Initial Commit
Browse files- README.md +60 -60
- config.json +2 -2
- eval_result_ner.json +1 -1
- model.safetensors +2 -2
- training_args.bin +1 -1
README.md
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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-pre-ner-full-xlmr_data-univner_half44
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results: []
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# scenario-non-kd-pre-ner-full-xlmr_data-univner_half44
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This model is a fine-tuned version of [
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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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- 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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### 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_half44
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results: []
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# scenario-non-kd-pre-ner-full-xlmr_data-univner_half44
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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: 0.1692
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- Precision: 0.7997
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- Recall: 0.8083
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- F1: 0.8040
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- Accuracy: 0.9794
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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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| 0.1456 | 0.5828 | 500 | 0.0795 | 0.7321 | 0.7445 | 0.7383 | 0.9744 |
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| 0.0686 | 1.1655 | 1000 | 0.0786 | 0.7478 | 0.7850 | 0.7660 | 0.9763 |
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| 0.0505 | 1.7483 | 1500 | 0.0765 | 0.7499 | 0.8058 | 0.7768 | 0.9775 |
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| 0.039 | 2.3310 | 2000 | 0.0850 | 0.7407 | 0.8025 | 0.7704 | 0.9759 |
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| 0.0308 | 2.9138 | 2500 | 0.0935 | 0.7340 | 0.8178 | 0.7736 | 0.9755 |
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| 0.0222 | 3.4965 | 3000 | 0.0941 | 0.7587 | 0.8097 | 0.7834 | 0.9776 |
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| 0.0202 | 4.0793 | 3500 | 0.0978 | 0.7615 | 0.8139 | 0.7868 | 0.9781 |
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| 0.0149 | 4.6620 | 4000 | 0.1010 | 0.7738 | 0.8039 | 0.7886 | 0.9780 |
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| 0.0134 | 5.2448 | 4500 | 0.1072 | 0.7917 | 0.7973 | 0.7945 | 0.9791 |
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| 0.0112 | 5.8275 | 5000 | 0.1023 | 0.7866 | 0.7948 | 0.7907 | 0.9789 |
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| 0.0091 | 6.4103 | 5500 | 0.1151 | 0.7765 | 0.8083 | 0.7921 | 0.9784 |
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| 0.0088 | 6.9930 | 6000 | 0.1144 | 0.7838 | 0.7980 | 0.7908 | 0.9785 |
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| 0.0077 | 7.5758 | 6500 | 0.1150 | 0.7748 | 0.7979 | 0.7862 | 0.9780 |
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| 0.0067 | 8.1585 | 7000 | 0.1191 | 0.7889 | 0.7960 | 0.7924 | 0.9787 |
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| 0.0059 | 8.7413 | 7500 | 0.1269 | 0.7703 | 0.8140 | 0.7916 | 0.9780 |
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| 0.0053 | 9.3240 | 8000 | 0.1267 | 0.7857 | 0.7944 | 0.7900 | 0.9784 |
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| 0.0053 | 9.9068 | 8500 | 0.1273 | 0.7957 | 0.7928 | 0.7942 | 0.9788 |
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| 0.0043 | 10.4895 | 9000 | 0.1321 | 0.7772 | 0.7990 | 0.7879 | 0.9782 |
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| 0.0039 | 11.0723 | 9500 | 0.1313 | 0.7940 | 0.7945 | 0.7943 | 0.9789 |
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| 0.0033 | 11.6550 | 10000 | 0.1361 | 0.7964 | 0.8031 | 0.7997 | 0.9788 |
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| 0.0033 | 12.2378 | 10500 | 0.1394 | 0.7828 | 0.8057 | 0.7941 | 0.9785 |
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| 0.0032 | 12.8205 | 11000 | 0.1445 | 0.7760 | 0.7928 | 0.7843 | 0.9781 |
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| 0.0029 | 13.4033 | 11500 | 0.1358 | 0.7833 | 0.8083 | 0.7956 | 0.9789 |
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| 0.003 | 13.9860 | 12000 | 0.1383 | 0.7830 | 0.8031 | 0.7929 | 0.9787 |
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| 0.0024 | 14.5688 | 12500 | 0.1403 | 0.7731 | 0.8130 | 0.7925 | 0.9779 |
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| 0.0028 | 15.1515 | 13000 | 0.1423 | 0.7998 | 0.7956 | 0.7977 | 0.9789 |
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| 0.0021 | 15.7343 | 13500 | 0.1443 | 0.7831 | 0.8062 | 0.7945 | 0.9785 |
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| 0.002 | 16.3170 | 14000 | 0.1396 | 0.7815 | 0.8088 | 0.7950 | 0.9785 |
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| 0.0018 | 16.8998 | 14500 | 0.1469 | 0.7808 | 0.8072 | 0.7938 | 0.9783 |
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| 0.0019 | 17.4825 | 15000 | 0.1450 | 0.7969 | 0.8029 | 0.7999 | 0.9790 |
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| 0.0018 | 18.0653 | 15500 | 0.1516 | 0.7906 | 0.8098 | 0.8001 | 0.9788 |
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| 0.0011 | 18.6480 | 16000 | 0.1591 | 0.7810 | 0.8093 | 0.7949 | 0.9787 |
