Initial Commit
Browse files- README.md +32 -70
- config.json +1 -1
- 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: haryoaw/scenario-TCR-NER_data-univner_full
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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_data-univner_full44
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results: []
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@@ -19,13 +19,13 @@ should probably proofread and complete it, then remove this comment. -->
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# scenario-non-kd-po-ner-full_data-univner_full44
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This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-
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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.
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- Recall: 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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| 0.0038 | 6.9849 | 12000 | 0.1019 | 0.8494 | 0.8631 | 0.8562 | 0.9846 |
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| 0.0031 | 7.2759 | 12500 | 0.1073 | 0.8563 | 0.8575 | 0.8569 | 0.9845 |
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| 0.0031 | 7.5669 | 13000 | 0.1013 | 0.8431 | 0.8696 | 0.8561 | 0.9847 |
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| 0.0034 | 7.8580 | 13500 | 0.1058 | 0.8533 | 0.8596 | 0.8565 | 0.9845 |
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| 0.0027 | 8.1490 | 14000 | 0.1154 | 0.8431 | 0.8719 | 0.8572 | 0.9845 |
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| 0.0027 | 8.4400 | 14500 | 0.1030 | 0.8404 | 0.8785 | 0.8591 | 0.9845 |
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| 0.0028 | 8.7311 | 15000 | 0.1132 | 0.8559 | 0.8510 | 0.8534 | 0.9846 |
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| 0.003 | 9.0221 | 15500 | 0.1106 | 0.8514 | 0.8648 | 0.8581 | 0.9848 |
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| 0.0022 | 9.3132 | 16000 | 0.1136 | 0.8586 | 0.8657 | 0.8621 | 0.9852 |
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| 0.0025 | 9.6042 | 16500 | 0.1128 | 0.8494 | 0.8697 | 0.8594 | 0.9848 |
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| 0.002 | 9.8952 | 17000 | 0.1139 | 0.8453 | 0.8600 | 0.8526 | 0.9841 |
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| 0.0024 | 10.1863 | 17500 | 0.1124 | 0.8541 | 0.8658 | 0.8599 | 0.9849 |
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| 0.002 | 10.4773 | 18000 | 0.1154 | 0.8368 | 0.8663 | 0.8513 | 0.9842 |
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| 0.0019 | 10.7683 | 18500 | 0.1182 | 0.8457 | 0.8629 | 0.8542 | 0.9844 |
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| 0.0023 | 11.0594 | 19000 | 0.1140 | 0.8531 | 0.8596 | 0.8563 | 0.9846 |
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| 0.0018 | 11.3504 | 19500 | 0.1194 | 0.8526 | 0.8683 | 0.8604 | 0.9851 |
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| 0.0016 | 11.6414 | 20000 | 0.1198 | 0.8527 | 0.8652 | 0.8589 | 0.9848 |
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| 0.0021 | 11.9325 | 20500 | 0.1169 | 0.8592 | 0.8654 | 0.8623 | 0.9852 |
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| 0.0016 | 12.2235 | 21000 | 0.1229 | 0.8605 | 0.8626 | 0.8616 | 0.9848 |
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| 0.0021 | 12.5146 | 21500 | 0.1198 | 0.8484 | 0.8697 | 0.8589 | 0.9846 |
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| 0.0019 | 12.8056 | 22000 | 0.1177 | 0.8535 | 0.8600 | 0.8568 | 0.9844 |
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| 0.0013 | 13.0966 | 22500 | 0.1190 | 0.8436 | 0.8716 | 0.8574 | 0.9844 |
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| 0.0016 | 13.3877 | 23000 | 0.1227 | 0.8475 | 0.8665 | 0.8569 | 0.9847 |
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| 0.0012 | 13.6787 | 23500 | 0.1237 | 0.8513 | 0.8676 | 0.8594 | 0.9848 |
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| 0.0015 | 13.9697 | 24000 | 0.1198 | 0.8407 | 0.8709 | 0.8555 | 0.9843 |
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| 0.0012 | 14.2608 | 24500 | 0.1239 | 0.8516 | 0.8689 | 0.8602 | 0.9850 |
