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scenario-non-kd-po-ner-full-xlmr_data-univner_half66

Token Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
Inference Endpoints
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  1. README.md +59 -31
  2. config.json +1 -1
  3. eval_result_ner.json +1 -1
  4. model.safetensors +2 -2
  5. training_args.bin +1 -1
README.md CHANGED
@@ -1,14 +1,14 @@
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  ---
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- base_model: haryoaw/scenario-TCR-NER_data-univner_half
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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-po-ner-full-xlmr_data-univner_half66
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  results: []
@@ -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 [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1331
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- - Precision: 0.8406
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- - Recall: 0.8491
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- - F1: 0.8448
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- - Accuracy: 0.9836
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  ## Model description
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@@ -56,29 +56,57 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.0102 | 0.5828 | 500 | 0.0979 | 0.8297 | 0.8550 | 0.8422 | 0.9832 |
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- | 0.0109 | 1.1655 | 1000 | 0.0889 | 0.8346 | 0.8466 | 0.8406 | 0.9835 |
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- | 0.0084 | 1.7483 | 1500 | 0.0932 | 0.8491 | 0.8462 | 0.8477 | 0.9839 |
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- | 0.0075 | 2.3310 | 2000 | 0.0919 | 0.8434 | 0.8437 | 0.8436 | 0.9835 |
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- | 0.0072 | 2.9138 | 2500 | 0.1043 | 0.8278 | 0.8380 | 0.8329 | 0.9826 |
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- | 0.0058 | 3.4965 | 3000 | 0.1020 | 0.8370 | 0.8468 | 0.8419 | 0.9832 |
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- | 0.0059 | 4.0793 | 3500 | 0.1030 | 0.8467 | 0.8458 | 0.8463 | 0.9839 |
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- | 0.005 | 4.6620 | 4000 | 0.1182 | 0.8492 | 0.8326 | 0.8408 | 0.9830 |
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- | 0.005 | 5.2448 | 4500 | 0.1141 | 0.8235 | 0.8510 | 0.8370 | 0.9821 |
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- | 0.0044 | 5.8275 | 5000 | 0.1184 | 0.8273 | 0.8572 | 0.8420 | 0.9828 |
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- | 0.0045 | 6.4103 | 5500 | 0.1213 | 0.8417 | 0.8494 | 0.8455 | 0.9833 |
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- | 0.0048 | 6.9930 | 6000 | 0.1126 | 0.8413 | 0.8413 | 0.8413 | 0.9835 |
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- | 0.0043 | 7.5758 | 6500 | 0.1240 | 0.8363 | 0.8472 | 0.8417 | 0.9830 |
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- | 0.0036 | 8.1585 | 7000 | 0.1185 | 0.8470 | 0.8450 | 0.8460 | 0.9838 |
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- | 0.0032 | 8.7413 | 7500 | 0.1249 | 0.8338 | 0.8391 | 0.8365 | 0.9828 |
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- | 0.0022 | 9.3240 | 8000 | 0.1260 | 0.8351 | 0.8499 | 0.8425 | 0.9835 |
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- | 0.003 | 9.9068 | 8500 | 0.1208 | 0.8273 | 0.8420 | 0.8346 | 0.9827 |
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- | 0.0024 | 10.4895 | 9000 | 0.1216 | 0.8451 | 0.8463 | 0.8457 | 0.9836 |
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- | 0.0027 | 11.0723 | 9500 | 0.1214 | 0.8410 | 0.8390 | 0.8400 | 0.9832 |
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- | 0.0019 | 11.6550 | 10000 | 0.1234 | 0.8419 | 0.8458 | 0.8438 | 0.9833 |
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- | 0.0023 | 12.2378 | 10500 | 0.1289 | 0.8339 | 0.8502 | 0.8420 | 0.9830 |
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- | 0.0019 | 12.8205 | 11000 | 0.1286 | 0.8400 | 0.8401 | 0.8401 | 0.9832 |
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- | 0.002 | 13.4033 | 11500 | 0.1331 | 0.8406 | 0.8491 | 0.8448 | 0.9836 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
1
  ---
 
