Upload batch 372 (20 files, last=construct_citations_dataset/all_field_pairs_433/psychology_val_pairs.jsonl)
Browse files- .gitattributes +1 -0
- construct_citations_dataset/all_field_pairs_433/art_test_pairs.jsonl +16 -0
- construct_citations_dataset/all_field_pairs_433/chemistry_test_pairs.jsonl +0 -0
- construct_citations_dataset/all_field_pairs_433/csrankings_test_pairs.jsonl +0 -0
- construct_citations_dataset/all_field_pairs_433/engineering_test_pairs.jsonl +0 -0
- construct_citations_dataset/all_field_pairs_433/environmental_science_val_pairs.jsonl +381 -0
- construct_citations_dataset/all_field_pairs_433/political_science_train_pairs.jsonl +129 -0
- construct_citations_dataset/all_field_pairs_433/psychology_train_pairs.jsonl +154 -0
- construct_citations_dataset/all_field_pairs_433/psychology_val_pairs.jsonl +94 -0
- construct_citations_dataset/all_field_pairs_433/sociology_train_pairs.jsonl +368 -0
- construct_citations_dataset/download_model.sh +65 -0
- construct_citations_dataset/gpt41mini_csrankings_test_results.json +0 -0
- construct_citations_dataset/llama32_all_fields_predictions.jsonl +3 -0
- construct_citations_dataset/llama32_all_fields_results.json +181 -0
- huggingface_dataset/Dataset_Card/Datatang_French_Speech_Data_by_Mobile_Phone.md +126 -0
- huggingface_dataset/Dataset_Card/Nerfgun3_john_kafka.md +45 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-kmfoda__booksum-kmfoda__booksum-e19ec2-2251271757.md +33 -0
- huggingface_dataset/Dataset_Card/sagawa_ord-uniq-canonicalized.md +34 -0
- huggingface_dataset/Dataset_Info/dataset_info.parquet +3 -0
- huggingface_dataset/Dataset_Info/datasetcard_info.parquet +3 -0
- huggingface_dataset/Dataset_Info/datasetcard_sections_info.parquet +3 -0
.gitattributes
CHANGED
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@@ -264,3 +264,4 @@ huggingface_dataset/datasetcard_pairs_no_limit/datasetcard_train_pairs.jsonl fil
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huggingface_dataset/datasetcard_pairs/datasetcard_val_pairs.jsonl filter=lfs diff=lfs merge=lfs -text
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huggingface_dataset/datasetcard_pairs/datasetcard_test_pairs.jsonl filter=lfs diff=lfs merge=lfs -text
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huggingface_dataset/datasetcard_pairs/datasetcard_train_pairs.jsonl filter=lfs diff=lfs merge=lfs -text
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huggingface_dataset/datasetcard_pairs/datasetcard_val_pairs.jsonl filter=lfs diff=lfs merge=lfs -text
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huggingface_dataset/datasetcard_pairs/datasetcard_test_pairs.jsonl filter=lfs diff=lfs merge=lfs -text
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huggingface_dataset/datasetcard_pairs/datasetcard_train_pairs.jsonl filter=lfs diff=lfs merge=lfs -text
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construct_citations_dataset/llama32_all_fields_predictions.jsonl filter=lfs diff=lfs merge=lfs -text
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construct_citations_dataset/all_field_pairs_433/art_test_pairs.jsonl
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{"paper_a_id": "2010980769", "paper_b_id": "2002355196", "citations_a": 13, "citations_b": 31, "ratio_class": "2x", "publication_month": "2001-01", "label": 1}
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{"paper_a_id": "1973140426", "paper_b_id": "2067304358", "citations_a": 10, "citations_b": 37, "ratio_class": "3x", "publication_month": "2002-01", "label": 1}
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{"paper_a_id": "1986164624", "paper_b_id": "2313645870", "citations_a": 24, "citations_b": 88, "ratio_class": "3x", "publication_month": "2003-01", "label": 1}
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{"paper_a_id": "2085833480", "paper_b_id": "2313645870", "citations_a": 27, "citations_b": 88, "ratio_class": "3x", "publication_month": "2003-01", "label": 1}
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{"paper_a_id": "2027855384", "paper_b_id": "1973824468", "citations_a": 25, "citations_b": 52, "ratio_class": "2x", "publication_month": "2004-01", "label": 1}
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{"paper_a_id": "2078752093", "paper_b_id": "2056003001", "citations_a": 26, "citations_b": 69, "ratio_class": "2x", "publication_month": "2006-01", "label": 1}
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{"paper_a_id": "2078752093", "paper_b_id": "2800171427", "citations_a": 26, "citations_b": 81, "ratio_class": "3x", "publication_month": "2006-01", "label": 1}
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{"paper_a_id": "1984640228", "paper_b_id": "2056003001", "citations_a": 27, "citations_b": 69, "ratio_class": "2x", "publication_month": "2006-01", "label": 1}
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{"paper_a_id": "1984640228", "paper_b_id": "2800171427", "citations_a": 27, "citations_b": 81, "ratio_class": "3x", "publication_month": "2006-01", "label": 1}
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{"paper_a_id": "2113905200", "paper_b_id": "2033271717", "citations_a": 25, "citations_b": 63, "ratio_class": "2x", "publication_month": "2007-01", "label": 1}
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{"paper_a_id": "2113905200", "paper_b_id": "955049519", "citations_a": 25, "citations_b": 97, "ratio_class": "3x", "publication_month": "2007-01", "label": 1}
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{"paper_a_id": "2038029644", "paper_b_id": "2057180907", "citations_a": 25, "citations_b": 60, "ratio_class": "2x", "publication_month": "2008-09", "label": 1}
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{"paper_a_id": "1982173729", "paper_b_id": "2285539985", "citations_a": 20, "citations_b": 81, "ratio_class": "4x", "publication_month": "2008-01", "label": 1}
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{"paper_a_id": "2327561258", "paper_b_id": "2890396613", "citations_a": 33, "citations_b": 137, "ratio_class": "4x", "publication_month": "2010-01", "label": 1}
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{"paper_a_id": "334016701", "paper_b_id": "287670492", "citations_a": 15, "citations_b": 35, "ratio_class": "2x", "publication_month": "2012-01", "label": 1}
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{"paper_a_id": "334016701", "paper_b_id": "2277281199", "citations_a": 15, "citations_b": 50, "ratio_class": "3x", "publication_month": "2012-01", "label": 1}
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construct_citations_dataset/all_field_pairs_433/chemistry_test_pairs.jsonl
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construct_citations_dataset/all_field_pairs_433/csrankings_test_pairs.jsonl
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The diff for this file is too large to render.
