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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
+assets/scores.jpg filter=lfs diff=lfs merge=lfs -text

assets/evalPytrec.py

@@ -0,0 +1,183 @@
+import os
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+os.environ["HF_HOME"] = "../../cache/hgCache"
+os.environ["TRANSFORMERS_CACHE"] = "../../cache/transformersCache/"
+
+import glob
+import logging
+import sys
+from collections import defaultdict
+
+import numpy as np
+import pytrec_eval
+import tqdm, torch
+import pandas as pd
+from pylate import models, rank
+
+
+document_length = 512
+
+model_name_or_paths = [
+    "9eren99/TrColBERT",
+    "jinaai/jina-colbert-v2",
+    "antoinelouis/colbert-xm",
+]
+
+datasetnames = [
+    "fiqa2018",
+    "climatefever",
+    "dbpedia",
+    "fever",
+    "hotpotqa",
+    # "msmarco",
+    "nfcorpus",
+    "nq",
+    "quoraretrieval",
+    "scidocs",
+    "arguana",
+    "scifact",
+    "touche2020",
+]
+for datasetname in datasetnames:
+    print("#############", datasetname, "##############")
+    evalResultsDf = None
+    for model_name_or_path in model_name_or_paths:
+        torch.cuda.empty_cache()
+        if "jinaai/jina-colbert-v2" == model_name_or_path:
+            model = models.ColBERT(
+                model_name_or_path=model_name_or_path,
+                query_prefix="[QueryMarker]",
+                document_prefix="[DocumentMarker]",
+                attend_to_expansion_tokens=True,
+                trust_remote_code=True,
+                document_length=document_length,
+            )
+        elif "antoinelouis/colbert-xm" == model_name_or_path:
+            model = models.ColBERT(model_name_or_path="antoinelouis/colbert-xm")
+            language = "tr_TR"  # Use a code from https://huggingface.co/facebook/xmod-base#languages
+
+            backbone = model[0].auto_model
+            if backbone.__class__.__name__.lower().startswith("xmod"):
+                backbone.set_default_language(language)
+        else:
+            model = models.ColBERT(
+                model_name_or_path=model_name_or_path,
+                document_length=document_length,
+                attend_to_expansion_tokens=(
+                    True if "attend" in model_name_or_path else False
+                ),
+            )
+
+        model.eval()
+        model.to("cuda")
+
+        dfDocs = pd.read_parquet(
+            f"datasets/{datasetname}/corpus/train-00000-of-00001.parquet"
+        ).dropna()
+        dfQueries = pd.read_parquet(
+            f"datasets/{datasetname}/queries/train-00000-of-00001.parquet"
+        ).dropna()
+
+        if "checkpoint" in model_name_or_path:
+            try:
+                model.tokenizer.model_input_names.remove("token_type_ids")
+            except:
+                print(model_name_or_path)
+            dfDocs.TurkishText = dfDocs.TurkishText.apply(
+                lambda x: x.replace("İ", "i").replace("I", "ı").lower()
+            )
+            dfQueries.TurkishText = dfQueries.TurkishText.apply(
+                lambda x: x.replace("İ", "i").replace("I", "ı").lower()
+            )
+
+        # Read test queries
+        queries = []
+        documents = []
+        passage_cand = {}
+        relevant_qid = []
+        relevant_docs = defaultdict(lambda: defaultdict(int))
+
+        # read corpus
+        newId2oldId_Docs = {}
+        for i, row in enumerate(dfDocs.values):
+            documents.append(row[2])
+            newId2oldId_Docs[i] = str(row[0])
+            relevant_qid.append(str(row[0]))
+
+        # read queries
+        newId2oldId_Queries = {}
+        for i, row in enumerate(dfQueries.values):
+            queries.append(row[2])
+            newId2oldId_Queries[i] = str(row[0])
+
+            for j, rowDoc in enumerate(dfDocs.values):
+                relevant_docs[str(row[0])][str(rowDoc[0])] = 0
+
+        # read qrels
+        dfQrels = pd.read_parquet(
+            f"datasets/{datasetname}/qrels/train-00000-of-00001.parquet"
+        )
+        for i, row in enumerate(dfQrels.values):
+            relevant_docs[str(row[0])][str(row[1])] = 1
+
+        candidateIds = [[i for i in range(len(documents))]]
+
+        queries_result_list = []
+        run = {}
+
+        documents_embeddings = model.encode(
+            [documents], is_query=False, show_progress_bar=True
+        )
+
+        for i, query in enumerate(tqdm.tqdm(queries)):
+
+            queries_embeddings = model.encode(
+                [query],
+                is_query=True,
+            )
+
+            reranked_documents = rank.rerank(
+                documents_ids=candidateIds,
+                queries_embeddings=queries_embeddings,
+                documents_embeddings=documents_embeddings,
+            )
+
+            run[newId2oldId_Queries[i]] = {}
+            for resDict in reranked_documents[0]:
+                run[newId2oldId_Queries[i]][newId2oldId_Docs[resDict["id"]]] = float(
+                    resDict["score"]
+                )
+
+        evaluator = pytrec_eval.RelevanceEvaluator(
+            relevant_docs, pytrec_eval.supported_measures
+        )
+        scores = evaluator.evaluate(run)
+
+        def print_line(measure, scope, value):
+            print("{:25s}{:8s}{:.4f}".format(measure, scope, value))
+
+        for query_id, query_measures in sorted(scores.items()):
+            break
+            for measure, value in sorted(query_measures.items()):
+                print_line(measure, query_id, value)
+
+        # Scope hack: use query_measures of last item in previous loop to
+        # figure out all unique measure names.
+        resultsColumns = ["model name"]
+        resultsRow = [model_name_or_path]
+        for measure in sorted(query_measures.keys()):
+            resultsColumns.append(measure)
+            resultsRow.append(
+                pytrec_eval.compute_aggregated_measure(
+                    measure,
+                    [query_measures[measure] for query_measures in scores.values()],
+                )
+            )
+
+        if evalResultsDf is None:
+            evalResultsDf = pd.DataFrame(columns=resultsColumns)
+        evalResultsDf.loc[-1] = resultsRow
+        evalResultsDf.index = evalResultsDf.index + 1
+
+    evalResultsDf.to_csv(f"resultsn/{datasetname}.csv", encoding="utf-8")

