diff --git a/.gitignore b/.gitignore index b06beea..d3ff032 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ .python-version anserini_docs anserini_indicies +data .DS_Store dense_hs.* best_models.py @@ -171,3 +172,6 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +dense_models_backup/ +*.index \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..b21414f --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,17 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--epochs","20","--lr","6e-05","--weight_decay","0.01","--model_dir","dense_models/baseline_distilbert_0","--run_id","baseline_distilbert_0","--model_or_checkpoint","distilbert-base-uncased","--embed_size","768","--batch_size","10","--encode_batch_size","128","--data_path","/home/ddo/CMU/PLLM/TREC-TOT","--negatives_path","/home/ddo/CMU/PLLM/TREC-TOT/negatives/bm25_negatives","--negatives_out","/home/ddo/CMU/PLLM/TREC-TOT/negatives/baseline_distilbert_0_negatives","--query","title_text","--device","cuda"] + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index ac31c65..95894a5 100644 --- a/README.md +++ b/README.md @@ -5,9 +5,9 @@ The following benchmarks (& runs) are available: | Benchmark | Runfiles | Dev-DCG | Dev-Success@1000 | Dev-MRR | |----------------------|----------|----------|-----------------|-------| -| [BM25](BM25.md) (k1=0.8, b=1.0) | [train](runs/bm25/train.run), [dev](runs/bm25/dev.run) | 0.1314 | 0.4067 | 0.0881 | -| [Dense Retrieval (SBERT)](DENSE.md) (Distilbert) | [train](runs/distilbert/train.run), [dev](runs/distilbert/dev.run) | 0.1627 | 0.6600 | 0.0743 | -| [GPT-4](GPT4.md)* | [train](runs/gpt4/train.run), [dev](runs/gpt4/dev.run) | 0.2407 | 0.3200 | 0.2180 | +| [BM25](docs/BM25.md) (k1=0.8, b=1.0) | [train](runs/bm25/train.run), [dev](runs/bm25/dev.run) | 0.1314 | 0.4067 | 0.0881 | +| [Dense Retrieval (SBERT)](docs/DENSE.md) (Distilbert) | [train](runs/distilbert/train.run), [dev](runs/distilbert/dev.run) | 0.1627 | 0.6600 | 0.0743 | +| [GPT-4](docs/GPT4.md)* | [train](runs/gpt4/train.run), [dev](runs/gpt4/dev.run) | 0.2407 | 0.3200 | 0.2180 | *: GPT-4 generates 20 candidates at most. See [GPT4](GPT4.md) for more details. @@ -18,7 +18,7 @@ The following benchmarks (& runs) are available: ## optional: create new environment using py-env virtual-env ## pyenv virtualenv 3.8.11 trec-tot-benchmarks # install requirements -pip install ir_datasets sentence-transformers==2.2.2 pyserini==0.20.0 pytrec_eval faiss-cpu==1.6.5 +pip install ir_datasets sentence-transformers==2.2.2 pyserini==0.20.0 pytrec_eval faiss-cpu==1.6.5 ranx==0.3.7 ``` After downloading the files (see guidelines), set DATA_PATH to the folder which @@ -39,4 +39,4 @@ Quick test to see if data is setup properly: python tot.py ``` The command above should print the correct number of train/dev queries and the number of documents -in the corpus, along with example queries and documents. \ No newline at end of file +in the corpus, along with example queries and documents. diff --git a/bm25.py b/bm25.py index 161d528..ef1ea79 100644 --- a/bm25.py +++ b/bm25.py @@ -18,7 +18,7 @@ log = logging.getLogger(__name__) -METRICS = "recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" +METRICS = "P_1,recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" def create_index(dataset, field_to_index, dest_folder, index): diff --git a/bm25_with_rrf.py b/bm25_with_rrf.py new file mode 100644 index 0000000..a2880b8 --- /dev/null +++ b/bm25_with_rrf.py @@ -0,0 +1,121 @@ +import json +import argparse +import os +import pandas as pd +import logging + + +from tqdm import tqdm +from typing import Dict +from pyserini.search.lucene import LuceneSearcher +from pyserini.trectools import TrecRun +from pyserini.fusion import reciprocal_rank_fusion +from ranx import Qrels, Run, evaluate + +from modules import llm_based_decomposition, sentence_decomposition +import tot +import ir_datasets +from src import utils +import pytrec_eval + +METRICS = "P_1,recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" + +log = logging.getLogger(__name__) + +def main(): + parser = argparse.ArgumentParser() + # Path to indexes directory + parser.add_argument("--index_name", default="bm25_0.8_1.0", help="name of index") + + parser.add_argument("--decomposition_method", default="llm", help="how to decompose") + + parser.add_argument("--data_path", default="./data", help="location to dataset") + + parser.add_argument("--split", choices={"train", "dev", "test"}, default="dev", help="split to run") + + parser.add_argument("--index_path", default="./anserini_indicies", help="path to store (all) indices") + + parser.add_argument("--metrics", default=METRICS, help="csv - metrics to evaluate") + + parser.add_argument("--param_k1", default=0.8, type=float, help="param: k1 for BM25") + + parser.add_argument("--param_b", default=1.0, type=float, help="param: b for BM25") + + # BM25 parameters + parser.add_argument('--K', type=int, help='retrieve top K documents', default=1000) + + # Binary flags to enable or disable ranking methodss + parser.add_argument('--rm3', type=str, help='enable or disable rm3', choices=['y', 'n'], default='n') + + # Run number + parser.add_argument('--run_number', type=int, help='run number', default=1) + + # Output options and directory + parser.add_argument('--output_dir', type=str, help='path to output_dir', default="runs/") + args = parser.parse_args() + + tot.register(args.data_path) + + irds_name = "trec-tot:" + args.split + dataset = ir_datasets.load(irds_name) + if args.decomposition_method == "llm": + queries_expanded = llm_based_decomposition(dataset, f"{args.data_path}/decomposed_queries") + else: + queries_expanded = sentence_decomposition(dataset, f"{args.data_path}/decomposed_queries") + + queries = json.load(open(queries_expanded)) + + run_save_folder = f'{args.output_dir}BM25-RRF' + if args.decomposition_method == "llm": + run_save_folder += f'-llm' + + run_save_folder += f'-RM3-{args.run_number}' if args.rm3 == 'y' else f'-{args.run_number}' + + run_save_full = f"{run_save_folder}/{args.split}.run" + + searcher = LuceneSearcher(os.path.join(args.index_path, args.index_name)) + searcher.set_bm25(k1=args.param_k1, b=args.param_b) + + if args.rm3 == 'y': + searcher.set_rm3() + + # Retrieve + run_result = [] + + for query_id in tqdm(queries): + for sintetic_query_id in queries[query_id]: + hits = searcher.search(f'{queries[query_id][sintetic_query_id]}', k=args.K) + sintetic_query_results = [] + for rank, hit in enumerate(hits, start=1): + sintetic_query_results.append((query_id, 'Q0', hit.docid, rank, hit.score, f'{query_id}_{sintetic_query_id}')) + + if sintetic_query_results != []: + run_result.append(TrecRun.from_list(sintetic_query_results)) + + results = reciprocal_rank_fusion(run_result, depth=args.K, k=args.K) + + print(f"saving run to: {run_save_full}") + os.makedirs(os.path.dirname(run_save_full), exist_ok=True) + results.save_to_txt(run_save_full) + + if dataset.has_qrels(): + + with open(run_save_full, 'r') as h: + run_to_eval = pytrec_eval.parse_run(h) + + qrel, n_missing = utils.get_qrel(dataset, run_to_eval) + if n_missing > 0: + raise ValueError(f"Number of missing qids in run: {n_missing}") + + evaluator = pytrec_eval.RelevanceEvaluator( + qrel, args.metrics.split(",")) + + eval_res = evaluator.evaluate(run_to_eval) + + eval_res_agg = utils.aggregate_pytrec(eval_res, "mean") + + for metric, (mean, std) in eval_res_agg.items(): + print(f"{metric:<12}: {mean:.4f} ({std:0.4f})") + +if __name__ == '__main__': + main() diff --git a/dense_rrf.py b/dense_rrf.py new file mode 100644 index 0000000..a2880b8 --- /dev/null +++ b/dense_rrf.py @@ -0,0 +1,121 @@ +import json +import argparse +import os +import pandas as pd +import logging + + +from tqdm import tqdm +from typing import Dict +from pyserini.search.lucene import LuceneSearcher +from pyserini.trectools import TrecRun +from pyserini.fusion import reciprocal_rank_fusion +from ranx import Qrels, Run, evaluate + +from modules import llm_based_decomposition, sentence_decomposition +import tot +import ir_datasets +from src import utils +import pytrec_eval + +METRICS = "P_1,recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" + +log = logging.getLogger(__name__) + +def main(): + parser = argparse.ArgumentParser() + # Path to indexes directory + parser.add_argument("--index_name", default="bm25_0.8_1.0", help="name of index") + + parser.add_argument("--decomposition_method", default="llm", help="how to decompose") + + parser.add_argument("--data_path", default="./data", help="location to dataset") + + parser.add_argument("--split", choices={"train", "dev", "test"}, default="dev", help="split to run") + + parser.add_argument("--index_path", default="./anserini_indicies", help="path to store (all) indices") + + parser.add_argument("--metrics", default=METRICS, help="csv - metrics to evaluate") + + parser.add_argument("--param_k1", default=0.8, type=float, help="param: k1 for BM25") + + parser.add_argument("--param_b", default=1.0, type=float, help="param: b for BM25") + + # BM25 parameters + parser.add_argument('--K', type=int, help='retrieve top K documents', default=1000) + + # Binary flags to enable or disable ranking methodss + parser.add_argument('--rm3', type=str, help='enable or disable rm3', choices=['y', 'n'], default='n') + + # Run number + parser.add_argument('--run_number', type=int, help='run number', default=1) + + # Output options and directory + parser.add_argument('--output_dir', type=str, help='path to output_dir', default="runs/") + args = parser.parse_args() + + tot.register(args.data_path) + + irds_name = "trec-tot:" + args.split + dataset = ir_datasets.load(irds_name) + if args.decomposition_method == "llm": + queries_expanded = llm_based_decomposition(dataset, f"{args.data_path}/decomposed_queries") + else: + queries_expanded = sentence_decomposition(dataset, f"{args.data_path}/decomposed_queries") + + queries = json.load(open(queries_expanded)) + + run_save_folder = f'{args.output_dir}BM25-RRF' + if args.decomposition_method == "llm": + run_save_folder += f'-llm' + + run_save_folder += f'-RM3-{args.run_number}' if args.rm3 == 'y' else f'-{args.run_number}' + + run_save_full = f"{run_save_folder}/{args.split}.run" + + searcher = LuceneSearcher(os.path.join(args.index_path, args.index_name)) + searcher.set_bm25(k1=args.param_k1, b=args.param_b) + + if args.rm3 == 'y': + searcher.set_rm3() + + # Retrieve + run_result = [] + + for query_id in tqdm(queries): + for sintetic_query_id in queries[query_id]: + hits = searcher.search(f'{queries[query_id][sintetic_query_id]}', k=args.K) + sintetic_query_results = [] + for rank, hit in enumerate(hits, start=1): + sintetic_query_results.append((query_id, 'Q0', hit.docid, rank, hit.score, f'{query_id}_{sintetic_query_id}')) + + if sintetic_query_results != []: + run_result.append(TrecRun.from_list(sintetic_query_results)) + + results = reciprocal_rank_fusion(run_result, depth=args.K, k=args.K) + + print(f"saving run to: {run_save_full}") + os.makedirs(os.path.dirname(run_save_full), exist_ok=True) + results.save_to_txt(run_save_full) + + if dataset.has_qrels(): + + with open(run_save_full, 'r') as h: + run_to_eval = pytrec_eval.parse_run(h) + + qrel, n_missing = utils.get_qrel(dataset, run_to_eval) + if n_missing > 0: + raise ValueError(f"Number of missing qids in run: {n_missing}") + + evaluator = pytrec_eval.RelevanceEvaluator( + qrel, args.metrics.split(",")) + + eval_res = evaluator.evaluate(run_to_eval) + + eval_res_agg = utils.aggregate_pytrec(eval_res, "mean") + + for metric, (mean, std) in eval_res_agg.items(): + print(f"{metric:<12}: {mean:.4f} ({std:0.4f})") + +if __name__ == '__main__': + main() diff --git a/docid_to_idx.json b/docid_to_idx.json new file mode 100644 index 0000000..742ae7a --- /dev/null +++ b/docid_to_idx.json @@ -0,0 +1 @@ +{"330": 0, "324": 1, "1948": 2, "2372": 3, "3217": 4, "3333": 5, 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"72613776": 231842, "72618104": 231843, "72618400": 231844, "72619811": 231845, "72623557": 231846, "72623717": 231847, "72623953": 231848, "72633704": 231849, "72634189": 231850, "72634502": 231851} \ No newline at end of file diff --git a/docs/BM25+RRF.md b/docs/BM25+RRF.md new file mode 100644 index 0000000..e69de29 diff --git a/BM25.md b/docs/BM25.md similarity index 100% rename from BM25.md rename to docs/BM25.md diff --git a/DENSE.md b/docs/DENSE.md similarity index 100% rename from DENSE.md rename to docs/DENSE.md diff --git a/GPT4.md b/docs/GPT4.md similarity index 100% rename from GPT4.md rename to docs/GPT4.md diff --git a/gpt4-zeroshot.py b/gpt4-zeroshot.py new file mode 100644 index 0000000..a2269b5 --- /dev/null +++ b/gpt4-zeroshot.py @@ -0,0 +1,72 @@ +import time +import openai # for calling the OpenAI API +import pandas as pd # for storing text and embeddings data +import re + + +base = '/Users/aprameya/Desktop/llms-project/data/' +EMBEDDING_MODEL = "text-embedding-ada-002" +GPT_MODEL = "gpt-4" +openai.api_key = "" + + +jsonObjdata = pd.read_json("/Users/aprameya/Downloads/corpus.jsonl", lines=True) +ans = [] +count=0 +d = dict() +for index, row in jsonObjdata.iterrows(): + d[row["doc_id"]] = row["page_title"] + + + +jsonObj = pd.read_json(path_or_buf=base+"queries.jsonl", lines=True) +ans = [] +actual = [] +count=0 +for index,row in jsonObj.iterrows(): + query = f"""You are an expert in movies. You are helping someone recollect a movie name that is on the tip of their tongue. You respond to each message with a single guess for the name of the movie being described.**important**: you only mention the names of the movie and nothing else. Given below is the movie description:" + + Description: + \"\"\" + {row["text"]} + \"\"\" + + """ + response = openai.ChatCompletion.create( + messages=[ + {'role': 'system', 'content': 'You help someone recollect a movie'}, + {'role': 'user', 'content': query}, + ], + model=GPT_MODEL, + temperature=0, + ) + ans.append(response['choices'][0]['message']['content']) + actual.append(d[row['wikipedia_id']]) + print(count) + count+=1 + time.sleep(3) +with open(base+'results.txt', 'w') as fp: + for item in ans: + fp.write(item+'\n') +with open(base+'labels.txt', 'w') as fp: + for item in actual: + fp.write(item+'\n') + + +### EVALUATE ### + +with open(base+'results.txt', 'r') as f: + guesses = f.readlines() +with open(base+'labels.txt', 'r') as f: + labels = f.readlines() +count = 0 +for i in range(len(labels)): + labels[i] = re.sub("\(.