5463, 5464, 5465, 5466, 5467, 5470, 5471, 5472, 5473, 5475, 5476, 5477, 5478, 5479, 5480, 5483, 5484, 5485, 5486, 5487, 5488, 5489, 5490, 5491, 5492, 5493, 5494, 5496, 5497, 5499, 5500, 5501, 5502, 5503, 5504, 5505, 5506, 5507, 5508, 5509, 5510, 5511, 5512, 5513, 5514, 5515, 5516, 5517, 5518, 5520, 5521, 5522, 5523, 5524, 5525, 5526, 5527, 5528, 5529, 5530, 5531, 5532, 5533, 5534, 5535, 5536, 5537, 5538, 5539, 5540, 5541, 5542, 5543, 5544, 5546, 5547, 5548, 5549, 5550, 5551, 5552, 5553, 5554, 5555, 5556, 5557, 5558, 5559, 5560, 5561, 5563, 5564, 5565, 5566, 5567, 5568, 5569, 5571, 5573, 5574, 5576, 5578, 5579, 5580, 5581, 5582, 5583, 5584, 5589, 5590, 5591, 5593, 5594, 5595, 5596, 5597, 5598, 5599, 5600, 5601, 5602, 5603, 5604, 5605, 5606, 5607, 5608, 5609, 5611, 5612, 5613, 5614, 5616, 5617, 5618, 5619, 5620, 5622, 5623, 5624, 5625, 5626, 5627, 5628, 5629, 5630, 5631, 5632, 5633, 5634, 5635, 5636, 5637, 5638, 5639, 5640, 5641, 5642, 5643, 5644, 5646, 5647, 5648, 5649, 5650, 5651, 5652, 5653, 5654, 5655, 5656, 5657, 5658, 5659, 5660, 5661, 5662, 5663, 5664, 5665, 5666, 5667, 5668, 5669, 5670, 5671, 5673, 5674, 5675, 5676, 5677, 5678, 5679, 5680, 5681, 5682, 5683, 5684, 5685, 5687, 5688, 5689, 5690, 5691, 5692, 5693, 5700, 5701, 5702, 5703, 5704, 5705, 5706, 5708, 5709, 5710, 5711, 5712, 5713, 5714, 5717, 5718, 5719, 5720, 5721, 5722, 5723, 5724, 5725, 5726, 5727, 5728, 5734, 5736, 5737, 5738, 5739, 5740, 5741, 5742, 5743, 5744, 5745, 5746, 5747, 5748, 5749, 5750, 5751, 5752, 5753, 5754, 5755, 5756, 5757, 5758, 5759, 5760, 5761, 5762, 5763, 5764, 5765, 5766, 5767, 5768, 5769, 5771, 5772, 5776, 5777, 5778, 5779, 5780, 5781, 5782, 5783, 5784, 5785, 5786, 5787, 5788, 5789, 5790, 5791, 5792, 5794, 5795, 5796, 5797, 5798, 5799, 5801, 5802, 5804, 5807, 5808, 5809, 5813, 5814, 5815, 5816, 5819, 5821, 5822, 5823, 5824, 5825, 5826, 5827, 5828, 5829, 5832, 5834, 5835, 5836, 5837, 5839, 5840, 5841, 5842, 5843, 5844, 5845, 5846, 5847, 5848, 5850, 5851, 5852, 5853, 5856, 5859, 5862, 5863, 5864, 5865]
+
+
+unanswerable_keyphrases = ["cannot find", "can't find", "not able to", "unable to", "does not provide", "cannot provide", "cannot answer", "couldnot answer", "can't answer", "couldn't answer", "cannot be found", "cannot be determined", "do not have", "couldn't find", "no information", "does not mention", "doesn't mention", "not explicitly mentioned", "can not find", "could not find", "does not specify", "doesn't provide", "doesn't specify", "there is no", "not mentioned", "don't have", "don't know"]

evaluation/get_scores.py

@@ -0,0 +1,363 @@
+
+from evaluation_utils import quac_correct_retrieved_instance_idx_list
+from evaluation_utils import unanswerable_keyphrases
+import json
+from metrics import F1Metric
+import copy
+import re
+
+
+def compute_f1_score(predicted_answers, groundtruth_answer, exp_name="default"):
+    """Evaluating F1 Score"""
+    print(len(predicted_answers), len(groundtruth_answer))
+    if len(predicted_answers) != len(groundtruth_answer):
+        groundtruth_answer = groundtruth_answer[:len(predicted_answers)]
+
+    guess_list = []
+    for guess in predicted_answers:
+        guess = guess.strip()
+        if "</s>" in guess:
+            guess = guess.replace("</s>", "")
+        guess_list.append(guess)
+
+    answer_list = []
+    for answer in groundtruth_answer:
+        answer_list.append(answer)
+
+    assert len(guess_list) == len(answer_list), \
+        "lengths of guess and answer are different!"
