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update model card README.md
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README.md
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---
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license: apache-2.0
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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: bert-finetuned-ner
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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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# bert-finetuned-ner
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2036
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- Precision: 0.6420
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- Recall: 0.6613
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- F1: 0.6515
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- Accuracy: 0.9421
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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: 32
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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: 3
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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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| No log | 1.0 | 261 | 0.2696 | 0.5284 | 0.5958 | 0.5601 | 0.9222 |
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| 0.2809 | 2.0 | 522 | 0.2086 | 0.6354 | 0.6464 | 0.6408 | 0.9399 |
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| 0.2809 | 3.0 | 783 | 0.2036 | 0.6420 | 0.6613 | 0.6515 | 0.9421 |
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### Framework versions
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- Transformers 4.30.1
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- Pytorch 2.0.1+cu117
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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