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construct_citations_dataset/all_field_pairs_433/engineering_test_pairs.jsonl
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The diff for this file is too large to render.
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construct_citations_dataset/all_field_pairs_433/environmental_science_val_pairs.jsonl
ADDED
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| 1 |
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{"paper_a_id": "2047128072", "paper_b_id": "2004473465", "citations_a": 19, "citations_b": 40, "ratio_class": "2x", "publication_month": "2001-01", "label": 1}
|
| 2 |
+
{"paper_a_id": "2040158293", "paper_b_id": "2004473465", "citations_a": 16, "citations_b": 40, "ratio_class": "2x", "publication_month": "2001-01", "label": 1}
|
| 3 |
+
{"paper_a_id": "1966881537", "paper_b_id": "2022396921", "citations_a": 42, "citations_b": 244, "ratio_class": "5x", "publication_month": "2001-07", "label": 1}
|
| 4 |
+
{"paper_a_id": "2036358351", "paper_b_id": "2022396921", "citations_a": 52, "citations_b": 244, "ratio_class": "4x", "publication_month": "2001-07", "label": 1}
|
| 5 |
+
{"paper_a_id": "2081864775", "paper_b_id": "2155151250", "citations_a": 45, "citations_b": 396, "ratio_class": "8x", "publication_month": "2001-08", "label": 1}
|
| 6 |
+
{"paper_a_id": "1989898841", "paper_b_id": "2327093633", "citations_a": 29, "citations_b": 138, "ratio_class": "4x", "publication_month": "2002-01", "label": 1}
|
| 7 |
+
{"paper_a_id": "1989898841", "paper_b_id": "1991724466", "citations_a": 29, "citations_b": 847, "ratio_class": "10x+", "publication_month": "2002-01", "label": 1}
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| 8 |
+
{"paper_a_id": "1977075512", "paper_b_id": "2327093633", "citations_a": 37, "citations_b": 138, "ratio_class": "3x", "publication_month": "2002-01", "label": 1}
|
| 9 |
+
{"paper_a_id": "1977075512", "paper_b_id": "1991724466", "citations_a": 37, "citations_b": 847, "ratio_class": "10x+", "publication_month": "2002-01", "label": 1}
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| 10 |
+
{"paper_a_id": "2143418269", "paper_b_id": "1989898841", "citations_a": 14, "citations_b": 29, "ratio_class": "2x", "publication_month": "2002-01", "label": 1}
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| 11 |
+
{"paper_a_id": "2143418269", "paper_b_id": "1977075512", "citations_a": 14, "citations_b": 37, "ratio_class": "2x", "publication_month": "2002-01", "label": 1}
|
| 12 |
+
{"paper_a_id": "2143418269", "paper_b_id": "2327093633", "citations_a": 14, "citations_b": 138, "ratio_class": "9x", "publication_month": "2002-01", "label": 1}
|
| 13 |
+
{"paper_a_id": "2143418269", "paper_b_id": "1991724466", "citations_a": 14, "citations_b": 847, "ratio_class": "10x+", "publication_month": "2002-01", "label": 1}
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| 14 |
+
{"paper_a_id": "2327093633", "paper_b_id": "1991724466", "citations_a": 138, "citations_b": 847, "ratio_class": "6x", "publication_month": "2002-01", "label": 1}
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| 15 |
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{"paper_a_id": "2085958409", "paper_b_id": "2327093633", "citations_a": 27, "citations_b": 138, "ratio_class": "5x", "publication_month": "2002-01", "label": 1}
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| 16 |
+
{"paper_a_id": "2085958409", "paper_b_id": "1991724466", "citations_a": 27, "citations_b": 847, "ratio_class": "10x+", "publication_month": "2002-01", "label": 1}
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| 17 |
+
{"paper_a_id": "2149471770", "paper_b_id": "2327093633", "citations_a": 21, "citations_b": 138, "ratio_class": "6x", "publication_month": "2002-01", "label": 1}
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| 18 |
+
{"paper_a_id": "2149471770", "paper_b_id": "1991724466", "citations_a": 21, "citations_b": 847, "ratio_class": "10x+", "publication_month": "2002-01", "label": 1}
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| 19 |
+
{"paper_a_id": "1975855152", "paper_b_id": "1977859942", "citations_a": 15, "citations_b": 130, "ratio_class": "8x", "publication_month": "2002-07", "label": 1}
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| 20 |
+
{"paper_a_id": "1541147584", "paper_b_id": "1977859942", "citations_a": 18, "citations_b": 130, "ratio_class": "7x", "publication_month": "2002-07", "label": 1}
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| 21 |
+
{"paper_a_id": "2021216550", "paper_b_id": "2107231972", "citations_a": 42, "citations_b": 157, "ratio_class": "3x", "publication_month": "2002-10", "label": 1}
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| 22 |
+
{"paper_a_id": "2024004294", "paper_b_id": "2107231972", "citations_a": 69, "citations_b": 157, "ratio_class": "2x", "publication_month": "2002-10", "label": 1}
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| 23 |
+
{"paper_a_id": "2134575817", "paper_b_id": "2044553955", "citations_a": 57, "citations_b": 152, "ratio_class": "2x", "publication_month": "2002-08", "label": 1}
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| 24 |
+
{"paper_a_id": "2134575817", "paper_b_id": "1998265162", "citations_a": 57, "citations_b": 177, "ratio_class": "3x", "publication_month": "2002-08", "label": 1}
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| 25 |
+
