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nerugm-lora-r2a1d0.1

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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/May24_20-05-52_indolem-petl-vm/events.out.tfevents.1716581159.indolem-petl-vm.1684364.0 +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-r2a1d0.1
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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-r2a1d0.1
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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: 0.1332
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+ - Precision: 0.7287
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+ - Recall: 0.8536
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+ - F1: 0.7862
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+ - Accuracy: 0.9555
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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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+ | 0.7886 | 1.0 | 528 | 0.4607 | 0.3243 | 0.0837 | 0.1330 | 0.8597 |
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+ | 0.3911 | 2.0 | 1056 | 0.2542 | 0.6081 | 0.6915 | 0.6471 | 0.9293 |
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+ | 0.2384 | 3.0 | 1584 | 0.1934 | 0.6527 | 0.7937 | 0.7163 | 0.9376 |
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+ | 0.1934 | 4.0 | 2112 | 0.1678 | 0.6880 | 0.8187 | 0.7477 | 0.9446 |
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+ | 0.172 | 5.0 | 2640 | 0.1589 | 0.6901 | 0.8373 | 0.7566 | 0.9468 |
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+ | 0.1602 | 6.0 | 3168 | 0.1533 | 0.6931 | 0.8489 | 0.7631 | 0.9488 |
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+ | 0.1532 | 7.0 | 3696 | 0.1505 | 0.6935 | 0.8559 | 0.7662 | 0.9498 |
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+ | 0.1457 | 8.0 | 4224 | 0.1456 | 0.7103 | 0.8536 | 0.7754 | 0.9522 |
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+ | 0.1401 | 9.0 | 4752 | 0.1418 | 0.7301 | 0.8536 | 0.7870 | 0.9543 |
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+ | 0.1375 | 10.0 | 5280 | 0.1388 | 0.7308 | 0.8582 | 0.7894 | 0.9551 |
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+ | 0.1331 | 11.0 | 5808 | 0.1360 | 0.7308 | 0.8582 | 0.7894 | 0.9555 |
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+ | 0.1304 | 12.0 | 6336 | 0.1365 | 0.7258 | 0.8536 | 0.7845 | 0.9549 |
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+ | 0.1285 | 13.0 | 6864 | 0.1343 | 0.7380 | 0.8512 | 0.7906 | 0.9559 |
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+ | 0.1255 | 14.0 | 7392 | 0.1345 | 0.7401 | 0.8605 | 0.7958 | 0.9559 |
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+ | 0.1249 | 15.0 | 7920 | 0.1346 | 0.7332 | 0.8605 | 0.7918 | 0.9549 |
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+ | 0.1238 | 16.0 | 8448 | 0.1342 | 0.7307 | 0.8559 | 0.7883 | 0.9551 |
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+ | 0.1232 | 17.0 | 8976 | 0.1342 | 0.7326 | 0.8582 | 0.7905 | 0.9557 |
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+ | 0.1215 | 18.0 | 9504 | 0.1351 | 0.7317 | 0.8605 | 0.7909 | 0.9549 |
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+ | 0.1209 | 19.0 | 10032 | 0.1337 | 0.7278 | 0.8559 | 0.7866 | 0.9547 |
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+ | 0.1207 | 20.0 | 10560 | 0.1332 | 0.7287 | 0.8536 | 0.7862 | 0.9555 |
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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": 2,
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+ "selfattn_lora": true,
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+ "use_gating": false
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+ },
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+ "config_id": "f6811b537fd15e16",
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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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+ "I-TIME": 9,
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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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