*?\)|\[.*?\]","",labels[i]) + guesses[i] = re.sub("\(.*?\)|\[.*?\]","",guesses[i]) + if(labels[i].strip()==guesses[i].strip()): + count+=1 + print(labels[i]) + +print(count/150) + + \ No newline at end of file diff --git a/idx_to_docid.json b/idx_to_docid.json new file mode 100644 index 0000000..2d70e4c --- /dev/null +++ b/idx_to_docid.json @@ -0,0 +1 @@ +{"0": "330", "1": "324", "2": "1948", "3": "2372", "4": "3217", "5": "3333", "6": "3376", "7": "3427", "8": "3746", "9": "3837", "10": "3947", "11": "4227", "12": "4231", "13": "4326", "14": "4560", "15": "4726", "16": "4727", "17": "4728", "18": "4729", "19": "4730", "20": "4742", "21": "4800", "22": "4942", "23": "4948", "24": "5035", "25": "5224", "26": "5313", "27": "5729", "28": "5907", "29": "6851", "30": "7906", "31": "8209", "32": "8481", "33": "8695", "34": "8829", "35": "8994", "36": "9294", "37": "9429", "38": "9433", "39": "9835", "40": "9954", "41": "9979", "42": "9995", "43": "10193", "44": "10487", "45": "10977", "46": "11121", "47": "11167", "48": "11223", "49": "11242", "50": "11281", "51": "11313", "52": "11315", "53": "11520", "54": "11585", "55": "11673", "56": "11685", "57": "11701", "58": "11992", "59": "11988", "60": "11993", "61": "11994", "62": "11998", "63": "12000", "64": "12001", "65": "12002", "66": "12003", "67": "12004", "68": "12005", "69": "12405", "70": "12449", "71": "13149", "72": "13154", "73": "13260", "74": "13290", "75": "13523", "76": "13901", "77": "14017", "78": "16782", "79": "17920", "80": "18230", "81": "18943", "82": "18996", "83": "19055", "84": "19701", "85": "19715", "86": "20113", "87": "20421", "88": "20664", "89": "20669", "90": "20786", "91": "20840", "92": "21037", "93": "21174", "94": "21180", "95": "21711", "96": "21813", "97": "22216", "98": "22626", "99": "22727", "100": "22751", "101": "22829", "102": "23255", "103": "23512", "104": "23875", "105": "23880", "106": "24639", "107": "24653", "108": "25491", "109": "25506", "110": "25721", "111": "25912", "112": "26541", "113": "27070", "114": "27071", "115": "27073", "116": "27074", "117": "27079", "118": "27092", "119": "27097", "120": "27098", "121": "27102", "122": "27977", "123": "28217", "124": "28269", "125": "28322", "126": "28327", "127": "28606", "128": "28623", "129": "28954", "130": "29645", "131": "29781", "132": "29782", "133": "29785", "134": "29838", "135": "29942", "136": "29943", "137": "30000", "138": "30006", "139": "30007", "140": "30028", "141": "30039", "142": "30084", "143": "30086", "144": "30275", "145": "30304", "146": "30307", "147": "30327", "148": "30515", "149": "30524", "150": "30527", "151": "30575", "152": "30581", "153": "30582", "154": "30625", "155": "30674", "156": "30809", "157": "30979", "158": "31015", "159": "31030", "160": "31038", "161": "31079", "162": "31136", "163": "31153", "164": "31176", "165": "31193", "166": "31253", "167": "31298", "168": "31351", "169": "31371", "170": "31454", "171": "31523", "172": "31556", "173": "31557", "174": "31574", "175": "31607", "176": "31629", "177": "31908", "178": "32252", "179": "32260", "180": "33121", "181": "33124", "182": "33270", "183": "33446", "184": "33560", "185": "33573", "186": "33908", "187": "34130", "188": "34212", "189": "35061", "190": "36540", "191": "36986", "192": "36999", "193": "37032", "194": "37068", "195": "37599", "196": "38088", "197": "38398", "198": "39311", "199": "39690", "200": "39732", "201": "40132", "202": "40672", "203": "41528", "204": "41881", "205": "42093", "206": "42106", "207": "42137", "208": "42147", "209": "42159", "210": "42221", "211": "42222", "212": "42224", "213": "42225", "214": "42293", "215": "42294", "216": "42313", "217": "42361", "218": "42383", "219": "42507", "220": "42551", "221": "42619", "222": "42648", "223": "42706", "224": "42856", "225": "42861", "226": "42886", "227": "42971", "228": "42993", "229": "43073", "230": "43274", "231": "43307", "232": "43452", "233": "43566", "234": "43849", "235": "44122", "236": "44210", "237": "44218", "238": "44283", "239": "44302", "240": "44348", "241": "44366", "242": "44437", "243": "44457", "244": "44501", "245": "44696", "246": "44749", "247": "44752", "248": "44771", "249": "45126", "250": "45224", "251": "45242", "252": "45481", "253": "45488", "254": "45568", "255": "45606", "256": "45772", "257": "45929", "258": "46387", "259": "46505", "260": "46757", "261": "47542", "262": "47692", "263": "48064", "264": "49088", "265": "49696", "266": "49800", "267": "50287", "268": "50455", "269": "50486", "270": "50744", "271": "50793", "272": "50928", "273": "50957", "274": "51524", "275": "51563", "276": "51679", "277": "51787", "278": "51888", "279": "51906", "280": "52055", "281": "52371", "282": "52389", "283": "52390", "284": "52549", "285": "52801", "286": "52942", "287": "53078", "288": "53085", "289": "53573", "290": "53891", "291": "53964", "292": "54030", "293": "54160", "294": "54166", "295": "54173", "296": "54175", "297": "54418", "298": "54540", "299": "54586", "300": "54588", "301": "54614", "302": "54639", "303": "54663", "304": "54769", "305": "54999", "306": "55325", "307": "55447", "308": "55495", "309": "55543", "310": "55601", "311": "55720", "312": "55876", "313": "56931", "314": "56989", "315": "56996", "316": "57585", "317": "57687", "318": "57820", "319": "57975", "320": "58018", "321": "58147", "322": "58350", "323": "58865", "324": "59449", "325": "60155", "326": "60157", "327": "60164", "328": "60169", "329": "60173", "330": "60174", "331": "60293", "332": "60835", "333": "60909", "334": "60911", "335": "61046", "336": "61047", "337": "61049", "338": "61052", "339": "61053", "340": "61057", "341": "61058", "342": "61059", "343": "61060", "344": "61061", "345": "61062", "346": "61066", "347": "61070", "348": "61071", "349": "61074", "350": "61141", "351": "61160", "352": "61168", "353": "61169", "354": "61170", "355": "61171", "356": "61173", "357": "61176", "358": "61177", "359": "61175", "360": "61182", "361": "61183", "362": "61184", "363": "61186", "364": "61189", "365": "61191", "366": "61323", "367": "61411", "368": "61480", "369": "61481", "370": "61483", "371": "61484", "372": "61491", "373": "61494", "374": "61498", "375": "61499", "376": "61501", "377": "61505", "378": "61507", "379": "61508", "380": "61514", "381": "61519", "382": "61520", "383": "61522", "384": "61525", "385": "61527", "386": "61529", "387": "61530", "388": "61531", "389": "61664", "390": "61904", "391": "61949", "392": "62026", "393": "62028", "394": "62084", "395": "62095", "396": "62097", "397": "62110", "398": "62111", "399": "62115", "400": "62117", "401": "62118", "402": "62121", "403": "62122", "404": "62180", "405": "62331", "406": "62440", "407": "62592", "408": "62668", "409": "62687", "410": "62688", "411": "62689", "412": "62693", "413": "63310", "414": "63504", "415": "64004", "416": "64291", "417": "64394", "418": "64657", "419": "64902", "420": "65305", "421": "65436", "422": "65458", "423": "65562", "424": "65669", "425": "65834", "426": "65956", "427": "66070", "428": "66114", "429": "66205", "430": "66215", "431": "66305", "432": "66322", "433": "66323", "434": "66466", "435": "66841", "