{"paper_a_id": "2111716441", "paper_b_id": "2085143289", "citations_a": 25, "citations_b": 187, "ratio_class": "7x", "publication_month": "2002-04", "label": 1}
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construct_citations_dataset/all_field_pairs_433/political_science_train_pairs.jsonl
ADDED
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construct_citations_dataset/all_field_pairs_433/psychology_train_pairs.jsonl
ADDED
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construct_citations_dataset/all_field_pairs_433/psychology_val_pairs.jsonl
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construct_citations_dataset/all_field_pairs_433/sociology_train_pairs.jsonl
ADDED
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| 1 |
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{"paper_a_id": "2100048250", "paper_b_id": "2079419150", "citations_a": 44, "citations_b": 756, "ratio_class": "10x+", "publication_month": "2001-01", "label": 1}
|
| 2 |
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|
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| 4 |
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|
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|
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{"paper_a_id": "2615045510", "paper_b_id": "2610438881", "citations_a": 16, "citations_b": 126, "ratio_class": "7x", "publication_month": "2017-05", "label": 1}
|
| 322 |
+
{"paper_a_id": "2742863779", "paper_b_id": "2619043042", "citations_a": 54, "citations_b": 111, "ratio_class": "2x", "publication_month": "2017-08", "label": 1}
|
| 323 |
+
{"paper_a_id": "2742863779", "paper_b_id": "2602818514", "citations_a": 54, "citations_b": 120, "ratio_class": "2x", "publication_month": "2017-08", "label": 1}
|
| 324 |
+
{"paper_a_id": "2743449812", "paper_b_id": "2619043042", "citations_a": 36, "citations_b": 111, "ratio_class": "3x", "publication_month": "2017-08", "label": 1}
|
| 325 |
+
{"paper_a_id": "2743449812", "paper_b_id": "2602818514", "citations_a": 36, "citations_b": 120, "ratio_class": "3x", "publication_month": "2017-08", "label": 1}
|
| 326 |
+
{"paper_a_id": "2601459735", "paper_b_id": "2591962304", "citations_a": 13, "citations_b": 28, "ratio_class": "2x", "publication_month": "2017-03", "label": 1}
|
| 327 |
+
{"paper_a_id": "2601459735", "paper_b_id": "2593815433", "citations_a": 13, "citations_b": 51, "ratio_class": "3x", "publication_month": "2017-03", "label": 1}
|
| 328 |
+
{"paper_a_id": "2596007455", "paper_b_id": "2593815433", "citations_a": 20, "citations_b": 51, "ratio_class": "2x", "publication_month": "2017-03", "label": 1}
|
| 329 |
+
{"paper_a_id": "2741001193", "paper_b_id": "2740604028", "citations_a": 17, "citations_b": 35, "ratio_class": "2x", "publication_month": "2017-07", "label": 1}
|
| 330 |
+
{"paper_a_id": "2754887487", "paper_b_id": "2755534198", "citations_a": 15, "citations_b": 136, "ratio_class": "9x", "publication_month": "2017-09", "label": 1}
|
| 331 |
+
{"paper_a_id": "2752966887", "paper_b_id": "2755534198", "citations_a": 29, "citations_b": 136, "ratio_class": "4x", "publication_month": "2017-09", "label": 1}
|
| 332 |
+
{"paper_a_id": "2793120500", "paper_b_id": "2808120728", "citations_a": 42, "citations_b": 88, "ratio_class": "2x", "publication_month": "2018-06", "label": 1}
|
| 333 |
+
{"paper_a_id": "2901069505", "paper_b_id": "2903088423", "citations_a": 16, "citations_b": 52, "ratio_class": "3x", "publication_month": "2018-11", "label": 1}
|
| 334 |
+
{"paper_a_id": "2901069505", "paper_b_id": "2915934426", "citations_a": 16, "citations_b": 65, "ratio_class": "4x", "publication_month": "2018-11", "label": 1}
|
| 335 |
+
{"paper_a_id": "2803037105", "paper_b_id": "2810043760", "citations_a": 14, "citations_b": 46, "ratio_class": "3x", "publication_month": "2018-05", "label": 1}
|
| 336 |
+
{"paper_a_id": "2790514066", "paper_b_id": "2793514975", "citations_a": 16, "citations_b": 40, "ratio_class": "2x", "publication_month": "2018-02", "label": 1}
|
| 337 |
+
{"paper_a_id": "2801553437", "paper_b_id": "2797808524", "citations_a": 24, "citations_b": 63, "ratio_class": "2x", "publication_month": "2018-04", "label": 1}
|
| 338 |
+
{"paper_a_id": "2802415765", "paper_b_id": "2797808524", "citations_a": 31, "citations_b": 63, "ratio_class": "2x", "publication_month": "2018-04", "label": 1}
|
| 339 |
+
{"paper_a_id": "2839347170", "paper_b_id": "2811068284", "citations_a": 19, "citations_b": 44, "ratio_class": "2x", "publication_month": "2018-07", "label": 1}
|
| 340 |
+
{"paper_a_id": "2884048454", "paper_b_id": "2811068284", "citations_a": 14, "citations_b": 44, "ratio_class": "3x", "publication_month": "2018-07", "label": 1}
|
| 341 |
+
{"paper_a_id": "2888855518", "paper_b_id": "2893813986", "citations_a": 18, "citations_b": 61, "ratio_class": "3x", "publication_month": "2018-09", "label": 1}
|
| 342 |
+
{"paper_a_id": "2898043809", "paper_b_id": "2894569289", "citations_a": 11, "citations_b": 82, "ratio_class": "7x", "publication_month": "2018-10", "label": 1}
|
| 343 |
+
{"paper_a_id": "2949066102", "paper_b_id": "3101263994", "citations_a": 11, "citations_b": 33, "ratio_class": "3x", "publication_month": "2019-05", "label": 1}
|
| 344 |
+
{"paper_a_id": "2949066102", "paper_b_id": "2944820760", "citations_a": 11, "citations_b": 59, "ratio_class": "5x", "publication_month": "2019-05", "label": 1}
|
| 345 |
+