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| 0.0014 | 14.5518 | 25000 | 0.1261 | 0.8432 | 0.8634 | 0.8532 | 0.9843 |
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| 0.0015 | 14.8428 | 25500 | 0.1220 | 0.8451 | 0.8716 | 0.8582 | 0.9849 |
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| 0.0013 | 15.1339 | 26000 | 0.1209 | 0.8608 | 0.8598 | 0.8603 | 0.9847 |
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| 0.0011 | 15.4249 | 26500 | 0.1261 | 0.8457 | 0.8637 | 0.8546 | 0.9847 |
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| 0.0011 | 15.7159 | 27000 | 0.1273 | 0.8510 | 0.8616 | 0.8563 | 0.9846 |
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| 0.0022 | 16.0070 | 27500 | 0.1282 | 0.8431 | 0.8738 | 0.8582 | 0.9847 |
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| 0.001 | 16.2980 | 28000 | 0.1357 | 0.8451 | 0.8628 | 0.8539 | 0.9842 |
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| 0.0013 | 16.5891 | 28500 | 0.1301 | 0.8465 | 0.8658 | 0.8561 | 0.9843 |
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| 0.0008 | 16.8801 | 29000 | 0.1335 | 0.8533 | 0.8678 | 0.8605 | 0.9845 |
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| 0.0011 | 17.1711 | 29500 | 0.1338 | 0.8572 | 0.8654 | 0.8613 | 0.9846 |
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| 0.0006 | 17.4622 | 30000 | 0.1368 | 0.8561 | 0.8628 | 0.8594 | 0.9847 |
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| 0.0008 | 17.7532 | 30500 | 0.1359 | 0.8579 | 0.8661 | 0.8620 | 0.9848 |
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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: haryoaw/scenario-TCR-NER_data-univner_en
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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-po-ner-full_data-univner_full44
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results: []
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# scenario-non-kd-po-ner-full_data-univner_full44
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This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1413
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- Precision: 0.7900
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- Recall: 0.8023
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- F1: 0.7961
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- Accuracy: 0.9836
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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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| 0.0035 | 1.2755 | 500 | 0.1065 | 0.7916 | 0.8023 | 0.7969 | 0.9842 |
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| 0.0036 | 2.5510 | 1000 | 0.1246 | 0.7914 | 0.7619 | 0.7764 | 0.9821 |
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| 0.0027 | 3.8265 | 1500 | 0.1191 | 0.7819 | 0.8054 | 0.7935 | 0.9837 |
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| 0.002 | 5.1020 | 2000 | 0.1324 | 0.7907 | 0.7940 | 0.7924 | 0.9831 |
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| 0.0023 | 6.3776 | 2500 | 0.1197 | 0.7826 | 0.8085 | 0.7953 | 0.9836 |
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| 0.0017 | 7.6531 | 3000 | 0.1390 | 0.7673 | 0.8054 | 0.7859 | 0.9819 |
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| 0.0012 | 8.9286 | 3500 | 0.1371 | 0.7827 | 0.7609 | 0.7717 | 0.9815 |
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| 0.0013 | 10.2041 | 4000 | 0.1459 | 0.7426 | 0.8002 | 0.7703 | 0.9809 |
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| 0.0017 | 11.4796 | 4500 | 0.1345 | 0.7771 | 0.7723 | 0.7747 | 0.9819 |
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| 0.0011 | 12.7551 | 5000 | 0.1327 | 0.7824 | 0.7930 | 0.7877 | 0.9831 |
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| 0.001 | 14.0306 | 5500 | 0.1422 | 0.7591 | 0.7961 | 0.7772 | 0.9813 |
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| 0.0009 | 15.3061 | 6000 | 0.1383 | 0.7715 | 0.7899 | 0.7806 | 0.9819 |
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| 0.0006 | 16.5816 | 6500 | 0.1360 | 0.7827 | 0.8054 | 0.7939 | 0.9831 |
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| 0.0006 | 17.8571 | 7000 | 0.1429 | 0.7889 | 0.7930 | 0.7909 | 0.9834 |
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| 0.0006 | 19.1327 | 7500 | 0.1409 | 0.7933 | 0.7826 | 0.7879 | 0.9827 |