+
+    precision, recall, f1 = F1Metric.compute_all_pairs(guess_list, answer_list)
+    print('Method: %s; Precision: %.4f; recall: %.4f; f1: %.4f' % (\
+        exp_name, precision, recall, f1))
+
+
+def load_groundtruth_file(data_file):
+    
+    with open(data_file, "r") as f:
+        examples = json.load(f)
+
+    data = []
+    for instance in examples:
+        if "answers" in instance:
+            answers = instance["answers"]
+        elif "answer" in instance:
+            if type(instance["answer"]) is str:
+                answers = [instance["answer"]]
+            elif type(instance["answer"]) is list:
+                answers = instance["answer"]
+            else:
+                answers = [str(instance["answer"])]
+        else:
+            raise ValueError("need to have answer or answers")
+        data.append(answers)
+
+    return data
+
+
+def load_prediction(data_file):
+
+    data = []
+    with open(data_file, "r") as f:
+        for line in f.readlines():
+            data.append(line.strip())
+
+    return data
+
+
+def evaluate_f1(ground_truth_file, prediction_file):
+
+    groundtruth_answers = load_groundtruth_file(ground_truth_file)
+    if "inscit" in ground_truth_file:
+        groundtruth_answers_update = []
+        for answers in groundtruth_answers:
+            answers_update = []
+            for ans in answers:
+                ## this answer is additionally added to the answer_list for inscit dataset, needs to remove
+                if ans != "Sorry. I cannot find the answer based on the context.":
+                    answers_update.append(ans)
+            assert len(answers_update) > 0
+            groundtruth_answers_update.append(copy.deepcopy(answers_update))
+        groundtruth_answers = groundtruth_answers_update
+
+    predicted_answers = load_prediction(prediction_file)
+    if "quac" in prediction_file or "doqa" in prediction_file:
+        predicted_answers_new = []
+        for pred in predicted_answers:
+            pred = pred.lower()
+            for keyphrase in unanswerable_keyphrases:
+                if keyphrase in pred:
+                    pred = "Sorry. I cannot find the answer based on the context."
+                    break
+            predicted_answers_new.append(pred)
+        predicted_answers = predicted_answers_new
+
+    compute_f1_score(predicted_answers, groundtruth_answers)
+
+
+def separate_cannot_answer(ground_truth_file, prediction_file):
+    # load ground truth
+    with open(ground_truth_file, "r") as f:
+        groundtruth_answers = json.load(f)
+    # load prediction
+    predicted_answers = load_prediction(prediction_file)
+    print(len(predicted_answers), len(groundtruth_answers))
+    if len(predicted_answers) != len(groundtruth_answers):
+        groundtruth_answers = groundtruth_answers[:len(predicted_answers)]
+
+    if "quac" in prediction_file:
+        """
+        For answerable cases, we want to make sure the retrieved context list contains the gold chunk.
+        For QuAC dataset, we use top-5 retrieved contexts as inputs, quac_correct_retrieved_instance_idx_list 
+        is the index list where the top-5 retrieved context contains the gold answer
+        """
+        answerable_instance_idx_list = quac_correct_retrieved_instance_idx_list
+    else:
+        answerable_instance_idx_list = None
+
+    predicted_answers_new = []
+    for pred in predicted_answers:
+        pred = pred.lower()
+        for keyphrase in unanswerable_keyphrases:
+            if keyphrase in pred:
+                pred = "Sorry. I cannot find the answer based on the context."
+                break
+        predicted_answers_new.append(pred)
+    predicted_answers = predicted_answers_new
+
+    cannot_answer_idx_list = []
+    answerable_idx_list = []
+    if answerable_instance_idx_list:
+        count_idx = 0
+    for idx, item in enumerate(groundtruth_answers):
+        if 'answers' in item:
+            answer = item["answers"][0]
+        else:
+            answer = item['answer']
+        noanswer_response = "Sorry. I cannot find the answer based on the context."