{"paper_a_id": "2949066102", "paper_b_id": "2942530530", "citations_a": 11, "citations_b": 90, "ratio_class": "8x", "publication_month": "2019-05", "label": 1}
|
| 346 |
+
{"paper_a_id": "2949066102", "paper_b_id": "2944943126", "citations_a": 11, "citations_b": 120, "ratio_class": "10x+", "publication_month": "2019-05", "label": 1}
|
| 347 |
+
{"paper_a_id": "2944820760", "paper_b_id": "2944943126", "citations_a": 59, "citations_b": 120, "ratio_class": "2x", "publication_month": "2019-05", "label": 1}
|
| 348 |
+
{"paper_a_id": "3101263994", "paper_b_id": "2942530530", "citations_a": 33, "citations_b": 90, "ratio_class": "2x", "publication_month": "2019-05", "label": 1}
|
| 349 |
+
{"paper_a_id": "3101263994", "paper_b_id": "2944943126", "citations_a": 33, "citations_b": 120, "ratio_class": "3x", "publication_month": "2019-05", "label": 1}
|
| 350 |
+
{"paper_a_id": "2910483174", "paper_b_id": "2910974378", "citations_a": 11, "citations_b": 23, "ratio_class": "2x", "publication_month": "2019-01", "label": 1}
|
| 351 |
+
{"paper_a_id": "2910483174", "paper_b_id": "2910759611", "citations_a": 11, "citations_b": 132, "ratio_class": "10x+", "publication_month": "2019-01", "label": 1}
|
| 352 |
+
{"paper_a_id": "2908068546", "paper_b_id": "2910974378", "citations_a": 11, "citations_b": 23, "ratio_class": "2x", "publication_month": "2019-01", "label": 1}
|
| 353 |
+
{"paper_a_id": "2908068546", "paper_b_id": "2910759611", "citations_a": 11, "citations_b": 132, "ratio_class": "10x+", "publication_month": "2019-01", "label": 1}
|
| 354 |
+
{"paper_a_id": "2910974378", "paper_b_id": "2910759611", "citations_a": 23, "citations_b": 132, "ratio_class": "5x", "publication_month": "2019-01", "label": 1}
|
| 355 |
+
{"paper_a_id": "2921661394", "paper_b_id": "2928108764", "citations_a": 14, "citations_b": 67, "ratio_class": "4x", "publication_month": "2019-03", "label": 1}
|
| 356 |
+
{"paper_a_id": "2921661394", "paper_b_id": "2922137011", "citations_a": 14, "citations_b": 143, "ratio_class": "10x+", "publication_month": "2019-03", "label": 1}
|
| 357 |
+
{"paper_a_id": "2921661394", "paper_b_id": "2922340600", "citations_a": 14, "citations_b": 226, "ratio_class": "10x+", "publication_month": "2019-03", "label": 1}
|
| 358 |
+
{"paper_a_id": "2928108764", "paper_b_id": "2922137011", "citations_a": 67, "citations_b": 143, "ratio_class": "2x", "publication_month": "2019-03", "label": 1}
|
| 359 |
+
{"paper_a_id": "2928108764", "paper_b_id": "2922340600", "citations_a": 67, "citations_b": 226, "ratio_class": "3x", "publication_month": "2019-03", "label": 1}
|
| 360 |
+
{"paper_a_id": "2940670894", "paper_b_id": "2937565550", "citations_a": 34, "citations_b": 78, "ratio_class": "2x", "publication_month": "2019-04", "label": 1}
|
| 361 |
+
{"paper_a_id": "2982173614", "paper_b_id": "2976615689", "citations_a": 11, "citations_b": 24, "ratio_class": "2x", "publication_month": "2019-09", "label": 1}
|
| 362 |
+
{"paper_a_id": "2982173614", "paper_b_id": "2972332861", "citations_a": 11, "citations_b": 29, "ratio_class": "2x", "publication_month": "2019-09", "label": 1}
|
| 363 |
+
{"paper_a_id": "2990667517", "paper_b_id": "2983436700", "citations_a": 14, "citations_b": 34, "ratio_class": "2x", "publication_month": "2019-11", "label": 1}
|
| 364 |
+
{"paper_a_id": "3010851780", "paper_b_id": "3012415378", "citations_a": 30, "citations_b": 92, "ratio_class": "3x", "publication_month": "2020-03", "label": 1}
|
| 365 |
+
{"paper_a_id": "3000078080", "paper_b_id": "3000358911", "citations_a": 25, "citations_b": 71, "ratio_class": "2x", "publication_month": "2020-01", "label": 1}
|
| 366 |
+
{"paper_a_id": "3005761012", "paper_b_id": "3006520000", "citations_a": 12, "citations_b": 67, "ratio_class": "5x", "publication_month": "2020-02", "label": 1}
|
| 367 |
+
{"paper_a_id": "3005882855", "paper_b_id": "3006520000", "citations_a": 12, "citations_b": 67, "ratio_class": "5x", "publication_month": "2020-02", "label": 1}
|
| 368 |
+
{"paper_a_id": "3046195942", "paper_b_id": "3027039470", "citations_a": 24, "citations_b": 138, "ratio_class": "5x", "publication_month": "2020-07", "label": 1}
|
construct_citations_dataset/download_model.sh
ADDED
|
@@ -0,0 +1,65 @@
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|
|
|
| 1 |
+
#!/bin/bash
|
| 2 |
+
# Script to pre-download the Llama model on a login node (which has internet access)
|
| 3 |
+
# Run this BEFORE submitting the Slurm job
|
| 4 |
+
|
| 5 |
+
set -euo pipefail
|
| 6 |
+
|
| 7 |
+
MODEL_NAME="${1:-meta-llama/Llama-3.2-3B-Instruct}"
|
| 8 |
+
HF_HOME="${HF_HOME:-/scratch/user/hangxiao/huggingface_cache}"
|
| 9 |
+
|
| 10 |
+
echo "Downloading model: ${MODEL_NAME}"
|
| 11 |
+
echo "Cache directory: ${HF_HOME}"
|
| 12 |
+
|
| 13 |
+
export HF_HOME
|
| 14 |
+
export TRANSFORMERS_CACHE="${HF_HOME}"
|
| 15 |
+
export HF_TOKEN="${HF_TOKEN:-}"
|
| 16 |
+
|
| 17 |
+
mkdir -p "${HF_HOME}"
|
| 18 |
+
|
| 19 |
+
source /sw/eb/sw/Anaconda3/2024.02-1/etc/profile.d/conda.sh
|
| 20 |
+
conda activate /scratch/user/hangxiao/.conda/envs/paper_impact
|
| 21 |
+
|
| 22 |
+
# Use huggingface-cli to download the model (simpler and faster)
|
| 23 |
+
if command -v huggingface-cli &> /dev/null; then
|
| 24 |
+
echo "Using huggingface-cli to download model..."
|
| 25 |
+
huggingface-cli download "${MODEL_NAME}" \
|
| 26 |
+
--cache-dir "${HF_HOME}" \
|
| 27 |
+
--token "${HF_TOKEN:-}" \
|
| 28 |
+
--local-dir-use-symlinks False
|
| 29 |
+
else
|
| 30 |
+
