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README.md
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---
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base_model: distilbert-base-uncased
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library_name: peft
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license: apache-2.0
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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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tags:
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- generated_from_trainer
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model-index:
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- name: distilbert-ner-lorafinetune-runs-v1
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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-lorafinetune-runs-v1
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0735
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- Precision: 0.9638
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- Recall: 0.9778
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- F1: 0.9708
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- Accuracy: 0.9888
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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: 16
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- eval_batch_size: 16
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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: 4
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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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| 0.0808 | 1.0 | 2643 | 0.1186 | 0.9399 | 0.9629 | 0.9513 | 0.9818 |
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| 0.0648 | 2.0 | 5286 | 0.0807 | 0.9556 | 0.9736 | 0.9645 | 0.9868 |
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| 0.0366 | 3.0 | 7929 | 0.0761 | 0.9611 | 0.9770 | 0.9690 | 0.9883 |
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| 0.0306 | 4.0 | 10572 | 0.0735 | 0.9638 | 0.9778 | 0.9708 | 0.9888 |
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### Framework versions
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- PEFT 0.12.0
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- Transformers 4.43.3
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- Pytorch 2.4.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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