+
+        if answer == noanswer_response:
+            cannot_answer_idx_list.append(idx)
+            continue
+        
+        if answerable_instance_idx_list:
+            if count_idx in answerable_instance_idx_list:
+                answerable_idx_list.append(idx)
+            count_idx += 1
+        else:
+            answerable_idx_list.append(idx)
+
+    print("number of cannot answer cases: %d (out of %d)" % (len(cannot_answer_idx_list), len(groundtruth_answers)))
+    print("number of answerable cases: %d (out of %d)" % (len(answerable_idx_list), len(groundtruth_answers)))
+
+    return predicted_answers, cannot_answer_idx_list, answerable_idx_list
+
+
+def get_cannot_answer_and_answerable_acc(predicted_answers, cannot_answer_idx_list, answerable_idx_list):
+    # cannot answer
+    noanswer_count = 0
+    for idx in cannot_answer_idx_list:
+        prediction = predicted_answers[idx]
+        prediction = prediction.lower()
+        # print(prediction)
+        if "sorry" in prediction and "cannot find the answer" in prediction:
+            # print(prediction)
+            noanswer_count += 1
+    cannot_answer_acc = noanswer_count / len(cannot_answer_idx_list)
+    print("accuracy of cannot answer cases: %.4f" % cannot_answer_acc)
+
+    # answerable
+    answerable_count = 0
+    for idx in answerable_idx_list:
+        prediction = predicted_answers[idx]
+        prediction = prediction.lower()
+        if "sorry" in prediction and "cannot find the answer" in prediction:
+            # print(prediction)
+            continue
+        answerable_count += 1
+    answerable_acc = answerable_count / len(answerable_idx_list)
+    print("accuracy of answerable cases: %.4f" % answerable_acc)
+
+
+def evaluate_cannot_answer_acc(ground_truth_file, prediction_file):
+    predicted_answers, cannot_answer_idx_list, answerable_idx_list = \
+                                separate_cannot_answer(ground_truth_file, prediction_file)
+
+    get_cannot_answer_and_answerable_acc(predicted_answers, cannot_answer_idx_list, answerable_idx_list)
+
+
+def evaluate_convfinqa(ground_truth_file, prediction_file):
+    """
+    Since the model will give a long answer output, while the gold answer for ConvFinQA are either 
+    a arithmetic formula or a final executed number.
+    We consider the output containing either the executed number or the arithmetic formula as correct.
+    This script is to measure the proportion of the outputs containing these elements.
+    """
+
+    def _is_float(string):
+        try:
+            float(string)
+            return True
+        except ValueError:
+            return False
+
+    with open(ground_truth_file, "r") as f:
+        gold_list = json.load(f)
+    
+    groundtruth_answers = [item['exe_answer'] for item in gold_list]
+    groundtruth_answers_formula = [item['answers'][0] for item in gold_list]
+
+    ## last turn question_list
+    question_list = [item['messages'][-1]['content'] for item in gold_list]
+    predicted_answers = load_prediction(prediction_file)
+
+    print(len(predicted_answers), len(groundtruth_answers))
+    if len(predicted_answers) != len(groundtruth_answers):
+        groundtruth_answers = groundtruth_answers[:len(predicted_answers)]
+
+    count_exact_match = 0
+    for question, pred, gold, gold_formula in zip(question_list, predicted_answers, groundtruth_answers, groundtruth_answers_formula):
+
+        original_pred = pred
+        ## convert 1,000,000 into 1000000
+        original_pred = original_pred.replace(",", "")
+
+        ## convert $10 million + $20 million into 10 + 20
+        original_pred = original_pred.replace("$", "").replace("million", "").replace("billion", "")
+
+        ## convert 10 (2017) + 20 (2018) into 10 + 20
+        pattern = r'\((\b\w+\b)\)'
+        original_pred = re.sub(pattern, '', original_pred)
+
+        ## make sure it each token only has one space in between
+        original_pred = " ".join(original_pred.split())
+        
+        if str(gold) in original_pred:
+            count_exact_match += 1
+        
+        elif str(gold_formula) in original_pred:
+            count_exact_match += 1
+        
+        elif _is_float(gold) and (str(round(float(gold), 3)) in original_pred or str(round(float(gold), 2)) in original_pred):
+            count_exact_match += 1
+        
+        elif "percent" in question and (str(float(gold)*100) in original_pred or str(round(float(gold)*100, 1)) in original_pred or str(round(float(gold)*100, 2)) in original_pred):
+            count_exact_match += 1
+        
+        elif str(gold).endswith(".0") and str(int(gold)) in original_pred:
+            ## gold is a integer like 80.0 then convert it into 80
+            count_exact_match += 1
+        
+        elif "decrease" in original_pred and _is_float(gold) and gold < 0 and (str(-1 * gold) in original_pred):
+            ## for the case where model generates something like a decrese of 10 million, while gold is -10.
