Initial Commit
Browse files- README.md +58 -58
- eval_result_ner.json +1 -1
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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---
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base_model: FacebookAI/xlm-roberta-base
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library_name: transformers
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license: mit
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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tags:
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- generated_from_trainer
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model-index:
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- name: scenario-non-kd-pre-ner-full-xlmr_data-univner_half55
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results: []
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@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.7969
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0003 | 28.5548 | 24500 | 0.1713 | 0.
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### Framework versions
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---
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library_name: transformers
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license: mit
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base_model: FacebookAI/xlm-roberta-base
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: scenario-non-kd-pre-ner-full-xlmr_data-univner_half55
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results: []
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1716
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- Precision: 0.7969
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- Recall: 0.7989
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- F1: 0.7979
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- Accuracy: 0.9786
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.144 | 0.5828 | 500 | 0.0813 | 0.6995 | 0.7625 | 0.7297 | 0.9732 |
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| 0.0678 | 1.1655 | 1000 | 0.0775 | 0.7546 | 0.7816 | 0.7678 | 0.9772 |
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| 0.0531 | 1.7483 | 1500 | 0.0810 | 0.7244 | 0.8137 | 0.7665 | 0.9760 |
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| 0.0368 | 2.3310 | 2000 | 0.0831 | 0.7742 | 0.7919 | 0.7830 | 0.9782 |
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| 0.0311 | 2.9138 | 2500 | 0.0871 | 0.7695 | 0.7917 | 0.7804 | 0.9773 |
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| 0.0222 | 3.4965 | 3000 | 0.0916 | 0.7856 | 0.7860 | 0.7858 | 0.9785 |
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| 0.0196 | 4.0793 | 3500 | 0.0982 | 0.7778 | 0.7990 | 0.7883 | 0.9781 |
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| 0.0145 | 4.6620 | 4000 | 0.1004 | 0.7769 | 0.7927 | 0.7847 | 0.9778 |
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| 0.0131 | 5.2448 | 4500 | 0.1058 | 0.7719 | 0.7878 | 0.7798 | 0.9769 |
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| 0.0108 | 5.8275 | 5000 | 0.1116 | 0.7830 | 0.7919 | 0.7875 | 0.9775 |
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| 0.0086 | 6.4103 | 5500 | 0.1137 | 0.7743 | 0.8018 | 0.7878 | 0.9778 |
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| 0.009 | 6.9930 | 6000 | 0.1180 | 0.7739 | 0.8078 | 0.7905 | 0.9778 |
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| 0.0066 | 7.5758 | 6500 | 0.1189 | 0.7761 | 0.8090 | 0.7922 | 0.9782 |
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| 0.007 | 8.1585 | 7000 | 0.1281 | 0.7813 | 0.7869 | 0.7841 | 0.9777 |
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| 0.0059 | 8.7413 | 7500 | 0.1222 | 0.7781 | 0.8070 | 0.7923 | 0.9785 |
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| 0.0057 | 9.3240 | 8000 | 0.1298 | 0.7694 | 0.8124 | 0.7903 | 0.9781 |
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| 0.0053 | 9.9068 | 8500 | 0.1260 | 0.7919 | 0.7951 | 0.7935 | 0.9787 |
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| 0.0043 | 10.4895 | 9000 | 0.1356 | 0.7719 | 0.8062 | 0.7887 | 0.9778 |
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| 0.0042 | 11.0723 | 9500 | 0.1309 | 0.7850 | 0.7982 | 0.7915 | 0.9786 |
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| 0.0042 | 11.6550 | 10000 | 0.1356 | 0.7789 | 0.7922 | 0.7855 | 0.9779 |
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| 0.0034 | 12.2378 | 10500 | 0.1367 | 0.7781 | 0.8013 | 0.7895 | 0.9782 |
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| 0.0032 | 12.8205 | 11000 | 0.1409 | 0.7732 | 0.8123 | 0.7923 | 0.9781 |
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| 0.0022 | 13.4033 | 11500 | 0.1498 | 0.7707 | 0.8068 | 0.7883 | 0.9778 |
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| 0.0031 | 13.9860 | 12000 | 0.1454 | 0.7704 | 0.8133 | 0.7913 | 0.9781 |
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| 0.0022 | 14.5688 | 12500 | 0.1436 | 0.7922 | 0.7934 | 0.7928 | 0.9784 |
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| 0.0024 | 15.1515 | 13000 | 0.1461 | 0.7734 | 0.8077 | 0.7902 | 0.9778 |
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| 0.0023 | 15.7343 | 13500 | 0.1465 | 0.7918 | 0.7996 | 0.7957 | 0.9786 |
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| 0.0017 | 16.3170 | 14000 | 0.1506 | 0.7838 | 0.8022 | 0.7929 | 0.9783 |
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| 0.0018 | 16.8998 | 14500 | 0.1466 | 0.7953 | 0.7973 | 0.7963 | 0.9787 |
