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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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datasets:
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- wikiann
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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: mbert-finetuned-azerbaijani-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: wikiann
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type: wikiann
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args: az
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metrics:
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- name: Precision
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type: precision
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value: 0.8898541731306236
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- name: Recall
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type: recall
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value: 0.915416533673795
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- name: F1
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type: f1
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value: 0.9024543738200126
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- name: Accuracy
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type: accuracy
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value: 0.966948310139165
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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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# mbert-finetuned-azerbaijani-ner
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1385
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- Precision: 0.8899
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- Recall: 0.9154
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- F1: 0.9025
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- Accuracy: 0.9669
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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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| 0.2928 | 1.0 | 625 | 0.1415 | 0.8584 | 0.8918 | 0.8748 | 0.9595 |
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| 0.1254 | 2.0 | 1250 | 0.1335 | 0.8875 | 0.9119 | 0.8996 | 0.9637 |
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| 0.077 | 3.0 | 1875 | 0.1385 | 0.8899 | 0.9154 | 0.9025 | 0.9669 |
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### Framework versions
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- Transformers 4.16.2
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- Pytorch 1.10.0+cu111
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- Datasets 1.18.3
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- Tokenizers 0.11.6
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