# Fallback: Use Python to download via transformers
|
| 31 |
+
echo "huggingface-cli not found, using transformers library..."
|
| 32 |
+
python <<EOF
|
| 33 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 34 |
+
import os
|
| 35 |
+
|
| 36 |
+
model_name = "${MODEL_NAME}"
|
| 37 |
+
cache_dir = "${HF_HOME}"
|
| 38 |
+
|
| 39 |
+
print(f"Downloading tokenizer for {model_name}...")
|
| 40 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
| 41 |
+
model_name,
|
| 42 |
+
cache_dir=cache_dir,
|
| 43 |
+
token=os.getenv("HF_TOKEN")
|
| 44 |
+
)
|
| 45 |
+
|
| 46 |
+
print(f"Downloading model config and files for {model_name}...")
|
| 47 |
+
# Just download config and tokenizer files - vLLM will handle model weights
|
| 48 |
+
# This ensures all metadata is cached
|
| 49 |
+
try:
|
| 50 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 51 |
+
model_name,
|
| 52 |
+
cache_dir=cache_dir,
|
| 53 |
+
token=os.getenv("HF_TOKEN"),
|
| 54 |
+
torch_dtype="auto"
|
| 55 |
+
)
|
| 56 |
+
except Exception as e:
|
| 57 |
+
print(f"Note: Full model download had issue: {e}")
|
| 58 |
+
print("This is okay - vLLM will download weights as needed")
|
| 59 |
+
|
| 60 |
+
print(f"Model files downloaded successfully to {cache_dir}")
|
| 61 |
+
EOF
|
| 62 |
+
fi
|
| 63 |
+
|
| 64 |
+
echo "Model download complete!"
|
| 65 |
+
|
construct_citations_dataset/gpt41mini_csrankings_test_results.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
construct_citations_dataset/llama32_all_fields_predictions.jsonl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:970a2dd4b397cc331db341ef409e6722f3df99262263efe98b7bec374618cb52
|
| 3 |
+
size 20049284
|
construct_citations_dataset/llama32_all_fields_results.json
ADDED
|
@@ -0,0 +1,181 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"summary": {
|
| 3 |
+
"overall_accuracy": 0.4567747602664519,
|
| 4 |
+
"other_fields_avg": 0.4398884239888424,
|
| 5 |
+
"total_pairs": 13661,
|
| 6 |
+
"evaluated_pairs": 13661,
|
| 7 |
+
"skipped_pairs": 0,
|
| 8 |
+
"elapsed_seconds": 2470.672971010208,
|
| 9 |
+
"model_name": "meta-llama/Llama-3.2-3B-Instruct",
|
| 10 |
+
"api_base": "http://127.0.0.1:8000/v1",
|
| 11 |
+
"backend_mode": "local_vllm",
|
| 12 |
+
"large_fields_excluded": [
|
| 13 |
+
"chemistry",
|
| 14 |
+
"csrankings",
|
| 15 |
+
"medicine",
|
| 16 |
+
"physics"
|
| 17 |
+
]
|
| 18 |
+
},
|
| 19 |
+
"per_field": {
|
| 20 |
+
"art": {
|
| 21 |
+
"pairs_count": 10,
|
| 22 |
+
"evaluated_count": 10,
|
| 23 |
+
"skipped_count": 0,
|
| 24 |
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"correct_predictions": 4,
|
| 25 |
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|
| 26 |
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|
| 27 |
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},
|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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},
|
| 36 |
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"business": {
|
| 37 |
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"pairs_count": 300,
|
| 38 |
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"evaluated_count": 300,
|
| 39 |
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"skipped_count": 0,
|
| 40 |
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"correct_predictions": 139,
|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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"chemistry": {
|
| 45 |
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"pairs_count": 3000,
|
| 46 |
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"evaluated_count": 3000,
|
| 47 |
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|
| 48 |
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"correct_predictions": 1373,
|
| 49 |
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"accuracy": 0.45766666666666667,
|
| 50 |
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|
| 51 |
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},
|
| 52 |
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"computer_science": {
|
| 53 |
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"pairs_count": 300,
|
| 54 |
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"evaluated_count": 300,
|
| 55 |
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"skipped_count": 0,
|
| 56 |
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"correct_predictions": 137,
|
| 57 |
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"accuracy": 0.45666666666666667,
|
| 58 |
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|
| 59 |
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},
|
| 60 |
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"csrankings": {
|
| 61 |
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"pairs_count": 3000,
|
| 62 |
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|
| 63 |
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"skipped_count": 0,
|
| 64 |
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"correct_predictions": 1427,
|
| 65 |
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"accuracy": 0.4756666666666667,
|
| 66 |
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|
| 67 |
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},
|
| 68 |
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"economics": {
|
| 69 |
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"pairs_count": 300,
|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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},
