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README.md
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---
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library_name: peft
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- conll2002
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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: distilbert-ner-qlorafinetune-runs
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results: []
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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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# distilbert-ner-qlorafinetune-runs
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2002 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2164
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- Precision: 0.6299
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- Recall: 0.6227
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- F1: 0.6263
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- Accuracy: 0.9372
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0004
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- training_steps: 640
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 1.0711 | 0.0766 | 20 | 0.7111 | 0.0 | 0.0 | 0.0 | 0.8570 |
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| 0.5291 | 0.1533 | 40 | 0.5467 | 0.0 | 0.0 | 0.0 | 0.8570 |
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| 0.4545 | 0.2299 | 60 | 0.4850 | 0.2172 | 0.1687 | 0.1899 | 0.8769 |
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| 0.4113 | 0.3065 | 80 | 0.4470 | 0.3227 | 0.1765 | 0.2282 | 0.8816 |
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| 0.3837 | 0.3831 | 100 | 0.4049 | 0.4187 | 0.3840 | 0.4006 | 0.8896 |
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| 0.334 | 0.4598 | 120 | 0.3639 | 0.4695 | 0.4276 | 0.4476 | 0.8981 |
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| 0.3342 | 0.5364 | 140 | 0.3499 | 0.5104 | 0.4520 | 0.4794 | 0.8997 |
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| 0.322 | 0.6130 | 160 | 0.3281 | 0.4939 | 0.4920 | 0.4929 | 0.9091 |
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| 0.2868 | 0.6897 | 180 | 0.3021 | 0.5207 | 0.4646 | 0.4911 | 0.9145 |
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| 0.2788 | 0.7663 | 200 | 0.2878 | 0.5361 | 0.5064 | 0.5209 | 0.9185 |
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| 0.2748 | 0.8429 | 220 | 0.2864 | 0.5419 | 0.5232 | 0.5324 | 0.9197 |
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| 0.2435 | 0.9195 | 240 | 0.2750 | 0.5306 | 0.5294 | 0.5300 | 0.9205 |
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| 0.238 | 0.9962 | 260 | 0.2636 | 0.5525 | 0.5623 | 0.5573 | 0.9239 |
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| 0.2465 | 1.0728 | 280 | 0.2616 | 0.5574 | 0.5602 | 0.5588 | 0.9255 |
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| 0.2296 | 1.1494 | 300 | 0.2607 | 0.5859 | 0.5409 | 0.5625 | 0.9252 |
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| 0.2141 | 1.2261 | 320 | 0.2491 | 0.5728 | 0.5841 | 0.5784 | 0.9279 |
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| 0.2229 | 1.3027 | 340 | 0.2483 | 0.5849 | 0.5767 | 0.5808 | 0.9289 |
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| 0.2234 | 1.3793 | 360 | 0.2413 | 0.5906 | 0.5712 | 0.5808 | 0.9310 |
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| 0.2217 | 1.4559 | 380 | 0.2416 | 0.5890 | 0.5944 | 0.5917 | 0.9321 |
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| 0.208 | 1.5326 | 400 | 0.2337 | 0.6117 | 0.5889 | 0.6001 | 0.9326 |
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| 0.1961 | 1.6092 | 420 | 0.2387 | 0.5950 | 0.6018 | 0.5984 | 0.9321 |
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| 0.2237 | 1.6858 | 440 | 0.2263 | 0.6230 | 0.6094 | 0.6161 | 0.9353 |
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| 0.2029 | 1.7625 | 460 | 0.2262 | 0.6377 | 0.6045 | 0.6207 | 0.9353 |
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| 0.203 | 1.8391 | 480 | 0.2229 | 0.6246 | 0.6167 | 0.6206 | 0.9358 |
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| 0.2098 | 1.9157 | 500 | 0.2221 | 0.6277 | 0.6264 | 0.6270 | 0.9363 |
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| 0.1907 | 1.9923 | 520 | 0.2237 | 0.6197 | 0.6186 | 0.6191 | 0.9355 |
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| 0.1774 | 2.0690 | 540 | 0.2214 | 0.6284 | 0.6170 | 0.6226 | 0.9365 |
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| 0.1822 | 2.1456 | 560 | 0.2213 | 0.6267 | 0.6211 | 0.6239 | 0.9368 |
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| 0.1783 | 2.2222 | 580 | 0.2180 | 0.6308 | 0.6266 | 0.6287 | 0.9371 |
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| 0.1856 | 2.2989 | 600 | 0.2174 | 0.6289 | 0.6206 | 0.6247 | 0.9369 |
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| 0.1773 | 2.3755 | 620 | 0.2172 | 0.6192 | 0.6278 | 0.6235 | 0.9362 |
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| 0.1647 | 2.4521 | 640 | 0.2164 | 0.6299 | 0.6227 | 0.6263 | 0.9372 |
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### Framework versions
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- PEFT 0.13.2
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- Transformers 4.46.3
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- Pytorch 2.5.1
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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