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nerugm-lora-r4a0d0.05

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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/May24_21-54-09_indolem-petl-vm/events.out.tfevents.1716587656.indolem-petl-vm.1742936.0 +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-r4a0d0.05
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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-r4a0d0.05
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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: 0.1305
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+ - Precision: 0.7407
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+ - Recall: 0.8698
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+ - F1: 0.8001
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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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+ | 0.7682 | 1.0 | 528 | 0.4394 | 0.4048 | 0.1185 | 0.1834 | 0.8663 |
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+ | 0.3466 | 2.0 | 1056 | 0.2217 | 0.6022 | 0.7379 | 0.6632 | 0.9327 |
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+ | 0.2131 | 3.0 | 1584 | 0.1728 | 0.6765 | 0.8396 | 0.7493 | 0.9428 |
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+ | 0.1759 | 4.0 | 2112 | 0.1509 | 0.7221 | 0.8559 | 0.7833 | 0.9516 |
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+ | 0.1563 | 5.0 | 2640 | 0.1422 | 0.7303 | 0.8605 | 0.7901 | 0.9533 |
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+ | 0.1464 | 6.0 | 3168 | 0.1429 | 0.7202 | 0.8722 | 0.7890 | 0.9541 |
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+ | 0.1394 | 7.0 | 3696 | 0.1440 | 0.7153 | 0.8745 | 0.7869 | 0.9525 |
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+ | 0.1325 | 8.0 | 4224 | 0.1398 | 0.7274 | 0.8791 | 0.7961 | 0.9553 |
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+ | 0.1269 | 9.0 | 4752 | 0.1341 | 0.7420 | 0.8675 | 0.7999 | 0.9579 |
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+ | 0.124 | 10.0 | 5280 | 0.1331 | 0.7379 | 0.8768 | 0.8014 | 0.9565 |
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+ | 0.1194 | 11.0 | 5808 | 0.1329 | 0.7389 | 0.8815 | 0.8039 | 0.9569 |
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+ | 0.1171 | 12.0 | 6336 | 0.1337 | 0.7384 | 0.8791 | 0.8027 | 0.9567 |
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+ | 0.1153 | 13.0 | 6864 | 0.1294 | 0.7447 | 0.8745 | 0.8044 | 0.9587 |
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+ | 0.1119 | 14.0 | 7392 | 0.1310 | 0.7472 | 0.8791 | 0.8078 | 0.9573 |
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+ | 0.1109 | 15.0 | 7920 | 0.1312 | 0.7457 | 0.8722 | 0.8040 | 0.9579 |
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+ | 0.1102 | 16.0 | 8448 | 0.1309 | 0.7442 | 0.8791 | 0.8061 | 0.9581 |
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+ | 0.1095 | 17.0 | 8976 | 0.1314 | 0.7447 | 0.8815 | 0.8073 | 0.9587 |
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+ | 0.1073 | 18.0 | 9504 | 0.1323 | 0.7403 | 0.8745 | 0.8018 | 0.9577 |
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+ | 0.107 | 19.0 | 10032 | 0.1300 | 0.7407 | 0.8698 | 0.8001 | 0.9581 |
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+ | 0.1073 | 20.0 | 10560 | 0.1305 | 0.7407 | 0.8698 | 0.8001 | 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": 0,
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+ "architecture": "lora",
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+ "attn_matrices": [
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+ "q",
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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": 4,
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+ "selfattn_lora": true,
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+ "use_gating": false
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+ },
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+ "config_id": "0f1f8fd7ea3284ca",
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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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