assets/evalPytrecBGEm3.py

@@ -0,0 +1,144 @@
+import os
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+os.environ["HF_HOME"] = "../../cache/hgCache"
+os.environ["TRANSFORMERS_CACHE"] = "../../cache/transformersCache/"
+
+import gzip
+import logging
+import sys
+from collections import defaultdict
+
+import numpy as np
+import pytrec_eval
+import tqdm
+import pandas as pd
+from pylate import models, rank
+from FlagEmbedding import BGEM3FlagModel
+
+
+datasetnames = [
+    "fiqa2018",
+    "climatefever",
+    "dbpedia",
+    "fever",
+    "fiqa2018",
+    "hotpotqa",
+    # "msmarco",
+    "nfcorpus",
+    "nq",
+    "quoraretrieval",
+    "scidocs",
+    "arguana",
+    "scifact",
+    "touche2020",
+]
+model = BGEM3FlagModel("BAAI/bge-m3", use_fp16=True)
+for datasetname in datasetnames:
+    evalResultsDf = None
+
+    dfDocs = pd.read_parquet(
+        f"datasets/{datasetname}/corpus/train-00000-of-00001.parquet"
+    ).dropna()
+    dfQueries = pd.read_parquet(
+        f"datasets/{datasetname}/queries/train-00000-of-00001.parquet"
+    ).dropna()
+
+    # Read test queries
+    queries = []
+    documents = []
+    passage_cand = {}
+    relevant_qid = []
+    relevant_docs = defaultdict(lambda: defaultdict(int))
+
+    # read corpus
+    newId2oldId_Docs = {}
+    for i, row in enumerate(dfDocs.values):
+        documents.append(row[2])
+        newId2oldId_Docs[i] = str(row[0])
+        relevant_qid.append(str(row[0]))
+
+    # read queries
+    newId2oldId_Queries = {}
+    for i, row in enumerate(dfQueries.values):
+        queries.append(row[2])
+        newId2oldId_Queries[i] = str(row[0])
+
+        for j, rowDoc in enumerate(dfDocs.values):
+            relevant_docs[str(row[0])][str(rowDoc[0])] = 0
+
+    # read qrels
+    dfQrels = pd.read_parquet(
+        f"datasets/{datasetname}/qrels/train-00000-of-00001.parquet"
+    )
+    for i, row in enumerate(dfQrels.values):
+        relevant_docs[str(row[0])][str(row[1])] = 1
+
+    candidateIds = [[i for i in range(len(documents))]]
+
+    queries_result_list = []
+    run = {}
+
+    document_embeddings = model.encode(
+        documents,
+        batch_size=4,
+        max_length=512,
+        return_dense=True,
+        return_sparse=True,
+        return_colbert_vecs=True,
+    )
+
+    for i, query in enumerate(tqdm.tqdm(queries)):
+
+        queries_embeddings = model.encode(
+            [query],
+            max_length=32,
+            return_dense=True,
+            return_sparse=True,
+            return_colbert_vecs=True,
+        )
+
+        similarities = []
+        for j in range(len(documents)):
+            similarities.append(
+                model.colbert_score(
+                    queries_embeddings["colbert_vecs"][0],
+                    document_embeddings["colbert_vecs"][j],
+                )
+            )
+
+        run[newId2oldId_Queries[i]] = {}
+        for j, score in enumerate(similarities):
+            run[newId2oldId_Queries[i]][newId2oldId_Docs[j]] = float(score)
+
+    evaluator = pytrec_eval.RelevanceEvaluator(
+        relevant_docs, pytrec_eval.supported_measures
+    )
+    scores = evaluator.evaluate(run)
+
+    def print_line(measure, scope, value):
+        print("{:25s}{:8s}{:.4f}".format(measure, scope, value))
+
+    for query_id, query_measures in sorted(scores.items()):
+        break
+        for measure, value in sorted(query_measures.items()):
+            print_line(measure, query_id, value)
+
+    # Scope hack: use query_measures of last item in previous loop to
+    # figure out all unique measure names.
+    resultsColumns = ["model name"]
+    resultsRow = ["bgem3"]
+    for measure in sorted(query_measures.keys()):
+        resultsColumns.append(measure)
+        resultsRow.append(
+            pytrec_eval.compute_aggregated_measure(
+                measure, [query_measures[measure] for query_measures in scores.values()]
+            )
+        )
+
+    if evalResultsDf is None:
+        evalResultsDf = pd.DataFrame(columns=resultsColumns)
+    evalResultsDf.loc[-1] = resultsRow
+    evalResultsDf.index = evalResultsDf.index + 1
+
+    evalResultsDf.to_csv(f"results/{datasetname}_bgem3.csv", encoding="utf-8")