436": "66853", "437": "66870", "438": "66873", "439": "66972", "440": "67371", "441": "67411", "442": "67414", "443": "67415", "444": "67417", "445": "67419", "446": "67420", "447": "67422", "448": "67423", "449": "67427", "450": "67431", "451": "67433", "452": "67441", "453": "67442", "454": "67444", "455": "67916", "456": "68047", "457": "68078", "458": "68079", "459": "68082", "460": "68084", "461": "68088", "462": "68092", "463": "68099", "464": "68108", "465": "68119", "466": "68124", "467": "68125", "468": "68130", "469": "68145", "470": "68245", "471": "68305", "472": "68311", "473": "68325", "474": "68374", "475": "68388", "476": "68479", "477": "68485", "478": "68501", "479": "68757", "480": "68769", "481": "69088", "482": "69362", "483": "69625", "484": "70091", "485": "70428", "486": "70508", "487": "70647", "488": "70923", "489": "71415", "490": "71473", "491": "71975", "492": "71984", "493": "72317", "494": "72453", "495": "73170", "496": "73366", "497": "73368", "498": "73370", "499": "73372", "500": "73371", "501": "73375", "502": "73376", "503": "73380", "504": "73392", "505": "73398", "506": "73395", "507": "73401", "508": "73402", "509": "73405", "510": "73411", "511": "73409", "512": "73413", "513": "73412", "514": "73414", "515": "73417", "516": "73419", "517": "73422", "518": "73423", "519": "73425", "520": "73427", "521": "73428", "522": "73432", "523": "73434", "524": "73437", "525": "73438", "526": "73439", "527": "73440", "528": "73441", "529": "73447", "530": "73444", "531": "73451", "532": "73455", "533": "73457", "534": "73460", "535": "73463", "536": "73467", "537": "73471", "538": "73473", "539": "73475", "540": "73478", "541": "73483", "542": "73488", "543": "73494", "544": "73497", "545": "73498", "546": "73585", "547": "73663", "548": "73830", "549": "73842", "550": "73875", "551": "73882", "552": "73900", "553": "73919", "554": "73930", "555": "73937", "556": "73970", "557": "73977", "558": "73979", "559": "73982", "560": "73988", "561": "74011", "562": "74018", "563": "74028", "564": "74048", "565": "74157", "566": "74159", "567": "74293", "568": "74325", "569": "74375", "570": "74376", "571": "74381", "572": "74383", "573": "74387", "574": "74391", "575": "74392", "576": "74394", "577": "74411", "578": "74530", "579": "74534", "580": "74535", "581": "74537", "582": "74577", "583": "74823", "584": "74824", "585": "74825", "586": "74832", "587": "74830", "588": "74848", "589": "74853", "590": "74856", "591": "74858", "592": "74859", "593": "74861", "594": "74862", "595": "74864", "596": "74868", "597": "74871", "598": "74872", "599": "74877", "600": "74892", "601": "74894", "602": "74902", "603": "74904", "604": "74907", "605": "74908", "606": "74912", "607": "74913", "608": "74917", "609": "74928", "610": "74930", "611": "74931", "612": "74932", "613": "74937", "614": "74942", "615": "74951", "616": "74956", "617": "74962", "618": "74963", "619": "74967", "620": "74966", "621": "74968", "622": "74969", "623": "74971", "624": "74978", "625": "74979", "626": "74984", "627": "75152", "628": "75159", "629": "75178", "630": "75210", "631": "75222", "632": "75255", "633": "75259", "634": "75261", "635": "75263", "636": "75264", "637": "75265", "638": "75266", "639": "75268", "640": "75270", "641": "75271", "642": "75874", "643": "75933", "644": "75984", "645": "76014", "646": "76016", "647": "76018", "648": "76020", "649": "76025", "650": "76026", "651": "76030", "652": "76034", "653": "76036", "654": "76040", "655": "76297", "656": "76298", "657": "76299", "658": "76300", "659": "76301", "660": "76304", "661": "76307", "662": "76310", "663": "76313", "664": "76314", "665": "76320", "666": "76325", "667": "76331", "668": "76335", "669": "76336", "670": "76339", "671": "76341", "672": "76345", "673": "76346", "674": "76348", "675": "76349", "676": "76351", "677": "76353", "678": "76356", "679": "76357", "680": "76361", "681": "76366", "682": "76368", "683": "76371", "684": "76375", "685": "76377", "686": "76379", "687": "76383", "688": "76385", "689": "76387", "690": "76389", "691": "76390", "692": "76395", "693": "76394", "694": "76398", "695": "76399", "696": "76401", "697": "76402", "698": "76403", "699": "76515", "700": "76617", "701": "76619", "702": "76627", "703": "76628", "704": "76629", "705": "76630", "706": "76631", "707": "76632", "708": "76634", "709": "76638", "710": "76641", "711": "76643", "712": "76647", "713": "77223", "714": "77460", "715": "77593", "716": "77598", "717": "77604", "718": "77605", "719": "77609", "720": "77619", "721": "77637", "722": "77639", "723": "77641", "724": "77643", "725": "77647", "726": "77653", "727": "77658", "728": "77663", "729": "77665", "730": "77672", "731": "77674", "732": "77685", "733": "77744", "734": "77751", "735": "77856", "736": "77865", "737": "78217", "738": "78242", "739": "78425", "740": "79700", "741": "79729", "742": "79848", "743": "80405", "744": "80488", "745": "80493", "746": "80499", "747": "80510", "748": "80523", "749": "80530", "750": "80533", "751": "80538", "752": "80883", "753": "80916", "754": "80923", "755": "80944", "756": "81100", "757": "81503", "758": "82390", "759": "82423", "760": "82842", "761": "82862", "762": "83434", "763": "83448", "764": "83464", "765": "83497", "766": "84213", "767": "84462", "768": "84931", "769": "85629", "770": "85651", "771": "86533", "772": "87390", "773": "87538", "774": "87639", "775": "87678", "776": "87682", "777": "87715", "778": "87774", "779": "87828", "780": "87835", "781": "87983", "782": "88051", "783": "88075", "784": "88324", "785": "88326", "786": "88468", "787": "88470", "788": "88472", "789": "88570", "790": "88582", "791": "88609", "792": "88678", "793": "88705", "794": "89048", "795": "89056", "796": "89272", "797": "89289", "798": "89305", "799": "89306", "800": "89397", "801": "89445", "802": "89449", "803": "89541", "804": "89552", "805": "89607", "806": "89610", "807": "89629", "808": "89631", "809": "89693", "810": "89698", "811": "89723", "812": "89793", "813": "89818", "814": "89857", "815": "90039", "816": "90156", "817": "90473", "818": "90684", "819": "90687", "820": "91133", "821": "92539", "822": "92540", "823": "92566", "824": "92580", "825": "92582", "826": "92596", "827": "92605", "828": "92608", "829": "92642", "830": "92684", "831": "92688", "832": "92689", "833": "92703", "834": "92706", "835": "92715", "836": "92999", "837": "93092", "838": "93093", "839": "93494", "840": "93559", "841": "93760", "842": "93801", "843": "93996", "844": "94028", "845": "95821", "846": "95953", "847": "96185", "848": "96893", "849": "97130", "850": "97646", "851": "97732", "852": "97734", "853": "97740", "854": "97758", "855": "97769", "856": "98019", "857": "98080", "858": "98164", "859": "98503", "860": "98506", "861": "98508", "862": "98510", "863": "98989", "864": "98991", "865": "99015", "866": "99022", "867": "99025", "868": "99031", "869": "99123", "870": "99304", "871": "99434", 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...} + +GPT_MODEL = "gpt-4" +openai.api_key = "" +pattern = re.compile(r"\d\.\s(.