2
  library_name: transformers
3
  license: mit
4
+ base_model: haryoaw/scenario-TCR-NER_data-univner_half
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+ tags:
6
+ - generated_from_trainer
7
  metrics:
8
  - precision
9
  - recall
10
  - f1
11
  - accuracy
 
 
12
  model-index:
13
  - name: scenario-non-kd-po-ner-full-xlmr_data-univner_half66
14
  results: []
 
21
 
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  This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
23
  It achieves the following results on the evaluation set:
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+ - Loss: 0.1641
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+ - Precision: 0.8048
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+ - Recall: 0.8120
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+ - F1: 0.8084
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+ - Accuracy: 0.9797
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30
  ## Model description
31
 
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0757 | 0.5828 | 500 | 0.0751 | 0.7550 | 0.7817 | 0.7681 | 0.9772 |
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+ | 0.0434 | 1.1655 | 1000 | 0.0856 | 0.7626 | 0.7987 | 0.7803 | 0.9783 |
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+ | 0.0293 | 1.7483 | 1500 | 0.0781 | 0.7835 | 0.8022 | 0.7928 | 0.9792 |
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+ | 0.0208 | 2.3310 | 2000 | 0.0929 | 0.7929 | 0.7868 | 0.7898 | 0.9784 |
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+ | 0.0171 | 2.9138 | 2500 | 0.0903 | 0.7893 | 0.8176 | 0.8032 | 0.9796 |
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+ | 0.0125 | 3.4965 | 3000 | 0.1048 | 0.7915 | 0.7876 | 0.7896 | 0.9779 |
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+ | 0.0121 | 4.0793 | 3500 | 0.1080 | 0.7890 | 0.8033 | 0.7961 | 0.9788 |
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+ | 0.0096 | 4.6620 | 4000 | 0.1116 | 0.7827 | 0.8083 | 0.7953 | 0.9789 |
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+ | 0.0083 | 5.2448 | 4500 | 0.1136 | 0.7957 | 0.8002 | 0.7979 | 0.9786 |
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+ | 0.0074 | 5.8275 | 5000 | 0.1162 | 0.7785 | 0.8139 | 0.7958 | 0.9787 |
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+ | 0.0066 | 6.4103 | 5500 | 0.1192 | 0.7851 | 0.8058 | 0.7953 | 0.9786 |
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+ | 0.0065 | 6.9930 | 6000 | 0.1200 | 0.8077 | 0.7791 | 0.7931 | 0.9787 |
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+ | 0.0049 | 7.5758 | 6500 | 0.1227 | 0.8039 | 0.7974 | 0.8007 | 0.9795 |
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+ | 0.0051 | 8.1585 | 7000 | 0.1259 | 0.7906 | 0.8058 | 0.7981 | 0.9789 |
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+ | 0.0041 | 8.7413 | 7500 | 0.1258 | 0.7992 | 0.7905 | 0.7948 | 0.9792 |
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+ | 0.004 | 9.3240 | 8000 | 0.1336 | 0.7865 | 0.8072 | 0.7967 | 0.9786 |
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+ | 0.0039 | 9.9068 | 8500 | 0.1261 | 0.8145 | 0.7902 | 0.8022 | 0.9792 |
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+ | 0.0032 | 10.4895 | 9000 | 0.1309 | 0.7940 | 0.7999 | 0.7970 | 0.9791 |
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+ | 0.0033 | 11.0723 | 9500 | 0.1320 | 0.8054 | 0.7869 | 0.7960 | 0.9793 |
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+ | 0.0026 | 11.6550 | 10000 | 0.1408 | 0.7915 | 0.8071 | 0.7992 | 0.9789 |
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+ | 0.0026 | 12.2378 | 10500 | 0.1404 | 0.7942 | 0.8005 | 0.7973 | 0.9788 |
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+ | 0.0022 | 12.8205 | 11000 | 0.1363 | 0.7897 | 0.8134 | 0.8014 | 0.9797 |
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+ | 0.0024 | 13.4033 | 11500 | 0.1442 | 0.8065 | 0.7970 | 0.8017 | 0.9793 |
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+ | 0.0021 | 13.9860 | 12000 | 0.1401 | 0.8092 | 0.7840 | 0.7964 | 0.9789 |
