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nerugm-lora-r8a1d0.05

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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_02-09-00_indolem-petl-vm/events.out.tfevents.1716602948.indolem-petl-vm.1863087.0 +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-r8a1d0.05
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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-r8a1d0.05
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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: 0.1266
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+ - Precision: 0.7622
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+ - Recall: 0.8698
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+ - F1: 0.8125
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+ - Accuracy: 0.9591
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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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+ | 0.7039 | 1.0 | 528 | 0.3293 | 0.5553 | 0.4962 | 0.5241 | 0.9123 |
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+ | 0.2536 | 2.0 | 1056 | 0.1835 | 0.6530 | 0.8210 | 0.7274 | 0.9424 |
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+ | 0.1831 | 3.0 | 1584 | 0.1832 | 0.6678 | 0.8210 | 0.7365 | 0.9440 |
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+ | 0.1623 | 4.0 | 2112 | 0.1463 | 0.7213 | 0.8466 | 0.7789 | 0.9535 |
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+ | 0.1439 | 5.0 | 2640 | 0.1387 | 0.7173 | 0.8420 | 0.7747 | 0.9541 |
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+ | 0.1348 | 6.0 | 3168 | 0.1383 | 0.7256 | 0.8652 | 0.7893 | 0.9553 |
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+ | 0.1293 | 7.0 | 3696 | 0.1394 | 0.7242 | 0.8652 | 0.7885 | 0.9545 |
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+ | 0.124 | 8.0 | 4224 | 0.1351 | 0.7353 | 0.8698 | 0.7969 | 0.9569 |
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+ | 0.1176 | 9.0 | 4752 | 0.1304 | 0.7404 | 0.8536 | 0.7930 | 0.9561 |
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+ | 0.1153 | 10.0 | 5280 | 0.1278 | 0.7582 | 0.8582 | 0.8051 | 0.9585 |
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+ | 0.111 | 11.0 | 5808 | 0.1304 | 0.7386 | 0.8652 | 0.7969 | 0.9579 |
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+ | 0.109 | 12.0 | 6336 | 0.1323 | 0.7415 | 0.8652 | 0.7986 | 0.9565 |
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+ | 0.1077 | 13.0 | 6864 | 0.1253 | 0.7649 | 0.8675 | 0.8130 | 0.9597 |
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+ | 0.1032 | 14.0 | 7392 | 0.1243 | 0.7639 | 0.8629 | 0.8104 | 0.9593 |
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+ | 0.1035 | 15.0 | 7920 | 0.1261 | 0.7664 | 0.8675 | 0.8138 | 0.9597 |
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+ | 0.1017 | 16.0 | 8448 | 0.1258 | 0.7470 | 0.8559 | 0.7977 | 0.9577 |
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+ | 0.1004 | 17.0 | 8976 | 0.1278 | 0.7576 | 0.8698 | 0.8098 | 0.9589 |
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+ | 0.099 | 18.0 | 9504 | 0.1284 | 0.7510 | 0.8675 | 0.8051 | 0.9585 |
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+ | 0.0991 | 19.0 | 10032 | 0.1256 | 0.7572 | 0.8605 | 0.8055 | 0.9581 |
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+ | 0.0984 | 20.0 | 10560 | 0.1266 | 0.7622 | 0.8698 | 0.8125 | 0.9591 |
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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": 0.05,
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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": "bb949181a19f3fc7",
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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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+ "O": 10
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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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