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nerugm-lora-r16-4

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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_05-39-56_indolem-petl-vm/events.out.tfevents.1716788403.indolem-petl-vm.3345068.0 +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-r16-4
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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-r16-4
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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: 0.1644
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+ - Precision: 0.7143
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+ - Recall: 0.8689
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+ - F1: 0.7841
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+ - Accuracy: 0.9498
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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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+ | 1.1516 | 1.0 | 106 | 0.7191 | 0.0 | 0.0 | 0.0 | 0.8366 |
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+ | 0.6846 | 2.0 | 212 | 0.6016 | 0.0909 | 0.0028 | 0.0055 | 0.8383 |
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+ | 0.5728 | 3.0 | 318 | 0.4865 | 0.2195 | 0.0513 | 0.0831 | 0.8536 |
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+ | 0.4602 | 4.0 | 424 | 0.3772 | 0.4132 | 0.2849 | 0.3373 | 0.8875 |
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+ | 0.3718 | 5.0 | 530 | 0.3013 | 0.5613 | 0.5869 | 0.5738 | 0.9222 |
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+ | 0.3063 | 6.0 | 636 | 0.2611 | 0.6124 | 0.7293 | 0.6658 | 0.9323 |
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+ | 0.2645 | 7.0 | 742 | 0.2272 | 0.6315 | 0.7664 | 0.6924 | 0.9367 |
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+ | 0.2422 | 8.0 | 848 | 0.2093 | 0.6343 | 0.7806 | 0.6999 | 0.9399 |
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+ | 0.2207 | 9.0 | 954 | 0.1960 | 0.6465 | 0.7920 | 0.7119 | 0.9417 |
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+ | 0.2079 | 10.0 | 1060 | 0.1873 | 0.6714 | 0.8091 | 0.7339 | 0.9427 |
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+ | 0.1973 | 11.0 | 1166 | 0.1791 | 0.6713 | 0.8205 | 0.7385 | 0.9439 |
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+ | 0.1889 | 12.0 | 1272 | 0.1790 | 0.6720 | 0.8348 | 0.7446 | 0.9446 |
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+ | 0.1812 | 13.0 | 1378 | 0.1712 | 0.6936 | 0.8319 | 0.7565 | 0.9461 |
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+ | 0.1803 | 14.0 | 1484 | 0.1708 | 0.6907 | 0.8462 | 0.7606 | 0.9461 |
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+ | 0.1754 | 15.0 | 1590 | 0.1712 | 0.6935 | 0.8575 | 0.7669 | 0.9466 |
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+ | 0.1709 | 16.0 | 1696 | 0.1684 | 0.6943 | 0.8604 | 0.7684 | 0.9473 |
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+ | 0.169 | 17.0 | 1802 | 0.1676 | 0.7044 | 0.8689 | 0.7781 | 0.9486 |
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+ | 0.1681 | 18.0 | 1908 | 0.1679 | 0.7044 | 0.8689 | 0.7781 | 0.9486 |
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+ | 0.1655 | 19.0 | 2014 | 0.1645 | 0.7160 | 0.8689 | 0.7851 | 0.9500 |
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+ | 0.1654 | 20.0 | 2120 | 0.1644 | 0.7143 | 0.8689 | 0.7841 | 0.9498 |
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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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+ "output_lora": false,
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+ "r": 16,
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+ "selfattn_lora": true,
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+ "use_gating": false
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+ },
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+ "config_id": "141b248112091265",
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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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