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nerugm-lora-r4a0d0.15

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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/May24_22-36-40_indolem-petl-vm/events.out.tfevents.1716590207.indolem-petl-vm.1767874.0 +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-r4a0d0.15
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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-r4a0d0.15
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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: 0.1301
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+ - Precision: 0.7357
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+ - Recall: 0.8652
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+ - F1: 0.7952
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+ - Accuracy: 0.9577
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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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+ | 0.7663 | 1.0 | 528 | 0.4380 | 0.3934 | 0.1116 | 0.1738 | 0.8659 |
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+ | 0.3481 | 2.0 | 1056 | 0.2220 | 0.6018 | 0.7403 | 0.6639 | 0.9339 |
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+ | 0.2139 | 3.0 | 1584 | 0.1790 | 0.6561 | 0.8327 | 0.7339 | 0.9400 |
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+ | 0.1777 | 4.0 | 2112 | 0.1535 | 0.7164 | 0.8559 | 0.7800 | 0.9512 |
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+ | 0.1578 | 5.0 | 2640 | 0.1445 | 0.7367 | 0.8698 | 0.7978 | 0.9535 |
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+ | 0.1469 | 6.0 | 3168 | 0.1441 | 0.7139 | 0.8745 | 0.7861 | 0.9535 |
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+ | 0.1399 | 7.0 | 3696 | 0.1453 | 0.7175 | 0.8838 | 0.7920 | 0.9524 |
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+ | 0.1333 | 8.0 | 4224 | 0.1403 | 0.7298 | 0.8838 | 0.7995 | 0.9547 |
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+ | 0.1273 | 9.0 | 4752 | 0.1368 | 0.7387 | 0.8722 | 0.7999 | 0.9563 |
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+ | 0.1246 | 10.0 | 5280 | 0.1342 | 0.7426 | 0.8768 | 0.8042 | 0.9569 |
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+ | 0.1195 | 11.0 | 5808 | 0.1351 | 0.7359 | 0.8791 | 0.8012 | 0.9571 |
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+ | 0.1172 | 12.0 | 6336 | 0.1349 | 0.7373 | 0.8791 | 0.8020 | 0.9573 |
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+ | 0.1155 | 13.0 | 6864 | 0.1296 | 0.7441 | 0.8768 | 0.8050 | 0.9581 |
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+ | 0.1118 | 14.0 | 7392 | 0.1302 | 0.7367 | 0.8698 | 0.7978 | 0.9577 |
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+ | 0.1111 | 15.0 | 7920 | 0.1322 | 0.7426 | 0.8768 | 0.8042 | 0.9577 |
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+ | 0.1097 | 16.0 | 8448 | 0.1303 | 0.7353 | 0.8698 | 0.7969 | 0.9577 |
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+ | 0.1094 | 17.0 | 8976 | 0.1306 | 0.7343 | 0.8722 | 0.7973 | 0.9573 |
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+ | 0.1077 | 18.0 | 9504 | 0.1319 | 0.7372 | 0.8722 | 0.7990 | 0.9577 |
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+ | 0.1065 | 19.0 | 10032 | 0.1296 | 0.7376 | 0.8675 | 0.7973 | 0.9577 |
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+ | 0.1078 | 20.0 | 10560 | 0.1301 | 0.7357 | 0.8652 | 0.7952 | 0.9577 |
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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": 0.15,
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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": 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": "6d5614c18adc2c4b",
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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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