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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: distilbert/distilbert-base-cased
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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: distillbert-base-cased-finetuned-ner2
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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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# distillbert-base-cased-finetuned-ner2
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This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1556
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- Precision: 0.7479
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- Recall: 0.7873
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- F1: 0.7671
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- Accuracy: 0.9518
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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: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 5
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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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| 0.2026 | 1.0 | 4750 | 0.1952 | 0.6895 | 0.7367 | 0.7123 | 0.9429 |
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| 0.1637 | 2.0 | 9500 | 0.1681 | 0.7358 | 0.7743 | 0.7546 | 0.9491 |
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| 0.1525 | 3.0 | 14250 | 0.1584 | 0.7448 | 0.7859 | 0.7648 | 0.9513 |
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| 0.1487 | 4.0 | 19000 | 0.1558 | 0.7463 | 0.7866 | 0.7659 | 0.9516 |
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| 0.1523 | 5.0 | 23750 | 0.1556 | 0.7479 | 0.7873 | 0.7671 | 0.9518 |
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### Framework versions
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- Transformers 4.50.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.4.1
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- Tokenizers 0.21.1
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