|
| 76 |
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"engineering": {
|
| 77 |
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"pairs_count": 300,
|
| 78 |
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"evaluated_count": 300,
|
| 79 |
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"skipped_count": 0,
|
| 80 |
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"correct_predictions": 126,
|
| 81 |
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"accuracy": 0.42,
|
| 82 |
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"model_invocations": 300
|
| 83 |
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},
|
| 84 |
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"environmental_science": {
|
| 85 |
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"pairs_count": 300,
|
| 86 |
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"evaluated_count": 300,
|
| 87 |
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"skipped_count": 0,
|
| 88 |
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"correct_predictions": 127,
|
| 89 |
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"accuracy": 0.42333333333333334,
|
| 90 |
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"model_invocations": 300
|
| 91 |
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},
|
| 92 |
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"geography": {
|
| 93 |
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"pairs_count": 300,
|
| 94 |
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"evaluated_count": 300,
|
| 95 |
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"skipped_count": 0,
|
| 96 |
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"correct_predictions": 129,
|
| 97 |
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"accuracy": 0.43,
|
| 98 |
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"model_invocations": 300
|
| 99 |
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},
|
| 100 |
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"geology": {
|
| 101 |
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"pairs_count": 300,
|
| 102 |
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"evaluated_count": 300,
|
| 103 |
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"skipped_count": 0,
|
| 104 |
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"correct_predictions": 129,
|
| 105 |
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"accuracy": 0.43,
|
| 106 |
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"model_invocations": 300
|
| 107 |
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},
|
| 108 |
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"history": {
|
| 109 |
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"pairs_count": 6,
|
| 110 |
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"evaluated_count": 6,
|
| 111 |
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"skipped_count": 0,
|
| 112 |
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"correct_predictions": 1,
|
| 113 |
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"accuracy": 0.16666666666666666,
|
| 114 |
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"model_invocations": 6
|
| 115 |
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},
|
| 116 |
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"materials_science": {
|
| 117 |
+
"pairs_count": 300,
|
| 118 |
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"evaluated_count": 300,
|
| 119 |
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"skipped_count": 0,
|
| 120 |
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"correct_predictions": 134,
|
| 121 |
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"accuracy": 0.44666666666666666,
|
| 122 |
+
"model_invocations": 300
|
| 123 |
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},
|
| 124 |
+
"mathematics": {
|
| 125 |
+
"pairs_count": 289,
|
| 126 |
+
"evaluated_count": 289,
|
| 127 |
+
"skipped_count": 0,
|
| 128 |
+
"correct_predictions": 128,
|
| 129 |
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"accuracy": 0.4429065743944637,
|
| 130 |
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"model_invocations": 289
|
| 131 |
+
},
|
| 132 |
+
"medicine": {
|
| 133 |
+
"pairs_count": 1076,
|
| 134 |
+
"evaluated_count": 1076,
|
| 135 |
+
"skipped_count": 0,
|
| 136 |
+
"correct_predictions": 485,
|
| 137 |
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"accuracy": 0.4507434944237918,
|
| 138 |
+
"model_invocations": 1076
|
| 139 |
+
},
|
| 140 |
+
"philosophy": {
|
| 141 |
+
"pairs_count": 64,
|
| 142 |
+
"evaluated_count": 64,
|
| 143 |
+
"skipped_count": 0,
|
| 144 |
+
"correct_predictions": 29,
|
| 145 |
+
"accuracy": 0.453125,
|
| 146 |
+
"model_invocations": 64
|
| 147 |
+
},
|
| 148 |
+
"physics": {
|
| 149 |
+
"pairs_count": 3000,
|
| 150 |
+
"evaluated_count": 3000,
|
| 151 |
+
"skipped_count": 0,
|
| 152 |
+
"correct_predictions": 1378,
|
| 153 |
+
"accuracy": 0.4593333333333333,
|
| 154 |
+
"model_invocations": 3000
|
| 155 |
+
},
|
| 156 |
+
"political_science": {
|
| 157 |
+
"pairs_count": 109,
|
| 158 |
+
"evaluated_count": 109,
|
| 159 |
+
"skipped_count": 0,
|
| 160 |
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"correct_predictions": 46,
|
| 161 |
+
"accuracy": 0.42201834862385323,
|
| 162 |
+
"model_invocations": 109
|
| 163 |
+
},
