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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: bert-small-finetuned-wnut17-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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config: wnut_17
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split: train
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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: 0.6259259259259259
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- name: Recall
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type: recall
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value: 0.4043062200956938
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- name: F1
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type: f1
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value: 0.49127906976744184
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- name: Accuracy
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type: accuracy
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value: 0.9255075123293955
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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-small-finetuned-wnut17-ner
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This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the wnut_17 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3649
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- Precision: 0.6259
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- Recall: 0.4043
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- F1: 0.4913
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- Accuracy: 0.9255
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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: 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 | 425 | 0.3578 | 0.6382 | 0.3481 | 0.4505 | 0.9229 |
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| 0.2359 | 2.0 | 850 | 0.3708 | 0.6535 | 0.3768 | 0.4780 | 0.9245 |
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| 0.1231 | 3.0 | 1275 | 0.3649 | 0.6259 | 0.4043 | 0.4913 | 0.9255 |
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### Framework versions
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- Transformers 4.21.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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