+            count_exact_match += 1
+
+    print("accuracy of exact match: %.4f" % (count_exact_match/len(predicted_answers)))
+
+
+def main():
+
+    ## doc2dial
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"    # e.g., outputs/doc2idal_output.txt
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"     # e.g., data/doc2dial/test.json
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+
+    ## quac
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+    evaluate_cannot_answer_acc(ground_truth_file, prediction_file)
+
+    ## qrecc
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+
+    ## topiocqa
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+
+    ## inscit
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+
+    ## coqa
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+
+    ## hybridial
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+
+    ## sqa
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+
+    ## doqa_cooking
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+    evaluate_cannot_answer_acc(ground_truth_file, prediction_file)
+
+    ## doqa_travel
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+    evaluate_cannot_answer_acc(ground_truth_file, prediction_file)
+
+    ## doqa_movies
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_f1(ground_truth_file, prediction_file)
+    evaluate_cannot_answer_acc(ground_truth_file, prediction_file)
+
+    ## convfinqa
+    prediction_file = "PATH_TO_THE_GENERATED_OUTPUT"
+    ground_truth_file = "PATH_TO_THE_TEST_DATA"
+    print("-"*80)
+    print(prediction_file)
+    print(ground_truth_file)
+    evaluate_convfinqa(ground_truth_file, prediction_file)
+
+
+if __name__ == "__main__":
+    main()
+

evaluation/metrics.py

@@ -0,0 +1,85 @@
+
+# The following code is adapted from
+# https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/metrics.py, 
+# which is licensed under the MIT license. More details on the license can be 
+# found at https://github.com/facebookresearch/ParlAI/blob/master/LICENSE.
+
+"""Provides standard metric evaluations for dialog."""
+
+from collections import Counter
+from typing import List
+import numpy as np
+import re
+
+re_art = re.compile(r'\b(a|an|the)\b')
+re_punc = re.compile(r'[!"#$%&()*+,-./:;<=>?@\[\]\\^`{|}~_\']')
+
+
+def normalize_answer(s):
+    """
+    Lower text and remove punctuation, articles and extra whitespace.
+    """
+    s = s.lower()
+    s = re_punc.sub(' ', s)
+    s = re_art.sub(' ', s)
+    s = ' '.join(s.split())
+    return s
+
+
+class F1Metric:
+    """
+    Helper class which computes token-level F1.
+    """
+
+    @staticmethod
+    def _prec_recall_f1_score(pred_items, gold_items):
+        """
+        Compute precision, recall and f1 given a set of gold and prediction items.
