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End of training
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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-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- wnut_17
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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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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: wnut_17
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type: wnut_17
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config: wnut_17
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split: test
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args: wnut_17
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metrics:
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- name: Precision
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type: precision
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value: 0.576214405360134
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- name: Recall
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type: recall
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value: 0.31881371640407785
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- name: F1
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type: f1
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value: 0.41050119331742246
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- name: Accuracy
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type: accuracy
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value: 0.94258475482023
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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 the wnut_17 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2722
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- Precision: 0.5762
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- Recall: 0.3188
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- F1: 0.4105
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- Accuracy: 0.9426
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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: 2
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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 | 0.2801 | 0.5214 | 0.2373 | 0.3261 | 0.9384 |
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| No log | 2.0 | 426 | 0.2722 | 0.5762 | 0.3188 | 0.4105 | 0.9426 |
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### Framework versions
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- Transformers 4.45.0
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- Pytorch 2.4.1+cpu
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- Datasets 3.0.0
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- Tokenizers 0.20.0
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