*)") +prompt = """ +Original Query: {} +\nDecomposed Query: +""" + + +def generate(content): + """ + """ + response = openai.ChatCompletion.create( + messages=[ + {'role': 'system', + 'content': 'You are a utility that decomposes a complex query into smaller sentences that are more relevant to the movie using the examples given.'}, + {'role': 'user', 'content': content}, + ], + model='gpt-4', + temperature=0) + return response['choices'][0]['message']['content'] + + +def llm_based_decomposition(dataset, outpath): + decomposed_queries = {} + for q in dataset.queries_iter(): + subqueries = {} + decomposed = generate(prompt.format(q.text)) + for i, sentence in enumerate(decomposed.split("\n")): + try: + sentence = pattern.match(sentence).groups()[0] + except Exception as e: + pass + subqueries[i+1] = f"{q.title}. {sentence}" + + decomposed_queries[q.query_id] = subqueries + queries_file = f"{outpath}/llm_decomposed_queries.json" + + with open(queries_file, 'w', encoding="utf-8") as fp: + json.dump(decomposed_queries, fp) + + return queries_file + + +def sentence_decomposition(dataset, outpath): + """Baseline decomposition method. + Each query is decomposed into its sentences as provided in the data. + Title of orignal query prepended to each decomposed subquery. + """ + decomposed_queries = {} + for q in dataset.queries_iter(): + subqueries = {} + for sentence in q.sentence_annotations: + subqueries[sentence['id']] = f"{q.title}. {sentence['text']}" + + decomposed_queries[q.query_id] = subqueries + + queries_file = f"{outpath}/sentence_decomposed_queries.json" + + with open(queries_file, 'w', encoding="utf-8") as fp: + json.dump(decomposed_queries, fp) + + return queries_file + diff --git a/runs/BM25-RRF-1/dev.run b/runs/BM25-RRF-1/dev.run new file mode 100644 index 0000000..9c978d5 --- /dev/null +++ b/runs/BM25-RRF-1/dev.run @@ -0,0 +1,150000 @@ +1008 Q0 53887337 1 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baseline_bm25 +397 Q0 53503722 708 23.517200469970703 baseline_bm25 +397 Q0 26897935 709 23.51569938659668 baseline_bm25 +397 Q0 38350958 710 23.51289939880371 baseline_bm25 +397 Q0 706315 711 23.512399673461914 baseline_bm25 +397 Q0 15038191 712 23.50790023803711 baseline_bm25 +397 Q0 10292532 713 23.507299423217773 baseline_bm25 +397 Q0 17578735 714 23.505199432373047 baseline_bm25 +397 Q0 27662044 715 23.5044002532959 baseline_bm25 +397 Q0 55234829 716 23.501399993896484 baseline_bm25 +397 Q0 9387429 717 23.500900268554688 baseline_bm25 +397 Q0 67665673 718 23.500499725341797 baseline_bm25 +397 Q0 21776777 719 23.5 baseline_bm25 +397 Q0 1822048 720 23.49720001220703 baseline_bm25 +397 Q0 22203372 721 23.489999771118164 baseline_bm25 +397 Q0 543399 722 23.486299514770508 baseline_bm25 +397 Q0 6612569 723 23.485000610351562 baseline_bm25 +397 Q0 635322 724 23.484399795532227 baseline_bm25 +397 Q0 1186616 725 23.479900360107422 baseline_bm25 +397 Q0 43846431 726 23.478099822998047 baseline_bm25 +397 Q0 7851947 727 23.476600646972656 baseline_bm25 +397 Q0 24629467 728 23.472000122070312 baseline_bm25 +397 Q0 23243428 729 23.47170066833496 baseline_bm25 +397 Q0 30273553 730 23.471599578857422 baseline_bm25 +397 Q0 1051820 731 23.470800399780273 baseline_bm25 +397 Q0 2239292 732 23.470199584960938 baseline_bm25 +397 Q0 59267903 733 23.46339988708496 baseline_bm25 +397 Q0 9505271 734 23.45840072631836 baseline_bm25 +397 Q0 39187369 735 23.454999923706055 baseline_bm25 +397 Q0 357245 736 23.454500198364258 baseline_bm25 +397 Q0 2212223 737 23.450300216674805 baseline_bm25 +397 Q0 1777628 738 23.447099685668945 baseline_bm25 +397 Q0 924799 739 23.44339942932129 baseline_bm25 +397 Q0 56440839 740 23.440099716186523 baseline_bm25 +397 Q0 39159786 741 23.439300537109375 baseline_bm25 +397 Q0 36570354 742 23.438499450683594 baseline_bm25 +397 Q0 6718040 743 23.43779945373535 baseline_bm25 +397 Q0 21971275 744 23.433399200439453 baseline_bm25 +397 Q0 28733477 745 23.43000030517578 baseline_bm25 +397 Q0 16881835 746 23.428699493408203 baseline_bm25 +397 Q0 19268787 747 23.428499221801758 baseline_bm25 +397 Q0 56053608 748 23.42609977722168 baseline_bm25 +397 Q0 21339336 749 23.424400329589844 baseline_bm25 +397 Q0 417987 750 23.42140007019043 baseline_bm25 +397 Q0 7629286 751 23.417200088500977 baseline_bm25 +397 Q0 529276 752 23.41309928894043 baseline_bm25 +397 Q0 173330 753 23.412900924682617 baseline_bm25 +397 Q0 1389099 754 23.412700653076172 baseline_bm25 +397 Q0 456555 755 23.41080093383789 baseline_bm25 +397 Q0 85651 756 23.410600662231445 baseline_bm25 +397 Q0 1873386 757 23.409700393676758 baseline_bm25 +397 Q0 43215087 758 23.408300399780273 baseline_bm25 +397 Q0 2433724 759 23.408199310302734 baseline_bm25 +397 Q0 60205896 760 23.408098220825195 baseline_bm25 +397 Q0 987894 761 23.407100677490234 baseline_bm25 +397 Q0 4460314 762 23.403099060058594 baseline_bm25 +397 Q0 39358996 763 23.398700714111328 baseline_bm25 +397 Q0 2735558 764 23.393299102783203 baseline_bm25 +397 Q0 9770050 765 23.39329719543457 baseline_bm25 +397 Q0 44086482 766 23.386699676513672 baseline_bm25 +397 Q0 6060651 767 23.384199142456055 baseline_bm25 +397 Q0 63176346 768 23.382999420166016 baseline_bm25 +397 Q0 3747178 769 23.382699966430664 baseline_bm25 +397 Q0 733213 770 23.377199172973633 baseline_bm25 +397 Q0 4597853 771 23.376300811767578 baseline_bm25 +397 Q0 9072423 772 23.373199462890625 baseline_bm25 +397 Q0 2918925 773 23.368499755859375 baseline_bm25 +397 Q0 5748375 774 23.36720085144043 baseline_bm25 +397 Q0 37375089 775 23.36479949951172 baseline_bm25 +397 Q0 225481 776 23.361499786376953 baseline_bm25 +397 Q0 335298 777 23.354999542236328 baseline_bm25 +397 Q0 7999587 778 23.354400634765625 baseline_bm25 +397 Q0 42304176 779 23.350299835205078 baseline_bm25 +397 Q0 1889426 780 23.345600128173828 baseline_bm25 +397 Q0 33432718 781 23.34160041809082 baseline_bm25 +397 Q0 982480 782 23.335800170898438 baseline_bm25 +397 Q0 60411747 783 23.333099365234375 baseline_bm25 +397 Q0 2370259 784 23.327600479125977 baseline_bm25 +397 Q0 14965069 785 23.325300216674805 baseline_bm25 +397 Q0 442333 786 23.323400497436523 baseline_bm25 +397 Q0 60755041 787 23.321399688720703 baseline_bm25 +397 Q0 28352957 788 23.31679916381836 baseline_bm25 +397 Q0 5326392 789 23.31439971923828 baseline_bm25 +397 Q0 22224559 790 23.311899185180664 baseline_bm25 +397 Q0 52390 791 23.308399200439453 baseline_bm25 +397 Q0 27223546 792 23.308000564575195 baseline_bm25 +397 Q0 22277030 793 23.307100296020508 baseline_bm25 +397 Q0 2919100 794 23.30459976196289 baseline_bm25 +397 Q0 10206962 795 23.30389976501465 baseline_bm25 +397 Q0 17922358 796 23.300899505615234 baseline_bm25 +397 Q0 55711095 797 23.2947998046875 baseline_bm25 +397 Q0 31602346 798 23.291099548339844 baseline_bm25 +397 Q0 27457018 799 23.288999557495117 baseline_bm25 +397 Q0 26358018 800 23.288700103759766 baseline_bm25 +397 Q0 8627638 801 23.284400939941406 baseline_bm25 +397 Q0 1221386 802 23.281200408935547 baseline_bm25 +397 Q0 2361396 803 23.279499053955078 baseline_bm25 +397 Q0 53977445 804 23.278799057006836 baseline_bm25 +397 Q0 485789 805 23.276599884033203 baseline_bm25 +397 Q0 40740387 806 23.272300720214844 baseline_bm25 +397 Q0 1854908 807 23.26759910583496 baseline_bm25 +397 Q0 1057569 808 23.266300201416016 baseline_bm25 +397 Q0 38593085 809 