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--- |
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license: mit |
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base_model: microsoft/Multilingual-MiniLM-L12-H384 |
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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: ner-coin |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ner-coin |
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This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0015 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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- F1: 1.0 |
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- Accuracy: 1.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 27 | 0.1989 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 2.0 | 54 | 0.1611 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 3.0 | 81 | 0.1334 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 4.0 | 108 | 0.1116 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 5.0 | 135 | 0.0943 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 6.0 | 162 | 0.0804 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 7.0 | 189 | 0.0692 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 8.0 | 216 | 0.0602 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 9.0 | 243 | 0.0528 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 10.0 | 270 | 0.0468 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 11.0 | 297 | 0.0418 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 12.0 | 324 | 0.0376 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 13.0 | 351 | 0.0341 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 14.0 | 378 | 0.0312 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 15.0 | 405 | 0.0287 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 16.0 | 432 | 0.0266 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 17.0 | 459 | 0.0247 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| No log | 18.0 | 486 | 0.0236 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| 0.0904 | 19.0 | 513 | 0.0218 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| 0.0904 | 20.0 | 540 | 0.0203 | 0.0 | 0.0 | 0.0 | 0.9989 | |
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| 0.0904 | 21.0 | 567 | 0.0156 | 0.8571 | 0.8571 | 0.8571 | 0.9997 | |
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| 0.0904 | 22.0 | 594 | 0.0142 | 1.0 | 0.8571 | 0.9231 | 0.9998 | |
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| 0.0904 | 23.0 | 621 | 0.0133 | 1.0 | 0.8571 | 0.9231 | 0.9998 | |
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| 0.0904 | 24.0 | 648 | 0.0122 | 1.0 | 0.8571 | 0.9231 | 0.9998 | |
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| 0.0904 | 25.0 | 675 | 0.0107 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 26.0 | 702 | 0.0099 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 27.0 | 729 | 0.0092 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 28.0 | 756 | 0.0086 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 29.0 | 783 | 0.0081 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 30.0 | 810 | 0.0076 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 31.0 | 837 | 0.0074 | 1.0 | 1.0 | 1.0 | 0.9998 | |
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| 0.0904 | 32.0 | 864 | 0.0073 | 1.0 | 0.8571 | 0.9231 | 0.9998 | |
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| 0.0904 | 33.0 | 891 | 0.0064 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 34.0 | 918 | 0.0061 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 35.0 | 945 | 0.0058 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 36.0 | 972 | 0.0055 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0904 | 37.0 | 999 | 0.0053 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 38.0 | 1026 | 0.0050 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 39.0 | 1053 | 0.0048 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 40.0 | 1080 | 0.0046 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 41.0 | 1107 | 0.0044 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 42.0 | 1134 | 0.0042 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 43.0 | 1161 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 44.0 | 1188 | 0.0039 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 45.0 | 1215 | 0.0038 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 46.0 | 1242 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 47.0 | 1269 | 0.0035 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 48.0 | 1296 | 0.0034 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 49.0 | 1323 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 50.0 | 1350 | 0.0032 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 51.0 | 1377 | 0.0031 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 52.0 | 1404 | 0.0030 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 53.0 | 1431 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 54.0 | 1458 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0122 | 55.0 | 1485 | 0.0028 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 56.0 | 1512 | 0.0027 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 57.0 | 1539 | 0.0026 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 58.0 | 1566 | 0.0026 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 59.0 | 1593 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 60.0 | 1620 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 61.0 | 1647 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 62.0 | 1674 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 63.0 | 1701 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 64.0 | 1728 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 65.0 | 1755 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 66.0 | 1782 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 67.0 | 1809 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 68.0 | 1836 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 69.0 | 1863 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 70.0 | 1890 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 71.0 | 1917 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 72.0 | 1944 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 73.0 | 1971 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0042 | 74.0 | 1998 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 75.0 | 2025 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 76.0 | 2052 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 77.0 | 2079 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 78.0 | 2106 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 79.0 | 2133 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 80.0 | 2160 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 81.0 | 2187 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 82.0 | 2214 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 83.0 | 2241 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 84.0 | 2268 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 85.0 | 2295 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 86.0 | 2322 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 87.0 | 2349 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 88.0 | 2376 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 89.0 | 2403 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 90.0 | 2430 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 91.0 | 2457 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0025 | 92.0 | 2484 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 93.0 | 2511 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 94.0 | 2538 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 95.0 | 2565 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 96.0 | 2592 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 97.0 | 2619 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 98.0 | 2646 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 99.0 | 2673 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0019 | 100.0 | 2700 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
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