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+ | 0.0022 | 14.5688 | 12500 | 0.1382 | 0.8100 | 0.7983 | 0.8041 | 0.9792 |
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+ | 0.0015 | 15.1515 | 13000 | 0.1506 | 0.8066 | 0.7995 | 0.8030 | 0.9793 |
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+ | 0.0018 | 15.7343 | 13500 | 0.1437 | 0.8047 | 0.7989 | 0.8018 | 0.9794 |
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+ | 0.002 | 16.3170 | 14000 | 0.1448 | 0.7997 | 0.8046 | 0.8022 | 0.9794 |
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+ | 0.0016 | 16.8998 | 14500 | 0.1466 | 0.8111 | 0.7934 | 0.8021 | 0.9792 |
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+ | 0.0013 | 17.4825 | 15000 | 0.1506 | 0.8046 | 0.7943 | 0.7994 | 0.9791 |
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+ | 0.0012 | 18.0653 | 15500 | 0.1508 | 0.8038 | 0.8067 | 0.8052 | 0.9796 |
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+ | 0.0011 | 18.6480 | 16000 | 0.1493 | 0.8026 | 0.8013 | 0.8020 | 0.9796 |
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+ | 0.0011 | 19.2308 | 16500 | 0.1570 | 0.7905 | 0.8048 | 0.7976 | 0.9787 |
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+ | 0.0011 | 19.8135 | 17000 | 0.1510 | 0.7980 | 0.8075 | 0.8027 | 0.9793 |
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+ | 0.001 | 20.3963 | 17500 | 0.1534 | 0.7928 | 0.8173 | 0.8049 | 0.9797 |
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+ | 0.0009 | 20.9790 | 18000 | 0.1485 | 0.7916 | 0.8181 | 0.8046 | 0.9798 |
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+ | 0.0008 | 21.5618 | 18500 | 0.1509 | 0.8074 | 0.8045 | 0.8060 | 0.9798 |
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+ | 0.0009 | 22.1445 | 19000 | 0.1515 | 0.8070 | 0.8084 | 0.8077 | 0.9801 |
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+ | 0.0006 | 22.7273 | 19500 | 0.1566 | 0.8022 | 0.8106 | 0.8064 | 0.9798 |
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+ | 0.0007 | 23.3100 | 20000 | 0.1620 | 0.8076 | 0.7976 | 0.8026 | 0.9794 |
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+ | 0.0008 | 23.8928 | 20500 | 0.1570 | 0.8028 | 0.8084 | 0.8056 | 0.9798 |
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+ | 0.0005 | 24.4755 | 21000 | 0.1563 | 0.8020 | 0.8110 | 0.8065 | 0.9798 |
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+ | 0.0004 | 25.0583 | 21500 | 0.1610 | 0.8059 | 0.8013 | 0.8036 | 0.9794 |
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+ | 0.0004 | 25.6410 | 22000 | 0.1645 | 0.8133 | 0.7943 | 0.8036 | 0.9793 |
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+ | 0.0004 | 26.2238 | 22500 | 0.1615 | 0.8031 | 0.8100 | 0.8066 | 0.9798 |
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+ | 0.0004 | 26.8065 | 23000 | 0.1630 | 0.8010 | 0.8156 | 0.8083 | 0.9796 |
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+ | 0.0003 | 27.3893 | 23500 | 0.1626 | 0.8062 | 0.8114 | 0.8088 | 0.9800 |
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+ | 0.0003 | 27.9720 | 24000 | 0.1626 | 0.8054 | 0.8147 | 0.8101 | 0.9800 |
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+ | 0.0002 | 28.5548 | 24500 | 0.1639 | 0.8079 | 0.8107 | 0.8093 | 0.9798 |
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+ | 0.0003 | 29.1375 | 25000 | 0.1637 | 0.8064 | 0.8085 | 0.8075 | 0.9797 |
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+ | 0.0002 | 29.7203 | 25500 | 0.1641 | 0.8048 | 0.8120 | 0.8084 | 0.9797 |
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  ### Framework versions
config.json CHANGED
@@ -34,7 +34,7 @@
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  "max_position_embeddings": 514,
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  "model_type": "xlm-roberta",
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  "num_attention_heads": 12,
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- "num_hidden_layers": 12,
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  "output_past": true,
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  "pad_token_id": 1,
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  "position_embedding_type": "absolute",
 