23.263500213623047 baseline_bm25 +397 Q0 16198836 810 23.26140022277832 baseline_bm25 +397 Q0 12758034 811 23.259899139404297 baseline_bm25 +397 Q0 1442085 812 23.256200790405273 baseline_bm25 +397 Q0 1453473 813 23.25279998779297 baseline_bm25 +397 Q0 12476867 814 23.252199172973633 baseline_bm25 +397 Q0 37277581 815 23.250499725341797 baseline_bm25 +397 Q0 3520815 816 23.24519920349121 baseline_bm25 +397 Q0 6534174 817 23.243799209594727 baseline_bm25 +397 Q0 338869 818 23.24250030517578 baseline_bm25 +397 Q0 43049997 819 23.241500854492188 baseline_bm25 +397 Q0 56509106 820 23.23870086669922 baseline_bm25 +397 Q0 706878 821 23.23509979248047 baseline_bm25 +397 Q0 12138300 822 23.231000900268555 baseline_bm25 +397 Q0 33891030 823 23.228099822998047 baseline_bm25 +397 Q0 57404182 824 23.227500915527344 baseline_bm25 +397 Q0 21882426 825 23.22209930419922 baseline_bm25 +397 Q0 13900943 826 23.220199584960938 baseline_bm25 +397 Q0 36787491 827 23.216999053955078 baseline_bm25 +397 Q0 47707306 828 23.215499877929688 baseline_bm25 +397 Q0 26476627 829 23.213300704956055 baseline_bm25 +397 Q0 47187788 830 23.211700439453125 baseline_bm25 +397 Q0 49709831 831 23.206300735473633 baseline_bm25 +397 Q0 32520884 832 23.20549964904785 baseline_bm25 +397 Q0 59394452 833 23.20509910583496 baseline_bm25 +397 Q0 36180741 834 23.202699661254883 baseline_bm25 +397 Q0 217800 835 23.202199935913086 baseline_bm25 +397 Q0 31551814 836 23.199899673461914 baseline_bm25 +397 Q0 3978689 837 23.19930076599121 baseline_bm25 +397 Q0 1027060 838 23.198200225830078 baseline_bm25 +397 Q0 2305746 839 23.197799682617188 baseline_bm25 +397 Q0 42997494 840 23.19499969482422 baseline_bm25 +397 Q0 1760098 841 23.192899703979492 baseline_bm25 +397 Q0 503573 842 23.192798614501953 baseline_bm25 +397 Q0 44863990 843 23.1924991607666 baseline_bm25 +397 Q0 47425203 844 23.191699981689453 baseline_bm25 +397 Q0 740989 845 23.190000534057617 baseline_bm25 +397 Q0 2952485 846 23.189300537109375 baseline_bm25 +397 Q0 1578168 847 23.184499740600586 baseline_bm25 +397 Q0 61742681 848 23.18239974975586 baseline_bm25 +397 Q0 1026330 849 23.18229866027832 baseline_bm25 +397 Q0 13123591 850 23.18079948425293 baseline_bm25 +397 Q0 443972 851 23.17889976501465 baseline_bm25 +397 Q0 884034 852 23.176700592041016 baseline_bm25 +397 Q0 52021909 853 23.175399780273438 baseline_bm25 +397 Q0 43829061 854 23.17289924621582 baseline_bm25 +397 Q0 26154108 855 23.172100067138672 baseline_bm25 +397 Q0 231829 856 23.170799255371094 baseline_bm25 +397 Q0 24542316 857 23.170299530029297 baseline_bm25 +397 Q0 22098183 858 23.16990089416504 baseline_bm25 +397 Q0 21495081 859 23.16790008544922 baseline_bm25 +397 Q0 58031267 860 23.164199829101562 baseline_bm25 +397 Q0 41664127 861 23.16200065612793 baseline_bm25 +397 Q0 1164579 862 23.157100677490234 baseline_bm25 +397 Q0 1067426 863 23.156099319458008 baseline_bm25 +397 Q0 38487740 864 23.156097412109375 baseline_bm25 +397 Q0 17072787 865 23.15599822998047 baseline_bm25 +397 Q0 17697980 866 23.155799865722656 baseline_bm25 +397 Q0 23774161 867 23.15530014038086 baseline_bm25 +397 Q0 29429728 868 23.153200149536133 baseline_bm25 +397 Q0 35566754 869 23.15220069885254 baseline_bm25 +397 Q0 34418416 870 23.152099609375 baseline_bm25 +397 Q0 203841 871 23.150100708007812 baseline_bm25 +397 Q0 657812 872 23.14389991760254 baseline_bm25 +397 Q0 5595246 873 23.141199111938477 baseline_bm25 +397 Q0 391455 874 23.137800216674805 baseline_bm25 +397 Q0 28236324 875 23.136899948120117 baseline_bm25 +397 Q0 18560748 876 23.13640022277832 baseline_bm25 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baseline_bm25 +397 Q0 23691233 896 23.105199813842773 baseline_bm25 +397 Q0 9573761 897 23.100400924682617 baseline_bm25 +397 Q0 4515738 898 23.09980010986328 baseline_bm25 +397 Q0 14421091 899 23.097200393676758 baseline_bm25 +397 Q0 28789149 900 23.096599578857422 baseline_bm25 +397 Q0 455013 901 23.096498489379883 baseline_bm25 +397 Q0 35584292 902 23.094600677490234 baseline_bm25 +397 Q0 48920674 903 23.092100143432617 baseline_bm25 +397 Q0 1888453 904 23.089399337768555 baseline_bm25 +397 Q0 1009305 905 23.088199615478516 baseline_bm25 +397 Q0 2091700 906 23.087600708007812 baseline_bm25 +397 Q0 50352193 907 23.08639907836914 baseline_bm25 +397 Q0 8441268 908 23.085100173950195 baseline_bm25 +397 Q0 1820227 909 23.08449935913086 baseline_bm25 +397 Q0 1371138 910 23.083499908447266 baseline_bm25 +397 Q0 32942181 911 23.082000732421875 baseline_bm25 +397 Q0 68203806 912 23.080400466918945 baseline_bm25 +397 Q0 30698901 913 23.0802001953125 baseline_bm25 +397 Q0 206490 914 23.07979965209961 baseline_bm25 +397 Q0 32111330 915 23.075599670410156 baseline_bm25 +397 Q0 33280854 916 23.075199127197266 baseline_bm25 +397 Q0 37024784 917 23.072999954223633 baseline_bm25 +397 Q0 50600148 918 23.071399688720703 baseline_bm25 +397 Q0 2817713 919 23.07110023498535 baseline_bm25 +397 Q0 26499171 920 23.069499969482422 baseline_bm25 +397 Q0 22215272 921 23.068700790405273 baseline_bm25 +397 Q0 64523088 922 23.066200256347656 baseline_bm25 +397 Q0 2447735 923 23.06439971923828 baseline_bm25 +397 Q0 3075236 924 23.06130027770996 baseline_bm25 +397 Q0 930501 925 23.06089973449707 baseline_bm25 +397 Q0 25857222 926 23.060199737548828 baseline_bm25 +397 Q0 1296482 927 23.059999465942383 baseline_bm25 +397 Q0 702579 928 23.057600021362305 baseline_bm25 +397 Q0 28698868 929 23.054899215698242 baseline_bm25 +397 Q0 9974375 930 23.05489730834961 baseline_bm25 +397 Q0 35940098 931 23.053499221801758 baseline_bm25 +397 Q0 555169 932 23.051000595092773 baseline_bm25 +397 Q0 265496 933 23.04990005493164 baseline_bm25 +397 Q0 33526730 934 23.04960060119629 baseline_bm25 +397 Q0 68065229 935 23.04560089111328 baseline_bm25 +397 Q0 12034233 936 23.044700622558594 baseline_bm25 +397 Q0 1330774 937 23.043800354003906 baseline_bm25 +397 Q0 40008354 938 23.043699264526367 baseline_bm25 +397 Q0 82842 939 23.040599822998047 baseline_bm25 +397 Q0 12591488 940 23.034000396728516 baseline_bm25 +397 Q0 27604306 941 23.03070068359375 baseline_bm25 +397 Q0 29007213 942 23.030399322509766 baseline_bm25 +397 Q0 72285166 943 23.029300689697266 baseline_bm25 +397 Q0 18892860 944 23.028400421142578 baseline_bm25 +397 Q0 474527 945 23.024900436401367 baseline_bm25 +397 Q0 31774825 946 23.024099349975586 baseline_bm25 +397 Q0 583221 947 23.022499084472656 baseline_bm25 +397 Q0 60962760 948 23.022397994995117 baseline_bm25 +397 Q0 2215034 949 23.021400451660156 baseline_bm25 +397 Q0 42232449 950 23.019500732421875 baseline_bm25 +397 Q0 47969400 951 23.017000198364258 baseline_bm25 +397 Q0 43123901 952 23.016399383544922 baseline_bm25 +397 Q0 38767245 953 23.015600204467773 baseline_bm25 +397 Q0 49831622 954 23.01059913635254 baseline_bm25 +397 Q0 39483147 955 23.008399963378906 baseline_bm25 +397 Q0 22265112 956 23.00819969177246 baseline_bm25 +397 Q0 51456753 957 23.006099700927734 baseline_bm25 +397 Q0 21998954 958 23.003299713134766 baseline_bm25 +397 Q0 23243433 959 23.001699447631836 baseline_bm25 +397 Q0 37597656 960 22.99720001220703 baseline_bm25 +397 Q0 47446906 961 22.99489974975586 baseline_bm25 +397 Q0 23793770 962 22.989099502563477 baseline_bm25 +397 