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| 0.0018 | 17.4825 | 15000 | 0.1502 | 0.7941 | 0.8012 | 0.7976 | 0.9789 |
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| 0.0019 | 18.0653 | 15500 | 0.1515 | 0.7871 | 0.8052 | 0.7960 | 0.9786 |
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| 0.0018 | 18.6480 | 16000 | 0.1501 | 0.8062 | 0.7780 | 0.7918 | 0.9782 |
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| 0.0016 | 19.2308 | 16500 | 0.1547 | 0.7887 | 0.7963 | 0.7924 | 0.9780 |
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| 0.001 | 19.8135 | 17000 | 0.1650 | 0.7819 | 0.8070 | 0.7942 | 0.9778 |
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| 0.0009 | 20.3963 | 17500 | 0.1612 | 0.7971 | 0.7833 | 0.7901 | 0.9780 |
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| 0.0012 | 20.9790 | 18000 | 0.1569 | 0.7903 | 0.8023 | 0.7962 | 0.9785 |
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| 0.0008 | 21.5618 | 18500 | 0.1640 | 0.7787 | 0.8081 | 0.7931 | 0.9779 |
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| 0.0009 | 22.1445 | 19000 | 0.1640 | 0.7950 | 0.7924 | 0.7937 | 0.9781 |
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| 0.0008 | 22.7273 | 19500 | 0.1650 | 0.7982 | 0.8023 | 0.8003 | 0.9789 |
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| 0.0007 | 23.3100 | 20000 | 0.1635 | 0.7962 | 0.8022 | 0.7992 | 0.9787 |
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| 0.0008 | 23.8928 | 20500 | 0.1678 | 0.7852 | 0.8005 | 0.7927 | 0.9784 |
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| 0.0006 | 24.4755 | 21000 | 0.1686 | 0.7970 | 0.8025 | 0.7997 | 0.9788 |
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| 0.0006 | 25.0583 | 21500 | 0.1686 | 0.7963 | 0.7970 | 0.7967 | 0.9785 |
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| 0.0006 | 25.6410 | 22000 | 0.1706 | 0.7941 | 0.7948 | 0.7945 | 0.9784 |
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| 0.0005 | 26.2238 | 22500 | 0.1681 | 0.7935 | 0.7963 | 0.7949 | 0.9785 |
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| 0.0005 | 26.8065 | 23000 | 0.1688 | 0.8008 | 0.7938 | 0.7973 | 0.9788 |
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| 0.0004 | 27.3893 | 23500 | 0.1700 | 0.7898 | 0.7996 | 0.7947 | 0.9784 |
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| 0.0005 | 27.9720 | 24000 | 0.1708 | 0.7914 | 0.8096 | 0.8004 | 0.9786 |
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| 0.0003 | 28.5548 | 24500 | 0.1713 | 0.7965 | 0.7979 | 0.7972 | 0.9785 |
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| 0.0004 | 29.1375 | 25000 | 0.1710 | 0.7951 | 0.8010 | 0.7980 | 0.9786 |
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| 0.0005 | 29.7203 | 25500 | 0.1716 | 0.7969 | 0.7989 | 0.7979 | 0.9786 |
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### Framework versions
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eval_result_ner.json
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{"ceb_gja": {"precision": 0.
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{"ceb_gja": {"precision": 0.4457831325301205, "recall": 0.7551020408163265, "f1": 0.5606060606060607, "accuracy": 0.9544401544401544}, "en_pud": {"precision": 0.7670454545454546, "recall": 0.7534883720930232, "f1": 0.7602064758329422, "accuracy": 0.9765300340007556}, "de_pud": {"precision": 0.7195004803073968, "recall": 0.7208854667949952, "f1": 0.7201923076923077, "accuracy": 0.9703717594111856}, "pt_pud": {"precision": 0.7764489420423183, "recall": 0.7679708826205641, "f1": 0.7721866422689845, "accuracy": 0.9781262015636348}, "ru_pud": {"precision": 0.6713352007469654, "recall": 0.694015444015444, "f1": 0.6824869482676791, "accuracy": 0.9678636011366571}, "sv_pud": {"precision": 0.8224206349206349, "recall": 0.8056365403304179, "f1": 0.8139420716740304, "accuracy": 0.9814950723422101}, "tl_trg": {"precision": 0.7407407407407407, "recall": 0.8695652173913043, "f1": 0.7999999999999999, "accuracy": 0.9877384196185286}, "tl_ugnayan": {"precision": 0.4473684210526316, "recall": 0.5151515151515151, "f1": 0.4788732394366197, "accuracy": 0.9617137648131268}, "zh_gsd": {"precision": 0.7835443037974683, "recall": 0.8070404172099087, "f1": 0.7951188182402055, "accuracy": 0.9708624708624709}, "zh_gsdsimp": {"precision": 0.8065789473684211, "recall": 0.8034076015727392, "f1": 0.8049901510177282, "accuracy": 0.9725274725274725}, "hr_set": {"precision": 0.8631074606433949, "recall": 0.8987883107626514, "f1": 0.880586592178771, "accuracy": 0.9866859027205276}, "da_ddt": {"precision": 0.8014354066985646, "recall": 0.7494407158836689, "f1": 0.7745664739884394, "accuracy": 0.9833383218597226}, "en_ewt": {"precision": 0.7836538461538461, "recall": 0.7490808823529411, "f1": 0.7659774436090225, "accuracy": 0.9764912140893334}, "pt_bosque": {"precision": 0.7544964028776978, "recall": 0.6905349794238683, "f1": 0.721100128921358, "accuracy": 0.9736632372119982}, "sr_set": {"precision": 0.8968347010550997, "recall": 0.9031877213695395, "f1": 0.9, "accuracy": 0.9866036249014972}, "sk_snk": {"precision": 0.6903954802259887, "recall": 0.66775956284153, "f1": 0.6788888888888889, "accuracy": 0.9563442211055276}, "sv_talbanken": {"precision": 0.8119266055045872, "recall": 0.9030612244897959, "f1": 0.8550724637681159, "accuracy": 0.9970064288168032}}
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model.safetensors
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training_args.bin
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