dpr_scale/transforms/dpr_transform.py [69:87]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        for row in rows:
            row = ujson.loads(row)
            # also support DPR output format
            if "positive_ctxs" not in row and "ctxs" in row:
                row["positive_ctxs"] = []
                row["hard_negative_ctxs"] = []
                for ctx in row["ctxs"]:
                    if ctx["has_answer"]:
                        row["positive_ctxs"].append(ctx)
                    else:
                        row["hard_negative_ctxs"].append(ctx)
                if not row["positive_ctxs"]:
                    row["positive_ctxs"].append(row["ctxs"][0])

            # sample positive contexts
            contexts_pos = row["positive_ctxs"]

            # Handle case when context is a list of tokens instead of string.
            try:
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dpr_scale/transforms/dpr_transform.py [196:214]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        for row in rows:
            row = ujson.loads(row)
            # also support DPR output format
            if "positive_ctxs" not in row and "ctxs" in row:
                row["positive_ctxs"] = []
                row["hard_negative_ctxs"] = []
                for ctx in row["ctxs"]:
                    if ctx["has_answer"]:
                        row["positive_ctxs"].append(ctx)
                    else:
                        row["hard_negative_ctxs"].append(ctx)
                if not row["positive_ctxs"]:
                    row["positive_ctxs"].append(row["ctxs"][0])

            # sample positive contexts
            contexts_pos = row["positive_ctxs"]

            # Handle case when context is a list of tokens instead of string.
            try:
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