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---
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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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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: my_ner_model
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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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# my_ner_model
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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: 0.2692
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- Precision: 0.5427
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- Recall: 0.3299
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- F1: 0.4104
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- Accuracy: 0.9440
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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: 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: 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 | 213 | 0.2727 | 0.6394 | 0.2678 | 0.3775 | 0.9402 |
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| No log | 2.0 | 426 | 0.2651 | 0.5677 | 0.3188 | 0.4083 | 0.9428 |
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| 0.1748 | 3.0 | 639 | 0.2692 | 0.5427 | 0.3299 | 0.4104 | 0.9440 |
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.3.0+cu118
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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