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nerugm-lora-r8-2

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  1. README.md +85 -0
  2. nerugm-lora/adapter_config.json +26 -0
  3. nerugm-lora/head_config.json +23 -0
  4. nerugm-lora/pytorch_adapter.bin +3 -0
  5. nerugm-lora/pytorch_model_head.bin +3 -0
  6. runs/May27_05-16-45_indolem-petl-vm/events.out.tfevents.1716787012.indolem-petl-vm.3331769.0 +2 -2
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: indolem/indobert-base-uncased
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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: nerugm-lora-r8-2
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+ results: []
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+ ---
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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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+
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+ # nerugm-lora-r8-2
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+
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+ This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1743
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+ - Precision: 0.6820
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+ - Recall: 0.8289
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+ - F1: 0.7483
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+ - Accuracy: 0.9445
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 16
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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: 20.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.2665 | 1.0 | 106 | 0.7137 | 0.0 | 0.0 | 0.0 | 0.8449 |
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+ | 0.713 | 2.0 | 212 | 0.6075 | 0.0 | 0.0 | 0.0 | 0.8451 |
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+ | 0.6346 | 3.0 | 318 | 0.5231 | 0.1905 | 0.0118 | 0.0222 | 0.8494 |
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+ | 0.5555 | 4.0 | 424 | 0.4458 | 0.275 | 0.0649 | 0.1050 | 0.8656 |
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+ | 0.4696 | 5.0 | 530 | 0.3715 | 0.4802 | 0.2861 | 0.3586 | 0.8949 |
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+ | 0.3932 | 6.0 | 636 | 0.3134 | 0.5563 | 0.5251 | 0.5402 | 0.9194 |
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+ | 0.3299 | 7.0 | 742 | 0.2706 | 0.5968 | 0.6637 | 0.6285 | 0.9277 |
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+ | 0.2896 | 8.0 | 848 | 0.2433 | 0.62 | 0.7316 | 0.6712 | 0.9340 |
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+ | 0.2656 | 9.0 | 954 | 0.2277 | 0.6289 | 0.7699 | 0.6923 | 0.9355 |
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+ | 0.2442 | 10.0 | 1060 | 0.2082 | 0.6526 | 0.7758 | 0.7089 | 0.9387 |
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+ | 0.23 | 11.0 | 1166 | 0.2020 | 0.6390 | 0.7935 | 0.7079 | 0.9382 |
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+ | 0.2229 | 12.0 | 1272 | 0.1977 | 0.6524 | 0.8083 | 0.7220 | 0.9385 |
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+ | 0.2132 | 13.0 | 1378 | 0.1886 | 0.6602 | 0.8083 | 0.7268 | 0.9402 |
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+ | 0.2055 | 14.0 | 1484 | 0.1810 | 0.6708 | 0.7994 | 0.7295 | 0.9415 |
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+ | 0.2038 | 15.0 | 1590 | 0.1822 | 0.6595 | 0.8112 | 0.7275 | 0.9405 |
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+ | 0.2004 | 16.0 | 1696 | 0.1788 | 0.6731 | 0.8201 | 0.7394 | 0.9430 |
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+ | 0.1966 | 17.0 | 1802 | 0.1775 | 0.6731 | 0.8260 | 0.7417 | 0.9432 |
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+ | 0.1931 | 18.0 | 1908 | 0.1765 | 0.6683 | 0.8260 | 0.7388 | 0.9435 |
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+ | 0.1937 | 19.0 | 2014 | 0.1749 | 0.6747 | 0.8260 | 0.7427 | 0.9437 |
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+ | 0.1888 | 20.0 | 2120 | 0.1743 | 0.6820 | 0.8289 | 0.7483 | 0.9445 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.15.2
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+ "config": {
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+ "alpha": 8,
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+ "architecture": "lora",
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+ "composition_mode": "add",
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+ "dropout": 0.0,
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+ "init_weights": "lora",
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+ "intermediate_lora": false,
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+ "leave_out": [],
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+ "output_lora": false,
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+ "r": 8,
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+ "selfattn_lora": true,
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+ "use_gating": false
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+ },
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+ "config_id": "625403edad0bf919",
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+ "hidden_size": 768,
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+ "model_class": "BertForTokenClassification",
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+ "model_name": "indolem/indobert-base-uncased",
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+ "model_type": "bert",
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+ "name": "nerugm-lora",
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+ "version": "0.2.0"
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+ }
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+ "model_class": "BertForTokenClassification",
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+ "model_name": "indolem/indobert-base-uncased",
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+ "model_type": "bert",
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+ "name": null,
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+ "num_labels": 11,
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+ "version": "0.2.0"
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+ }
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