assets/results/arguana.csv

@@ -0,0 +1,4 @@
+,model name,11pt_avg,G,P_10,P_100,P_1000,P_15,P_20,P_200,P_30,P_5,P_500,Rndcg,Rprec,Rprec_mult_0.20,Rprec_mult_0.40,Rprec_mult_0.60,Rprec_mult_0.80,Rprec_mult_1.00,Rprec_mult_1.20,Rprec_mult_1.40,Rprec_mult_1.60,Rprec_mult_1.80,Rprec_mult_2.00,binG,bpref,gm_bpref,gm_map,infAP,iprec_at_recall_0.00,iprec_at_recall_0.10,iprec_at_recall_0.20,iprec_at_recall_0.30,iprec_at_recall_0.40,iprec_at_recall_0.50,iprec_at_recall_0.60,iprec_at_recall_0.70,iprec_at_recall_0.80,iprec_at_recall_0.90,iprec_at_recall_1.00,map,map_cut_10,map_cut_100,map_cut_1000,map_cut_15,map_cut_20,map_cut_200,map_cut_30,map_cut_5,map_cut_500,ndcg,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,ndcg_cut_15,ndcg_cut_20,ndcg_cut_200,ndcg_cut_30,ndcg_cut_5,ndcg_cut_500,ndcg_rel,num_nonrel_judged_ret,num_q,num_rel,num_rel_ret,num_ret,recall_10,recall_100,recall_1000,recall_15,recall_20,recall_200,recall_30,recall_5,recall_500,recip_rank,relative_P_10,relative_P_100,relative_P_1000,relative_P_15,relative_P_20,relative_P_200,relative_P_30,relative_P_5,relative_P_500,relstring,runid,set_F,set_P,set_map,set_recall,set_relative_P,success_1,success_10,success_5,utility
+2,/home/esta/projects/eren/nlpSuite/colbert2/output/knowledge-distillation-3ep-webfaqcus-128-nonorm/checkpoint-11380,0.371506707078452,0.49910037420916464,0.07200000000000001,0.0088,0.001,0.05200000000000001,0.03900000000000001,0.004600000000000001,0.027333333333333334,0.128,0.00192,0.3595501871045824,0.22,0.22,0.22,0.22,0.22,0.22,0.14,0.14,0.14,0.14,0.14,0.49910037420916464,0.22,0.00012589254117941672,0.1546580389008592,0.3715082221303439,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.371506707078452,0.3631904761904762,0.371059326702709,0.371506707078452,0.36788045288045285,0.36788045288045285,0.3713331594326821,0.3697419247419247,0.3516666666666666,0.3714500692718142,0.49910037420916464,0.4490779100994905,0.4845981360200332,0.49910037420916464,0.464909759713257,0.464909759713257,0.4901501153711396,0.47381593078675865,0.42238165942214523,0.49487483324646675,0.49910037420916464,181700.0,50.0,50.0,50.0,181750.0,0.72,0.88,1.0,0.78,0.78,0.92,0.82,0.64,0.96,0.371506707078452,0.72,0.88,1.0,0.78,0.78,0.92,0.82,0.64,0.96,0.0,0.0,0.0005500550055005501,0.00027510316368638244,0.00027510316368638244,1.0,1.0,0.22,0.72,0.64,-3633.0
+1,jinaai/jina-colbert-v2,0.3861531879408564,0.5172976566130368,0.07200000000000001,0.009600000000000003,0.001,0.05200000000000001,0.040999999999999995,0.005,0.027333333333333334,0.132,0.002,0.3486488283065184,0.18,0.18,0.18,0.18,0.18,0.18,0.21,0.21,0.21,0.21,0.21,0.5172976566130368,0.18,7.943282347242815e-05,0.20596522916144652,0.38615524944273877,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.3861531879408564,0.37521428571428567,0.38578451651713913,0.3861531879408564,0.3801093906093906,0.38247050172050173,0.3861531879408564,0.38247050172050173,0.36699999999999994,0.3861531879408564,0.5172976566130368,0.46011753364467384,0.5113933933096584,0.5172976566130368,0.4762023739591841,0.48580356306888134,0.5172976566130368,0.48580356306888134,0.4404749027756259,0.5172976566130368,0.5172976566130368,181700.0,50.0,50.0,50.0,181750.0,0.72,0.96,1.0,0.78,0.82,1.0,0.82,0.66,1.0,0.3861531879408564,0.72,0.96,1.0,0.78,0.82,1.0,0.82,0.66,1.0,0.0,0.0,0.0005500550055005501,0.00027510316368638244,0.00027510316368638244,1.0,1.0,0.18,0.72,0.66,-3633.0
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assets/results/arguana_bgem3.csv

