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nerugm-lora-r8a1d0.1

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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/May25_02-30-15_indolem-petl-vm/events.out.tfevents.1716604222.indolem-petl-vm.1865599.0 +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-r8a1d0.1
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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-r8a1d0.1
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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: 0.1278
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+ - Precision: 0.7600
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+ - Recall: 0.8815
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+ - F1: 0.8162
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+ - Accuracy: 0.9593
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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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+ | 0.713 | 1.0 | 528 | 0.3558 | 0.4950 | 0.3736 | 0.4258 | 0.8990 |
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+ | 0.2793 | 2.0 | 1056 | 0.1931 | 0.6472 | 0.8048 | 0.7174 | 0.9392 |
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+ | 0.1876 | 3.0 | 1584 | 0.1619 | 0.6758 | 0.8466 | 0.7516 | 0.9462 |
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+ | 0.1593 | 4.0 | 2112 | 0.1416 | 0.7366 | 0.8629 | 0.7948 | 0.9555 |
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+ | 0.1412 | 5.0 | 2640 | 0.1350 | 0.7386 | 0.8652 | 0.7969 | 0.9559 |
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+ | 0.1325 | 6.0 | 3168 | 0.1361 | 0.7324 | 0.8698 | 0.7952 | 0.9555 |
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+ | 0.126 | 7.0 | 3696 | 0.1383 | 0.7310 | 0.8698 | 0.7944 | 0.9553 |
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+ | 0.1194 | 8.0 | 4224 | 0.1349 | 0.7456 | 0.8838 | 0.8088 | 0.9583 |
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+ | 0.1137 | 9.0 | 4752 | 0.1299 | 0.7495 | 0.8745 | 0.8072 | 0.9583 |
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+ | 0.1112 | 10.0 | 5280 | 0.1285 | 0.7455 | 0.8698 | 0.8029 | 0.9579 |
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+ | 0.1065 | 11.0 | 5808 | 0.1304 | 0.7525 | 0.8815 | 0.8119 | 0.9587 |
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+ | 0.1044 | 12.0 | 6336 | 0.1329 | 0.7520 | 0.8791 | 0.8106 | 0.9577 |
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+ | 0.1026 | 13.0 | 6864 | 0.1257 | 0.7520 | 0.8722 | 0.8076 | 0.9585 |
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+ | 0.0989 | 14.0 | 7392 | 0.1265 | 0.7626 | 0.8791 | 0.8167 | 0.9599 |
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+ | 0.0982 | 15.0 | 7920 | 0.1281 | 0.7631 | 0.8815 | 0.8180 | 0.9597 |
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+ | 0.0974 | 16.0 | 8448 | 0.1264 | 0.7515 | 0.8768 | 0.8093 | 0.9597 |
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+ | 0.0966 | 17.0 | 8976 | 0.1282 | 0.7545 | 0.8838 | 0.8140 | 0.9589 |
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+ | 0.095 | 18.0 | 9504 | 0.1292 | 0.7570 | 0.8815 | 0.8145 | 0.9589 |
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+ | 0.0941 | 19.0 | 10032 | 0.1268 | 0.7585 | 0.8815 | 0.8154 | 0.9595 |
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+ | 0.0948 | 20.0 | 10560 | 0.1278 | 0.7600 | 0.8815 | 0.8162 | 0.9593 |
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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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+ {
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+ "config": {
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+ "alpha": 1,
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+ "architecture": "lora",
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+ "attn_matrices": [
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+ "q",
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+ "v"
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+ ],
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+ "composition_mode": "add",
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+ "dropout": 0.1,
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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": "54b8f479420ff12a",
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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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