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| 0.0005 | 20.4082 | 8000 | 0.1415 | 0.7886 | 0.7992 | 0.7938 | 0.9835 |
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| 0.0005 | 21.6837 | 8500 | 0.1361 | 0.7913 | 0.7930 | 0.7921 | 0.9832 |
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| 0.0004 | 22.9592 | 9000 | 0.1393 | 0.8069 | 0.8002 | 0.8035 | 0.9839 |
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| 0.0004 | 24.2347 | 9500 | 0.1376 | 0.7784 | 0.8147 | 0.7962 | 0.9835 |
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| 0.0003 | 25.5102 | 10000 | 0.1421 | 0.7862 | 0.7919 | 0.7891 | 0.9833 |
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| 0.0002 | 26.7857 | 10500 | 0.1417 | 0.7882 | 0.8054 | 0.7967 | 0.9834 |
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| 0.0002 | 28.0612 | 11000 | 0.1399 | 0.7900 | 0.7981 | 0.7940 | 0.9835 |
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| 0.0001 | 29.3367 | 11500 | 0.1413 | 0.7900 | 0.8023 | 0.7961 | 0.9836 |
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### Framework versions
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config.json
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{
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"_name_or_path": "haryoaw/scenario-TCR-NER_data-
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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{
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"_name_or_path": "haryoaw/scenario-TCR-NER_data-univner_en",
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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eval_result_ner.json
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{"ceb_gja": {"precision": 0.
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{"ceb_gja": {"precision": 0.37209302325581395, "recall": 0.6530612244897959, "f1": 0.47407407407407404, "accuracy": 0.9405405405405406}, "en_pud": {"precision": 0.8098933074684772, "recall": 0.7767441860465116, "f1": 0.7929724596391263, "accuracy": 0.9789384208537968}, "de_pud": {"precision": 0.781431334622824, "recall": 0.7776708373435997, "f1": 0.7795465508924265, "accuracy": 0.9758098542028034}, "pt_pud": {"precision": 0.8095684803001876, "recall": 0.7852593266606005, "f1": 0.7972286374133949, "accuracy": 0.9798778143290469}, "ru_pud": {"precision": 0.6734496124031008, "recall": 0.6708494208494209, "f1": 0.672147001934236, "accuracy": 0.9661069491087574}, "sv_pud": {"precision": 0.8372781065088757, "recall": 0.8250728862973761, "f1": 0.8311306901615271, "accuracy": 0.9827007758439924}, "tl_trg": {"precision": 0.75, "recall": 0.9130434782608695, "f1": 0.8235294117647057, "accuracy": 0.9863760217983651}, "tl_ugnayan": {"precision": 0.575, "recall": 0.696969696969697, "f1": 0.6301369863013698, "accuracy": 0.9735642661804923}, "zh_gsd": {"precision": 0.5416666666666666, "recall": 0.423728813559322, "f1": 0.4754937820043892, "accuracy": 0.9314019314019314}, "zh_gsdsimp": {"precision": 0.560200668896321, "recall": 0.43905635648754915, "f1": 0.4922850844966936, "accuracy": 0.9354811854811855}, "hr_set": {"precision": 0.800453514739229, "recall": 0.7548111190306486, "f1": 0.7769625825385178, "accuracy": 0.9726298433635614}, "da_ddt": {"precision": 0.8036649214659686, "recall": 0.6868008948545862, "f1": 0.7406513872135103, "accuracy": 0.9798463533872094}, "en_ewt": {"precision": 0.8349802371541502, "recall": 0.7766544117647058, "f1": 0.8047619047619047, "accuracy": 0.9790014742797944}, "pt_bosque": {"precision": 0.8185365853658536, "recall": 0.6905349794238683, "f1": 0.7491071428571429, "accuracy": 0.9733009708737864}, "sr_set": {"precision": 0.8207070707070707, "recall": 0.7674144037780402, "f1": 0.7931665649786455, "accuracy": 0.970492951580422}, "sk_snk": {"precision": 0.7124413145539906, "recall": 0.6633879781420765, "f1": 0.687040181097906, "accuracy": 0.9568153266331658}, "sv_talbanken": {"precision": 0.8082191780821918, "recall": 0.9030612244897959, "f1": 0.8530120481927711, "accuracy": 0.9972518035039505}}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1109857804
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size 1109857804
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 5304
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