Q0 29360653 963 22.986600875854492 baseline_bm25 +397 Q0 1412856 964 22.983699798583984 baseline_bm25 +397 Q0 56733729 965 22.98259925842285 baseline_bm25 +397 Q0 44703906 966 22.982200622558594 baseline_bm25 +397 Q0 51380026 967 22.980300903320312 baseline_bm25 +397 Q0 3485095 968 22.978300094604492 baseline_bm25 +397 Q0 53098969 969 22.976499557495117 baseline_bm25 +397 Q0 53031069 970 22.96969985961914 baseline_bm25 +397 Q0 1703177 971 22.965599060058594 baseline_bm25 +397 Q0 67439954 972 22.965299606323242 baseline_bm25 +397 Q0 4954437 973 22.964000701904297 baseline_bm25 +397 Q0 32049399 974 22.959800720214844 baseline_bm25 +397 Q0 30859309 975 22.956199645996094 baseline_bm25 +397 Q0 38178212 976 22.955900192260742 baseline_bm25 +397 Q0 57304665 977 22.951900482177734 baseline_bm25 +397 Q0 37760218 978 22.945899963378906 baseline_bm25 +397 Q0 437332 979 22.940000534057617 baseline_bm25 +397 Q0 1238514 980 22.939899444580078 baseline_bm25 +397 Q0 3186725 981 22.934799194335938 baseline_bm25 +397 Q0 54758266 982 22.928300857543945 baseline_bm25 +397 Q0 40770354 983 22.92729949951172 baseline_bm25 +397 Q0 607673 984 22.92140007019043 baseline_bm25 +397 Q0 11419349 985 22.920299530029297 baseline_bm25 +397 Q0 63535965 986 22.915700912475586 baseline_bm25 +397 Q0 54327921 987 22.91309928894043 baseline_bm25 +397 Q0 210860 988 22.909000396728516 baseline_bm25 +397 Q0 357510 989 22.90880012512207 baseline_bm25 +397 Q0 889661 990 22.906999588012695 baseline_bm25 +397 Q0 1310735 991 22.904199600219727 baseline_bm25 +397 Q0 7537900 992 22.899599075317383 baseline_bm25 +397 Q0 2624659 993 22.897300720214844 baseline_bm25 +397 Q0 67423 994 22.896099090576172 baseline_bm25 +397 Q0 48667380 995 22.89459991455078 baseline_bm25 +397 Q0 177906 996 22.89150047302246 baseline_bm25 +397 Q0 70676584 997 22.88920021057129 baseline_bm25 +397 Q0 61836448 998 22.885000228881836 baseline_bm25 +397 Q0 423019 999 22.882200241088867 baseline_bm25 584 Q0 2418347 0 105.4092025756836 baseline_bm25 584 Q0 17697980 1 98.85679626464844 baseline_bm25 584 Q0 885876 2 97.81300354003906 baseline_bm25 diff --git a/scripts/run_bm25.sh b/scripts/run_bm25.sh new file mode 100755 index 0000000..2ebb55a --- /dev/null +++ b/scripts/run_bm25.sh @@ -0,0 +1,13 @@ +# run: /scripts/run_bm25.sh split +# split: train or dev +# train can be used to generate negatives. + +split=$1 +data_path=/home/ddo/CMU/PLLM/TREC-TOT + +python bm25.py --index_name bm25_0.8_1.0 --index_path ./anserini_indicies \ + --data_path $data_path \ + --param_k1 0.8 --param_b 1.0 \ + --field text --query title_text --split $split \ + --run ./runs/bm25/$split.run --run_format trec_eval --run_id baseline_bm25 \ + --negatives_out $data_path/negatives/bm25_negatives/$split-title_text-negatives.json diff --git a/scripts/run_bm25_rff.sh b/scripts/run_bm25_rff.sh new file mode 100755 index 0000000..4bc26d3 --- /dev/null +++ b/scripts/run_bm25_rff.sh @@ -0,0 +1 @@ +python bm25_with_rrf.py \ No newline at end of file diff --git a/scripts/run_dense.sh b/scripts/run_dense.sh new file mode 100755 index 0000000..e0a83c9 --- /dev/null +++ b/scripts/run_dense.sh @@ -0,0 +1,20 @@ +#data_path=./data +data_path=/home/ddo/CMU/PLLM/TREC-TOT + + +echo python train_dense.py --epochs 20 --lr 6e-05 --weight_decay 0.01 \ + --model_dir dense_models/baseline_distilbert_0 \ + --run_id baseline_distilbert_0 --model_or_checkpoint distilbert-base-uncased \ + --embed_size 768 --batch_size 10 --encode_batch_size 128 --data_path $data_path \ + --negatives_path $data_path/negatives/bm25_negatives \ + --negatives_out $data_path/negatives/baseline_distilbert_0_negatives \ + --query title_text \ + --device cuda + +python train_dense.py --epochs 20 --lr 6e-05 --weight_decay 0.01 \ + --negatives_path $data_path/negatives/baseline_distilbert_0_negatives \ + --negatives_out $data_path/negatives/baseline_distilbert_1_negatives \ + --model_dir dense_models/baseline_distilbert \ + --run_id baseline_distilbert --model_or_checkpoint distilbert-base-uncased \ + --embed_size 768 --batch_size 10 --encode_batch_size 128 --data_path $data_path \ + --query title_text --device cuda diff --git a/tot.py b/tot.py index 28801b6..912b82c 100644 --- a/tot.py +++ b/tot.py @@ -145,13 +145,13 @@ def register(path): if __name__ == '__main__': - path = input("Enter data path:") + path = "./data" register(path.strip()) sets = [] - for split in {"train", "dev", "test"}: + for split in {"train", "dev"}: name = split sets.append(NAME + ":" + name) diff --git a/train_dence_rrf.py b/train_dence_rrf.py new file mode 100644 index 0000000..d0d460f --- /dev/null +++ b/train_dence_rrf.py @@ -0,0 +1,246 @@ +import ir_datasets +from torch.utils.data import DataLoader +from sentence_transformers import SentenceTransformer, losses, models +from src import data, encode, utils +import pytrec_eval +from torch import nn +import argparse +import os +import logging +from faiss import write_index, read_index +import json +import tot +import bm25 + +log = logging.getLogger(__name__) + +if __name__ == '__main__': + + parser = argparse.ArgumentParser("train_dense", description="Trains a dense retrieval model") + + parser.add_argument("--data_path", default="./datasets/TREC-TOT", help="location to dataset") + + parser.add_argument("--negatives_path", default="./bm25_negatives", + help="path to folder containing negatives ") + + parser.add_argument("--query", choices=["title", "text", "title_text"], default="title_text") + + parser.add_argument("--model_or_checkpoint", type=str, required=True, help="hf checkpoint/ path to pt-model") + parser.add_argument("--embed_size", required=True, type=int, help="hidden size of the model") + + parser.add_argument("--epochs", type=int, required=True, help="number of epochs to train") + parser.add_argument("--lr", type=float, default=2e-5, help="learning rate") + parser.add_argument("--weight_decay", type=float, default=0.01, help="weight decay") + parser.add_argument("--warmup_steps", type=int, default=0, help="warmup steps") + parser.add_argument("--batch_size", type=int, default=24, help="batch size (training)") + parser.add_argument("--encode_batch_size", type=int, default=124, help="batch size (inference)") + parser.add_argument("--evaluation_steps", type=int, default=-1, help="steps before evaluation is run") + + parser.add_argument("--freeze_base_model", action="store_true", default=False, + help="if set, freezes the base layer and trains only a projection layer on top") + parser.add_argument("--metrics", required=False, default=bm25.METRICS, help="csv - metrics to evaluate") + parser.add_argument("--n_hits", default=1000, type=int, help="number of hits to retrieve") + + parser.add_argument("--device", type=str, default="cuda", help="device to train /evaluate model on") + + parser.add_argument("--model_dir", type=str, help="folder to store model & runs", required=True) + parser.add_argument("--run_id", required=True, help="run id (required if run_format = trec_eval)") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--negatives_out", default=None, + help="if provided, dumps negatives for use in training other models") + parser.add_argument("--n_negatives", default=10, type=int, help="number of negatives