|
| 164 |
+
"psychology": {
|
| 165 |
+
"pairs_count": 173,
|
| 166 |
+
"evaluated_count": 173,
|
| 167 |
+
"skipped_count": 0,
|
| 168 |
+
"correct_predictions": 71,
|
| 169 |
+
"accuracy": 0.41040462427745666,
|
| 170 |
+
"model_invocations": 173
|
| 171 |
+
},
|
| 172 |
+
"sociology": {
|
| 173 |
+
"pairs_count": 300,
|
| 174 |
+
"evaluated_count": 300,
|
| 175 |
+
"skipped_count": 0,
|
| 176 |
+
"correct_predictions": 140,
|
| 177 |
+
"accuracy": 0.4666666666666667,
|
| 178 |
+
"model_invocations": 300
|
| 179 |
+
}
|
| 180 |
+
}
|
| 181 |
+
}
|
huggingface_dataset/Dataset_Card/Datatang_French_Speech_Data_by_Mobile_Phone.md
ADDED
|
@@ -0,0 +1,126 @@
|
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|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
YAML tags:
|
| 3 |
+
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
|
| 4 |
+
---
|
| 5 |
+
|
| 6 |
+
# Dataset Card for Datatang/French_Speech_Data_by_Mobile_Phone
|
| 7 |
+
|
| 8 |
+
## Table of Contents
|
| 9 |
+
- [Table of Contents](#table-of-contents)
|
| 10 |
+
- [Dataset Description](#dataset-description)
|
| 11 |
+
- [Dataset Summary](#dataset-summary)
|
| 12 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 13 |
+
- [Languages](#languages)
|
| 14 |
+
- [Dataset Structure](#dataset-structure)
|
| 15 |
+
- [Data Instances](#data-instances)
|
| 16 |
+
- [Data Fields](#data-fields)
|
| 17 |
+
- [Data Splits](#data-splits)
|
| 18 |
+
- [Dataset Creation](#dataset-creation)
|
| 19 |
+
- [Curation Rationale](#curation-rationale)
|
| 20 |
+
- [Source Data](#source-data)
|
| 21 |
+
- [Annotations](#annotations)
|
| 22 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 23 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 24 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 25 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 26 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 27 |
+
- [Additional Information](#additional-information)
|
| 28 |
+
- [Dataset Curators](#dataset-curators)
|
| 29 |
+
- [Licensing Information](#licensing-information)
|
| 30 |
+
- [Citation Information](#citation-information)
|
| 31 |
+
- [Contributions](#contributions)
|
| 32 |
+
|
| 33 |
+
## Dataset Description
|
| 34 |
+
|
| 35 |
+
- **Homepage:** https://bit.ly/3y9aVyS
|
| 36 |
+
- **Repository:**
|
| 37 |
+
- **Paper:**
|
| 38 |
+
- **Leaderboard:**
|
| 39 |
+
- **Point of Contact:**
|
| 40 |
+
|
| 41 |
+
### Dataset Summary
|
| 42 |
+
|
| 43 |
+
The data volumn is 769 hours and is recorded by 1623 French native speakers. The recording text is designed by linguistic experts, which covers general interactive, in-car and home category. The texts are manually proofread with high accuracy. Recording devices are mainstream Android phones and iPhones.
|
| 44 |
+
|
| 45 |
+
For more details, please refer to the link: https://bit.ly/3y9aVyS
|
| 46 |
+
|
| 47 |
+
### Supported Tasks and Leaderboards
|
| 48 |
+
|
| 49 |
+
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
|
| 50 |
+
|
| 51 |
+
### Languages
|
| 52 |
+
|
| 53 |
+
French
|
| 54 |
+
## Dataset Structure
|
| 55 |
+
|
| 56 |
+
### Data Instances
|
| 57 |
+
|
| 58 |
+
[More Information Needed]
|
| 59 |
+
|
| 60 |
+
### Data Fields
|
| 61 |
+
|
| 62 |
+
[More Information Needed]
|
| 63 |
+
|
| 64 |
+
### Data Splits
|
| 65 |
+
|
| 66 |
+
[More Information Needed]
|
| 67 |
+
|
| 68 |
+
## Dataset Creation
|
| 69 |
+
|
| 70 |
+
### Curation Rationale
|
| 71 |
+
|
| 72 |
+
[More Information Needed]
|
| 73 |
+
|
| 74 |
+
### Source Data
|
| 75 |
+
|
| 76 |
+
#### Initial Data Collection and Normalization
|
| 77 |
+
|
| 78 |
+
[More Information Needed]
|
| 79 |
+
|
| 80 |
+
#### Who are the source language producers?
|
| 81 |
+
|
| 82 |
+
[More Information Needed]
|
| 83 |
+
|
| 84 |
+
### Annotations
|
| 85 |
+
|
| 86 |
+
#### Annotation process
|
| 87 |
+
|
| 88 |
+
[More Information Needed]
|
| 89 |
+
|
| 90 |
+
#### Who are the annotators?
|
| 91 |
+
|
| 92 |
+
[More Information Needed]
|
| 93 |
+
|
| 94 |
+
### Personal and Sensitive Information
|
| 95 |
+
|
| 96 |
+
[More Information Needed]
|
| 97 |
+
|
| 98 |
+
## Considerations for Using the Data
|
| 99 |
+
|
| 100 |
+
### Social Impact of Dataset
|
| 101 |
+
|
| 102 |
+
[More Information Needed]
|
| 103 |
+
|
| 104 |
+
### Discussion of Biases
|
| 105 |
+
|
| 106 |
+
[More Information Needed]
|
| 107 |
+
|
| 108 |
+
### Other Known Limitations
|
| 109 |
+
|
| 110 |
+
[More Information Needed]
|
| 111 |
+
|
| 112 |
+
## Additional Information
|
| 113 |
+
|
| 114 |
+
### Dataset Curators
|
| 115 |
+
|
| 116 |
+
[More Information Needed]
|
| 117 |
+
|
| 118 |
+
### Licensing Information
|
| 119 |
+
|
| 120 |
+
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
|
| 121 |
+
|
| 122 |
+
### Citation Information
|
| 123 |
+
|
| 124 |
+
[More Information Needed]
|
| 125 |
+
|
| 126 |
+
### Contributions
|
huggingface_dataset/Dataset_Card/Nerfgun3_john_kafka.md
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- stable-diffusion
|
| 6 |
+
- text-to-image
|
| 7 |
+
license: creativeml-openrail-m
|
| 8 |
+
inference: false
|
| 9 |
+
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
# John Kafka Artist Embedding / Textual Inversion
|
| 13 |
+
|
| 14 |
+
## Usage
|
| 15 |
+
To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
|
| 16 |
+
|
| 17 |
+
To use it in a prompt: ```"drawn by john_kafka"```
|
| 18 |
+
|
| 19 |
+
If it is to strong just add [] around it.