+        :param pred_items: iterable of predicted values
+        :param gold_items: iterable of gold values
+        :return: tuple (p, r, f1) for precision, recall, f1
+        """
+        common = Counter(gold_items) & Counter(pred_items)
+        num_same = sum(common.values())
+        if num_same == 0:
+            return 0, 0, 0
+        precision = 1.0 * num_same / len(pred_items)
+        recall = 1.0 * num_same / len(gold_items)
+        f1 = (2 * precision * recall) / (precision + recall)
+        return precision, recall, f1
+
+    @staticmethod
+    def compute_each_pair(guess: str, answer: str):
+        if answer == "":
+            return None, None, None
+        if guess == "":
+            return 0, 0, 0
+        g_tokens = normalize_answer(guess).split()
+        a_tokens = normalize_answer(answer).split()
+
+        precision, recall, f1 = F1Metric._prec_recall_f1_score(g_tokens, a_tokens)
+        return precision, recall, f1
+        
+    @staticmethod
+    def compute_all_pairs(guesses: List[str], answers: List[list]):
+        assert len(guesses) == len(answers)
+        precision_list, recall_list, f1_list = [], [], []
+        for guess, answer in zip(guesses, answers):
+            assert type(answer) == list
+            f1_list_tmp = []
+            for answer_each in answer:
+                answer_each = answer_each.strip()
+                if answer_each == "":
+                    continue
+                precision, recall, f1 = F1Metric.compute_each_pair(guess, answer_each)
+                f1_list_tmp.append(f1)
+            
+            if len(f1_list_tmp) > 0:
+                f1 = max(f1_list_tmp)
+                if precision is None or recall is None or f1 is None:
+                    continue
+                precision_list.append(precision)
+                recall_list.append(recall)
+                f1_list.append(f1)
+        
+        return np.mean(precision_list), np.mean(recall_list), np.mean(f1_list)

evaluation/run_generation_vllm.py

@@ -0,0 +1,89 @@
+
+
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+from arguments import get_args
+from dataset import load_data, get_inputs
+import torch
+import os
+
+def get_prompt_list(args):
+
+    ## get tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(args.model_id)
+
+    ## get input data
+    if args.eval_dataset == "doc2dial":
+        input_datapath = os.path.join(args.data_folder, args.doc2dial_path)
+    elif args.eval_dataset == "convfinqa":
+        input_datapath = os.path.join(args.data_folder, args.convfinqa_path)
+    elif args.eval_dataset == "quac":
+        input_datapath = os.path.join(args.data_folder, args.quac_path)
+    elif args.eval_dataset == "qrecc":
+        input_datapath = os.path.join(args.data_folder, args.qrecc_path)
+    elif args.eval_dataset == "doqa_cooking":
+        input_datapath = os.path.join(args.data_folder, args.doqa_cooking_path)
+    elif args.eval_dataset == "doqa_travel":
+        input_datapath = os.path.join(args.data_folder, args.doqa_travel_path)
+    elif args.eval_dataset == "doqa_movies":
+        input_datapath = os.path.join(args.data_folder, args.doqa_movies_path)
+    elif args.eval_dataset == "coqa":
+        input_datapath = os.path.join(args.data_folder, args.coqa_path)
+    elif args.eval_dataset == "sqa":
+        input_datapath = os.path.join(args.data_folder, args.sqa_path)
+    elif args.eval_dataset == "topiocqa":
+        input_datapath = os.path.join(args.data_folder, args.topiocqa_path)
+    elif args.eval_dataset == "inscit":
+        input_datapath = os.path.join(args.data_folder, args.inscit_path)
+    elif args.eval_dataset == "hybridial":
+        input_datapath = os.path.join(args.data_folder, args.hybridial_path)
+
+    else:
+        raise Exception("please input a correct eval_dataset name!")
+    
+    data_list = load_data(input_datapath)
+    print("number of samples in the dataset:", len(data_list))
+    prompt_list = get_inputs(data_list, args.eval_dataset, tokenizer, num_ctx=args.num_ctx, max_output_len=args.out_seq_len)
+
+    return prompt_list
+
+
+def main():
+    args = get_args()
+    
+    ## bos token for llama-3
+    bos_token = "<|begin_of_text|>"
+
+    ## get model_path
+    model_path = os.path.join(args.model_folder, args.model_name)
+    
+    ## get prompt_list
+    prompt_list = get_prompt_list(args)
+    
+    ## get output_datapath
+    output_datapath = os.path.join(args.output_folder, "%s_output.txt" % args.eval_dataset)
+
+    ## run inference
+    sampling_params = SamplingParams(temperature=0, top_k=1, max_tokens=args.max_tokens)
+
+    ## This changes the GPU support to 8
+    model_vllm = LLM(model_path, tensor_parallel_size=8)
+
+    output_list = []
+    for prompt in prompt_list:
+        prompt = bos_token + prompt
+        output = model_vllm.generate([prompt], sampling_params)[0]
+        generated_text = output.outputs[0].text
+        generated_text = generated_text.strip().replace("\n", " ")
+        
+        # print("generated_text:", generated_text)
+        output_list.append(generated_text)
+
+    print("writing to %s" % output_datapath)
+    with open(output_datapath, "w") as f:
+        for output in output_list:
+            f.write(output + "\n")
+
+
+if __name__ == "__main__":
+    main()