to obtain") + + logging.basicConfig(level=logging.INFO) + + args = parser.parse_args() + utils.set_seed(args.seed) + log.info(f"args: {args}") + + tot.register(args.data_path) + metrics = args.metrics.split(",") + + model_dir = args.model_dir + os.makedirs(model_dir, exist_ok=True) + + if args.freeze_base_model: + base_model = SentenceTransformer(args.model_or_checkpoint, device=args.device) + for param in base_model.parameters(): + param.requires_grad = False + projection = models.Dense(args.embed_size, args.embed_size, + activation_function=nn.Tanh()) + model = SentenceTransformer(modules=[base_model, projection], device=args.device) + else: + model = SentenceTransformer("/home/ddo/CMU/PLLM/llms-project/dense_models/baseline_distilbert/model", device="cuda") + + # model = SentenceTransformer(args.model_or_checkpoint, device=args.device) + + irds_splits = {} + st_data = {} + args.decomposition_method = "llm" + if args.decomposition_method == "llm": + queries_expanded = "/home/ddo/CMU/PLLM/TREC-TOT/decomposed_queries/llm_decomposed_queries.json" + else: + queries_expanded = "/home/ddo/CMU/PLLM/TREC-TOT/decomposed_queries/sentence_decomposed_queries.json" + + queries = json.load(open(queries_expanded)) + args.output_dir = "/home/ddo/CMU/PLLM/TREC-TOT/" + run_save_folder = f'{args.output_dir}DENSE-RRF' + if args.decomposition_method == "llm": + run_save_folder += f'-llm' + # splits + args.run_number = 1 + args.rm3 = 'y' + args.split = 'dev' + run_save_folder += f'-RM3-{args.run_number}' if args.rm3 == 'y' else f'-{args.run_number}' + + run_save_full = f"{run_save_folder}/{args.split}.run" + + for split in {"train", "dev"}: + irds_splits[split] = ir_datasets.load(f"trec-tot:{split}") + + log.info(f"loaded split {split}") + st_data[split] = data.SBERTDataset(irds_splits[split], query_type=args.query, + negatives=utils.read_json( + os.path.join(args.negatives_path, + f"{split}-{args.query}-negatives.json"))) + + log.info(f"training model for {args.epochs} epochs") + train_dataloader = DataLoader(st_data["train"], shuffle=True, batch_size=args.batch_size) + + args.loss_fn = "mnrl" + if args.loss_fn == "mnrl": + train_loss = losses.MultipleNegativesRankingLoss(model=model) + else: + raise NotImplementedError(args.loss_fn) + + val_evaluator = data.get_ir_evaluator(st_data["dev"], name=f"dev", + mrr_at_k=[1000], + ndcg_at_k=[10, 1000], + corpus_chunk_size=args.encode_batch_size) + + optimizer_params = { + "lr": args.lr + } + + # Tune the model + """model.fit(train_objectives=[(train_dataloader, train_loss)], + evaluation_steps=args.evaluation_steps, + output_path=os.path.join(model_dir, "model"), + evaluator=val_evaluator, + epochs=args.epochs, + warmup_steps=args.warmup_steps, + optimizer_params=optimizer_params, + weight_decay=args.weight_decay, + save_best_model=True)""" + + log.info("encoding corpus with model") + embed_size = args.embed_size + # index, (idx_to_docid, docid_to_idx) = encode.encode_dataset_faiss(model, embedding_size=embed_size, + # dataset=irds_splits["train"], + # device=args.device, + # encode_batch_size=args.encode_batch_size) + index = read_index( "dense.index") + idx_to_docid = json.load(open("idx_to_docid.json","r")) + docid_to_idx = json.load(open("docid_to_idx.json","r")) + runs = {} + eval_res_agg = {} + eval_res = {} + + try: + log.info("attempting to load test set") + # plug in the test set + irds_splits["test"] = ir_datasets.load(f"trec-tot:test") + log.info("success!") + except KeyError: + log.info("couldn't find test set!") + pass + + split_qrels = {} + model.eval() + + qids = [] + # queries = [] + # for query in queries: + # queries.append(utils.get_query(query, query_type)) + # qids.append(query.query_id) + import torch + eval_batch_size = 64 + device = 'cpu' + # with torch.no_grad(): + # query_embeddings = model.encode(queries, batch_size=eval_batch_size, show_progress_bar=True, + # convert_to_numpy=True, device=device) + + # top_k = 10 + # scores, raw_doc_ids = index.search(query_embeddings, k=top_k) + # run = {} + # for qid, sc, rdoc_ids in zip(qids, scores, raw_doc_ids): + # run[qid] = {} + # for s, rdid in zip(sc, rdoc_ids): + # if rdid == -1: + # log.warning(f"invalid doc ids!") + # continue + # run[qid][idx_to_docid[rdid]] = float(s) + + # return run + # for split, dataset in irds_splits.items(): + # log.info(f"running & evaluating {split}") + + # run = encode.create_run_faiss(model=model, + # dataset=dataset, + # query_type=args.query, device=args.device, + # eval_batch_size=args.encode_batch_size, + # index=index, idx_to_docid=idx_to_docid, + # docid_to_idx=docid_to_idx, + # top_k=args.n_hits) + # runs[split] = run + + # if dataset.has_qrels(): + # qrel, n_missing = utils.get_qrel(dataset, run) + # split_qrels[split] = qrel + # evaluator = pytrec_eval.RelevanceEvaluator( + # qrel, metrics) + + # eval_res[split] = evaluator.evaluate(run) + # eval_res_agg[split] = utils.aggregate_pytrec(eval_res[split], "mean") + + # for metric, (mean, std) in eval_res_agg[split].items(): + # log.info(f"{metric:<12}: {mean:.4f} ({std:0.4f})") + + utils.write_json({ + "aggregated_result": eval_res_agg, + "run": runs, + "result": eval_res, + "args": vars(args) + }, os.path.join(model_dir, "out.gz"), zipped=True) + + run_id = args.run_id + assert run_id is not None + + for split, run in runs.items(): + run_path = os.path.join(model_dir, f"{split}.run") + with open(run_path, "w") as writer: + for qid, r in run.items(): + for rank, (doc_id, score) in enumerate(sorted(r.items(), key=lambda _: -_[1])): + writer.write(f"{qid}\tQ0\t{doc_id}\t{rank}\t{score}\t{run_id}\n") + + if args.negatives_out: + log.info(f"writing negatives to folder: {args.negatives_out}") + os.makedirs(args.negatives_out, exist_ok=True) + out = {} + + for split, run in runs.items(): + if split == "test": + continue + negatives_path = os.path.join(args.negatives_out, f"{split}-{args.query}-negatives.json") + qrel = split_qrels[split] + for qid, hits in run.items(): + hits = sorted(hits.items(), key=lambda _: -_[1]) + negs = [] + for (doc, score) in hits: + if qrel[qid].get(doc, 0) > 0: + continue + if len(negs) == args.n_negatives: + break + negs.append(doc) + out[qid] = negs + utils.write_json(out, negatives_path) diff --git a/train_dense.py b/train_dense.py index a75c873..277ef6e 100644 --- a/train_dense.py +++ b/train_dense.py @@ -7,7 +7,8 @@ import argparse import os import logging - +from faiss import write_index, read_index +import json import tot import bm25 @@ -69,7 +70,9 @@ activation_function=nn.Tanh()) model = SentenceTransformer(modules=[base_model, projection], device=args.device) else: - model = SentenceTransformer(args.model_or_checkpoint, device=args.device) + model = SentenceTransformer("/home/ddo/CMU/PLLM/llms-project/dense_models/baseline_distilbert_0/model", device="cuda") + + # model = SentenceTransformer(args.model_or_checkpoint, device=args.device) irds_splits = {} st_data = {} @@ -119,7 +122,9 @@ dataset=irds_splits["train"], device=args.device, encode_batch_size=args.encode_batch_size) - + write_index(index, "dense.index") + json.dump(idx_to_docid, open("idx_to_docid.json","w")) + json.dump(docid_to_idx, open("docid_to_idx.json","w")) runs = {} eval_res_agg = {} eval_res = {}