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| 0.0014 | 19.2308 | 16500 | 0.1561 | 0.8002 | 0.8051 | 0.8026 | 0.9791 |
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| 0.0011 | 19.8135 | 17000 | 0.1568 | 0.7901 | 0.8093 | 0.7996 | 0.9788 |
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| 0.0013 | 20.3963 | 17500 | 0.1573 | 0.7980 | 0.7950 | 0.7965 | 0.9790 |
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| 0.001 | 20.9790 | 18000 | 0.1566 | 0.7987 | 0.7915 | 0.7951 | 0.9787 |
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| 0.001 | 21.5618 | 18500 | 0.1594 | 0.7926 | 0.8041 | 0.7983 | 0.9787 |
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| 0.001 | 22.1445 | 19000 | 0.1616 | 0.8068 | 0.7899 | 0.7983 | 0.9790 |
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| 0.0011 | 22.7273 | 19500 | 0.1644 | 0.7908 | 0.8098 | 0.8002 | 0.9788 |
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| 0.0007 | 23.3100 | 20000 | 0.1628 | 0.7870 | 0.8018 | 0.7943 | 0.9788 |
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| 0.0008 | 23.8928 | 20500 | 0.1615 | 0.7960 | 0.7995 | 0.7977 | 0.9787 |
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| 0.0006 | 24.4755 | 21000 | 0.1614 | 0.8002 | 0.7993 | 0.7998 | 0.9790 |
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| 0.0007 | 25.0583 | 21500 | 0.1612 | 0.8001 | 0.7979 | 0.7990 | 0.9791 |
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| 0.0005 | 25.6410 | 22000 | 0.1627 | 0.7940 | 0.8104 | 0.8021 | 0.9791 |
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| 0.0005 | 26.2238 | 22500 | 0.1664 | 0.7945 | 0.8062 | 0.8003 | 0.9793 |
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| 0.0005 | 26.8065 | 23000 | 0.1663 | 0.7871 | 0.8175 | 0.8020 | 0.9792 |
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| 0.0004 | 27.3893 | 23500 | 0.1691 | 0.8037 | 0.8091 | 0.8064 | 0.9796 |
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| 0.0005 | 27.9720 | 24000 | 0.1684 | 0.7979 | 0.8087 | 0.8032 | 0.9794 |
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| 0.0003 | 28.5548 | 24500 | 0.1686 | 0.7999 | 0.8064 | 0.8031 | 0.9793 |
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| 0.0004 | 29.1375 | 25000 | 0.1692 | 0.8005 | 0.8081 | 0.8043 | 0.9794 |
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| 0.0003 | 29.7203 | 25500 | 0.1692 | 0.7997 | 0.8083 | 0.8040 | 0.9794 |
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### Framework versions
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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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":
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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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"_name_or_path": "FacebookAI/xlm-roberta-base",
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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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",
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eval_result_ner.json
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{"ceb_gja": {"precision": 0.
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{"ceb_gja": {"precision": 0.44155844155844154, "recall": 0.6938775510204082, "f1": 0.5396825396825397, "accuracy": 0.9536679536679536}, "en_pud": {"precision": 0.7821393523061825, "recall": 0.7413953488372093, "f1": 0.761222540592168, "accuracy": 0.9770494899886664}, "de_pud": {"precision": 0.7226415094339622, "recall": 0.737247353224254, "f1": 0.7298713673177702, "accuracy": 0.9713093619614646}, "pt_pud": {"precision": 0.8009478672985783, "recall": 0.7688808007279345, "f1": 0.7845868152274839, "accuracy": 0.9790233690776263}, "ru_pud": {"precision": 0.6575984990619137, "recall": 0.6766409266409267, "f1": 0.6669838249286395, "accuracy": 0.967036941358822}, "sv_pud": {"precision": 0.8170974155069582, "recall": 0.7988338192419825, "f1": 0.8078624078624078, "accuracy": 0.980866009645628}, "tl_trg": {"precision": 0.7307692307692307, "recall": 0.8260869565217391, "f1": 0.7755102040816326, "accuracy": 0.9850136239782016}, "tl_ugnayan": {"precision": 0.5, "recall": 0.6363636363636364, "f1": 0.56, "accuracy": 0.9662716499544212}, "zh_gsd": {"precision": 0.8070866141732284, "recall": 0.8018252933507171, "f1": 0.8044473512099413, "accuracy": 0.9731102231102231}, "zh_gsdsimp": {"precision": 0.7866666666666666, "recall": 0.7732634338138925, "f1": 0.7799074686054197, "accuracy": 0.970945720945721}, "hr_set": {"precision": 0.88, "recall": 0.8937990021382751, "f1": 0.8868458274398867, "accuracy": 0.9869332234130256}, "da_ddt": {"precision": 0.8029556650246306, "recall": 0.7293064876957495, "f1": 0.7643610785463072, "accuracy": 0.9820413049985034}, "en_ewt": {"precision": 0.7806324110671937, "recall": 0.7261029411764706, "f1": 0.7523809523809524, "accuracy": 0.9753356974937244}, "pt_bosque": {"precision": 0.7879078694817658, "recall": 0.6757201646090535, "f1": 0.7275143996455471, "accuracy": 0.9738443703811042}, "sr_set": {"precision": 0.8980070339976554, "recall": 0.9043683589138135, "f1": 0.9011764705882354, "accuracy": 0.9861658348655985}, "sk_snk": {"precision": 0.6898096304591266, "recall": 0.673224043715847, "f1": 0.6814159292035399, "accuracy": 0.9559516331658291}, "sv_talbanken": {"precision": 0.7918552036199095, "recall": 0.8928571428571429, "f1": 0.8393285371702638, "accuracy": 0.9969573538793738}}
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model.safetensors
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training_args.bin
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