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  "max_position_embeddings": 514,
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  "model_type": "xlm-roberta",
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  "num_attention_heads": 12,
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+ "num_hidden_layers": 6,
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  "output_past": true,
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  "pad_token_id": 1,
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  "position_embedding_type": "absolute",
eval_result_ner.json CHANGED
@@ -1 +1 @@
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- {"ceb_gja": {"precision": 0.6515151515151515, "recall": 0.8775510204081632, "f1": 0.7478260869565216, "accuracy": 0.9783783783783784}, "en_pud": {"precision": 0.8212237093690249, "recall": 0.7990697674418604, "f1": 0.8099952852428101, "accuracy": 0.9802134491877598}, "de_pud": {"precision": 0.8014981273408239, "recall": 0.8238691049085659, "f1": 0.8125296630280019, "accuracy": 0.9786226618536402}, "pt_pud": {"precision": 0.8650137741046832, "recall": 0.8571428571428571, "f1": 0.8610603290676416, "accuracy": 0.9854317084632802}, "ru_pud": {"precision": 0.7129798903107861, "recall": 0.752895752895753, "f1": 0.732394366197183, "accuracy": 0.9727202273314389}, "sv_pud": {"precision": 0.859086491739553, "recall": 0.859086491739553, "f1": 0.859086491739553, "accuracy": 0.9858985112182848}, "tl_trg": {"precision": 0.9166666666666666, "recall": 0.9565217391304348, "f1": 0.9361702127659574, "accuracy": 0.9959128065395095}, "tl_ugnayan": {"precision": 0.6923076923076923, "recall": 0.8181818181818182, "f1": 0.7500000000000001, "accuracy": 0.9808568824065633}, "zh_gsd": {"precision": 0.8350785340314136, "recall": 0.8318122555410691, "f1": 0.8334421946440235, "accuracy": 0.9779387279387279}, "zh_gsdsimp": {"precision": 0.8398950131233596, "recall": 0.8387942332896461, "f1": 0.839344262295082, "accuracy": 0.9781884781884782}, "hr_set": {"precision": 0.9279151943462898, "recall": 0.9358517462580185, "f1": 0.9318665720369056, "accuracy": 0.9912613355317395}, "da_ddt": {"precision": 0.8275058275058275, "recall": 0.7941834451901566, "f1": 0.8105022831050228, "accuracy": 0.9857328145265889}, "en_ewt": {"precision": 0.8248587570621468, "recall": 0.8051470588235294, "f1": 0.8148837209302325, "accuracy": 0.9815914252699526}, "pt_bosque": {"precision": 0.8616636528028933, "recall": 0.7843621399176954, "f1": 0.8211977595863852, "accuracy": 0.982393855962904}, "sr_set": {"precision": 0.9475566150178785, "recall": 0.9386068476977568, "f1": 0.9430604982206405, "accuracy": 0.9908939672533054}, "sk_snk": {"precision": 0.7965554359526372, "recall": 0.8087431693989071, "f1": 0.8026030368763557, "accuracy": 0.9722047738693468}, "sv_talbanken": {"precision": 0.8786407766990292, "recall": 0.923469387755102, "f1": 0.900497512437811, "accuracy": 0.9979388526279629}}
 
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