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nerugm-lora-r2a0d0.05

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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_18-39-08_indolem-petl-vm/events.out.tfevents.1716575957.indolem-petl-vm.1631081.0 +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-r2a0d0.05
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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-r2a0d0.05
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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: 0.1346
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+ - Precision: 0.7366
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+ - Recall: 0.8629
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+ - F1: 0.7948
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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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+ | 0.7885 | 1.0 | 528 | 0.4616 | 0.3182 | 0.0813 | 0.1296 | 0.8599 |
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+ | 0.3921 | 2.0 | 1056 | 0.2524 | 0.6053 | 0.6798 | 0.6404 | 0.9273 |
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+ | 0.2392 | 3.0 | 1584 | 0.1932 | 0.6500 | 0.7844 | 0.7109 | 0.9382 |
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+ | 0.1931 | 4.0 | 2112 | 0.1676 | 0.6905 | 0.8234 | 0.7511 | 0.9444 |
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+ | 0.1719 | 5.0 | 2640 | 0.1583 | 0.7056 | 0.8396 | 0.7668 | 0.9478 |
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+ | 0.1602 | 6.0 | 3168 | 0.1539 | 0.7115 | 0.8582 | 0.7780 | 0.9502 |
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+ | 0.1533 | 7.0 | 3696 | 0.1520 | 0.7031 | 0.8629 | 0.7748 | 0.9506 |
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+ | 0.1455 | 8.0 | 4224 | 0.1456 | 0.7263 | 0.8559 | 0.7858 | 0.9525 |
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+ | 0.1398 | 9.0 | 4752 | 0.1425 | 0.7301 | 0.8536 | 0.7870 | 0.9537 |
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+ | 0.1368 | 10.0 | 5280 | 0.1395 | 0.7229 | 0.8536 | 0.7828 | 0.9533 |
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+ | 0.1331 | 11.0 | 5808 | 0.1365 | 0.7360 | 0.8536 | 0.7904 | 0.9551 |
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+ | 0.1305 | 12.0 | 6336 | 0.1377 | 0.7332 | 0.8605 | 0.7918 | 0.9549 |
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+ | 0.1279 | 13.0 | 6864 | 0.1357 | 0.7415 | 0.8582 | 0.7956 | 0.9565 |
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+ | 0.1251 | 14.0 | 7392 | 0.1355 | 0.7371 | 0.8652 | 0.7960 | 0.9555 |
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+ | 0.1239 | 15.0 | 7920 | 0.1359 | 0.7366 | 0.8629 | 0.7948 | 0.9549 |
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+ | 0.1231 | 16.0 | 8448 | 0.1347 | 0.7351 | 0.8629 | 0.7939 | 0.9551 |
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+ | 0.122 | 17.0 | 8976 | 0.1353 | 0.7351 | 0.8629 | 0.7939 | 0.9555 |
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+ | 0.1205 | 18.0 | 9504 | 0.1356 | 0.7317 | 0.8605 | 0.7909 | 0.9549 |
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+ | 0.1202 | 19.0 | 10032 | 0.1347 | 0.7351 | 0.8629 | 0.7939 | 0.9551 |
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+ | 0.1204 | 20.0 | 10560 | 0.1346 | 0.7366 | 0.8629 | 0.7948 | 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": 0,
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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": 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": "8106482e5aeaad89",
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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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