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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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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: distilbert-base-uncased-finetuned-ner
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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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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: 0.5899772209567198
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- name: Recall
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type: recall
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value: 0.4117647058823529
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- name: F1
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type: f1
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value: 0.4850187265917604
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- name: Accuracy
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type: accuracy
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value: 0.9304392705585502
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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-base-uncased-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 wnut_17 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3202
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- Precision: 0.5900
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- Recall: 0.4118
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- F1: 0.4850
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- Accuracy: 0.9304
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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 | 0.3469 | 0.5480 | 0.2814 | 0.3718 | 0.9193 |
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| No log | 2.0 | 426 | 0.3135 | 0.5909 | 0.3903 | 0.4701 | 0.9281 |
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| 0.1903 | 3.0 | 639 | 0.3202 | 0.5900 | 0.4118 | 0.4850 | 0.9304 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.11.0
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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