@@ -0,0 +1,2 @@
+,model name,11pt_avg,G,P_10,P_100,P_1000,P_15,P_20,P_200,P_30,P_5,P_500,Rndcg,Rprec,Rprec_mult_0.20,Rprec_mult_0.40,Rprec_mult_0.60,Rprec_mult_0.80,Rprec_mult_1.00,Rprec_mult_1.20,Rprec_mult_1.40,Rprec_mult_1.60,Rprec_mult_1.80,Rprec_mult_2.00,binG,bpref,gm_bpref,gm_map,infAP,iprec_at_recall_0.00,iprec_at_recall_0.10,iprec_at_recall_0.20,iprec_at_recall_0.30,iprec_at_recall_0.40,iprec_at_recall_0.50,iprec_at_recall_0.60,iprec_at_recall_0.70,iprec_at_recall_0.80,iprec_at_recall_0.90,iprec_at_recall_1.00,map,map_cut_10,map_cut_100,map_cut_1000,map_cut_15,map_cut_20,map_cut_200,map_cut_30,map_cut_5,map_cut_500,ndcg,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,ndcg_cut_15,ndcg_cut_20,ndcg_cut_200,ndcg_cut_30,ndcg_cut_5,ndcg_cut_500,ndcg_rel,num_nonrel_judged_ret,num_q,num_rel,num_rel_ret,num_ret,recall_10,recall_100,recall_1000,recall_15,recall_20,recall_200,recall_30,recall_5,recall_500,recip_rank,relative_P_10,relative_P_100,relative_P_1000,relative_P_15,relative_P_20,relative_P_200,relative_P_30,relative_P_5,relative_P_500,relstring,runid,set_F,set_P,set_map,set_recall,set_relative_P,success_1,success_10,success_5,utility
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assets/results/climatefever.csv

@@ -0,0 +1,4 @@
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assets/results/climatefever_bgem3.csv

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assets/results/dbpedia.csv

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assets/results/dbpedia_bgem3.csv

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assets/results/fever.csv

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assets/results/fever_bgem3.csv

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assets/results/fiqa2018.csv

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assets/results/fiqa2018_bgem3.csv

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assets/results/hotpotqa.csv

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assets/results/hotpotqa_bgem3.csv

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assets/results/nfcorpus.csv

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assets/results/nfcorpus_bgem3.csv

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assets/results/nq.csv

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assets/results/nq_bgem3.csv

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assets/results/quoraretrieval.csv

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assets/results/quoraretrieval_bgem3.csv

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assets/results/scidocs.csv

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assets/results/scidocs_bgem3.csv

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assets/results/scifact.csv

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assets/results/scifact_bgem3.csv

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assets/results/touche2020.csv

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assets/results/touche2020_bgem3.csv

@@ -0,0 +1,2 @@
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