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nerugm-lora-r8a1d0.15

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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_02-51-28_indolem-petl-vm/events.out.tfevents.1716605495.indolem-petl-vm.1878228.0 +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-r8a1d0.15
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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-r8a1d0.15
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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: 0.1281
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+ - Precision: 0.7470
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+ - Recall: 0.8629
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+ - F1: 0.8008
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+ - Accuracy: 0.9579
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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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+ | 0.7018 | 1.0 | 528 | 0.3353 | 0.5529 | 0.4800 | 0.5138 | 0.9115 |
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+ | 0.2639 | 2.0 | 1056 | 0.1912 | 0.6494 | 0.8210 | 0.7252 | 0.9412 |
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+ | 0.1862 | 3.0 | 1584 | 0.1672 | 0.6739 | 0.8536 | 0.7531 | 0.9466 |
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+ | 0.1612 | 4.0 | 2112 | 0.1446 | 0.7238 | 0.8512 | 0.7824 | 0.9539 |
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+ | 0.1439 | 5.0 | 2640 | 0.1390 | 0.7254 | 0.8582 | 0.7863 | 0.9545 |
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+ | 0.1358 | 6.0 | 3168 | 0.1392 | 0.7256 | 0.8652 | 0.7893 | 0.9551 |
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+ | 0.129 | 7.0 | 3696 | 0.1384 | 0.7267 | 0.8698 | 0.7919 | 0.9561 |
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+ | 0.1228 | 8.0 | 4224 | 0.1339 | 0.7353 | 0.8698 | 0.7969 | 0.9575 |
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+ | 0.1168 | 9.0 | 4752 | 0.1321 | 0.7439 | 0.8559 | 0.7960 | 0.9577 |
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+ | 0.1146 | 10.0 | 5280 | 0.1300 | 0.7445 | 0.8582 | 0.7973 | 0.9581 |
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+ | 0.1105 | 11.0 | 5808 | 0.1327 | 0.7333 | 0.8675 | 0.7948 | 0.9571 |
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+ | 0.1083 | 12.0 | 6336 | 0.1333 | 0.7342 | 0.8652 | 0.7943 | 0.9569 |
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+ | 0.106 | 13.0 | 6864 | 0.1265 | 0.7490 | 0.8582 | 0.7999 | 0.9591 |
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+ | 0.1032 | 14.0 | 7392 | 0.1269 | 0.7445 | 0.8582 | 0.7973 | 0.9589 |
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+ | 0.1023 | 15.0 | 7920 | 0.1291 | 0.7455 | 0.8629 | 0.7999 | 0.9585 |
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+ | 0.1014 | 16.0 | 8448 | 0.1271 | 0.7400 | 0.8582 | 0.7947 | 0.9575 |
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+ | 0.1002 | 17.0 | 8976 | 0.1281 | 0.7460 | 0.8722 | 0.8042 | 0.9589 |
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+ | 0.0986 | 18.0 | 9504 | 0.1304 | 0.7416 | 0.8722 | 0.8016 | 0.9573 |
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+ | 0.0978 | 19.0 | 10032 | 0.1271 | 0.7520 | 0.8652 | 0.8046 | 0.9589 |
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+ | 0.0984 | 20.0 | 10560 | 0.1281 | 0.7470 | 0.8629 | 0.8008 | 0.9579 |
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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": 0.15,
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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": "d7a928f95cb5f33d",
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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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