|
| 20 |
+
|
| 21 |
+
Trained until 6000 steps
|
| 22 |
+
|
| 23 |
+
Have fun :)
|
| 24 |
+
|
| 25 |
+
## Example Pictures
|
| 26 |
+
|
| 27 |
+
<table>
|
| 28 |
+
<tr>
|
| 29 |
+
<td><img src=https://i.imgur.com/aCnC1zv.png width=100% height=100%/></td>
|
| 30 |
+
<td><img src=https://i.imgur.com/FdBuWbG.png width=100% height=100%/></td>
|
| 31 |
+
<td><img src=https://i.imgur.com/1rkuXkZ.png width=100% height=100%/></td>
|
| 32 |
+
<td><img src=https://i.imgur.com/5N9Wp7q.png width=100% height=100%/></td>
|
| 33 |
+
<td><img src=https://i.imgur.com/v2AkXjU.png width=100% height=100%/></td>
|
| 34 |
+
</tr>
|
| 35 |
+
</table>
|
| 36 |
+
|
| 37 |
+
## License
|
| 38 |
+
|
| 39 |
+
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
|
| 40 |
+
The CreativeML OpenRAIL License specifies:
|
| 41 |
+
|
| 42 |
+
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
|
| 43 |
+
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
|
| 44 |
+
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
|
| 45 |
+
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-kmfoda__booksum-kmfoda__booksum-e19ec2-2251271757.md
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| 1 |
+
---
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| 2 |
+
type: predictions
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| 3 |
+
tags:
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| 4 |
+
- autotrain
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| 5 |
+
- evaluation
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| 6 |
+
datasets:
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| 7 |
+
- kmfoda/booksum
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| 8 |
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eval_info:
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| 9 |
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task: summarization
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| 10 |
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model: pszemraj/tglobal-XL-booksum-WIP3r3-sharded
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| 11 |
+
metrics: ['bertscore']
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| 12 |
+
dataset_name: kmfoda/booksum
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| 13 |
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dataset_config: kmfoda--booksum
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| 14 |
+
dataset_split: test
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| 15 |
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col_mapping:
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| 16 |
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text: chapter
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| 17 |
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target: summary_text
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| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
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| 20 |
+
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| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
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| 22 |
+
|
| 23 |
+
* Task: Summarization
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| 24 |
+
* Model: pszemraj/tglobal-XL-booksum-WIP3r3-sharded
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| 25 |
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* Dataset: kmfoda/booksum
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| 26 |
+
* Config: kmfoda--booksum
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| 27 |
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* Split: test
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| 28 |
+
|
| 29 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
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| 30 |
+
|
| 31 |
+
## Contributions
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| 32 |
+
|
| 33 |
+
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
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huggingface_dataset/Dataset_Card/sagawa_ord-uniq-canonicalized.md
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| 1 |
+
---
|
| 2 |
+
annotations_creators: []
|
| 3 |
+
language_creators: []
|
| 4 |
+
license:
|
| 5 |
+
- apache-2.0
|
| 6 |
+
multilinguality:
|
| 7 |
+
- monolingual
|
| 8 |
+
pretty_name: canonicalized ORD
|
| 9 |
+
size_categories:
|
| 10 |
+
- 1M<n<10M
|
| 11 |
+
source_datasets:
|
| 12 |
+
- original
|
| 13 |
+
tags:
|
| 14 |
+
- ord
|
| 15 |
+
- chemical
|
| 16 |
+
- reaction
|
| 17 |
+
task_categories:
|
| 18 |
+
- text2text-generation
|
| 19 |
+
- translation
|
| 20 |
+
task_ids: []
|
| 21 |
+
---
|
| 22 |
+
|
| 23 |
+
### dataset description
|
| 24 |
+
We downloaded open-reaction-database(ORD) dataset from [here](https://github.com/open-reaction-database/ord-data). As a preprocess, we removed overlapping data and canonicalized them using RDKit.
|
| 25 |
+
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
|
| 26 |
+
|
| 27 |
+
```python:
|
| 28 |
+
from rdkit import Chem
|
| 29 |
+
def canonicalize(mol):
|
| 30 |
+
mol = Chem.MolToSmiles(Chem.MolFromSmiles(mol),True)
|
| 31 |
+
return mol
|
| 32 |
+
```
|
| 33 |
+
|
| 34 |
+
We randomly split the preprocessed data into train, validation and test. The ratio is 8:1:1.
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huggingface_dataset/Dataset_Info/dataset_info.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fe2ea04f72ef0eb546a6fa6a74018972a1080dde98aee68ab288c65e0390f899
|
| 3 |
+
size 1098674
|
huggingface_dataset/Dataset_Info/datasetcard_info.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5372579a636a8db5c91dc580d429cc9a51e864da603139e8fa03269bf1d12a6f
|
| 3 |
+
size 12206689
|
huggingface_dataset/Dataset_Info/datasetcard_sections_info.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
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|
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|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5a8dacd139879b1aa46024d36f42217ab28cbf5772f1906a6237ff18537096eb
|
| 3 |
+
size 3645210
|