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muhtasham
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tajberto-ner

Token Classification
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+ ---
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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: tajberto-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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+ config: tg
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+ split: train+test
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+ args: tg
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.576
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+ - name: Recall
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+ type: recall
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+ value: 0.6923076923076923
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+ - name: F1
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+ type: f1
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+ value: 0.62882096069869
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8934049079754601
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+ ---
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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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+
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+ # tajberto-ner
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+
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+ This model is a fine-tuned version of [muhtasham/TajBERTo](https://huggingface.co/muhtasham/TajBERTo) on the wikiann dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6129
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+ - Precision: 0.576
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+ - Recall: 0.6923
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+ - F1: 0.6288
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+ - Accuracy: 0.8934
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 200
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 2.0 | 50 | 0.6171 | 0.1667 | 0.2885 | 0.2113 | 0.7646 |
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+ | No log | 4.0 | 100 | 0.4733 | 0.2824 | 0.4615 | 0.3504 | 0.8344 |
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+ | No log | 6.0 | 150 | 0.3857 | 0.3372 | 0.5577 | 0.4203 | 0.8589 |
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+ | No log | 8.0 | 200 | 0.4523 | 0.4519 | 0.5865 | 0.5105 | 0.8765 |
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+ | No log | 10.0 | 250 | 0.3870 | 0.44 | 0.6346 | 0.5197 | 0.8834 |
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+ | No log | 12.0 | 300 | 0.4512 | 0.5267 | 0.6635 | 0.5872 | 0.8865 |
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+ | No log | 14.0 | 350 | 0.4934 | 0.4789 | 0.6538 | 0.5528 | 0.8819 |
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+ | No log | 16.0 | 400 | 0.4924 | 0.4783 | 0.6346 | 0.5455 | 0.8842 |
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+ | No log | 18.0 | 450 | 0.5355 | 0.4595 | 0.6538 | 0.5397 | 0.8788 |
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+ | 0.1682 | 20.0 | 500 | 0.5440 | 0.5547 | 0.6827 | 0.6121 | 0.8942 |
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+ | 0.1682 | 22.0 | 550 | 0.5299 | 0.5794 | 0.7019 | 0.6348 | 0.9003 |
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+ | 0.1682 | 24.0 | 600 | 0.5735 | 0.5691 | 0.6731 | 0.6167 | 0.8926 |
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+ | 0.1682 | 26.0 | 650 | 0.6027 | 0.5833 | 0.6731 | 0.6250 | 0.8796 |
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+ | 0.1682 | 28.0 | 700 | 0.6119 | 0.568 | 0.6827 | 0.6201 | 0.8934 |
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+ | 0.1682 | 30.0 | 750 | 0.6098 | 0.5635 | 0.6827 | 0.6174 | 0.8911 |
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+ | 0.1682 | 32.0 | 800 | 0.6237 | 0.5469 | 0.6731 | 0.6034 | 0.8834 |
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+ | 0.1682 | 34.0 | 850 | 0.6215 | 0.5530 | 0.7019 | 0.6186 | 0.8842 |
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+ | 0.1682 | 36.0 | 900 | 0.6179 | 0.5802 | 0.7308 | 0.6468 | 0.8888 |
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+ | 0.1682 | 38.0 | 950 | 0.6201 | 0.5373 | 0.6923 | 0.6050 | 0.8873 |
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+ | 0.0007 | 40.0 | 1000 | 0.6114 | 0.5952 | 0.7212 | 0.6522 | 0.8911 |
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+ | 0.0007 | 42.0 | 1050 | 0.6073 | 0.5625 | 0.6923 | 0.6207 | 0.8896 |
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+ | 0.0007 | 44.0 | 1100 | 0.6327 | 0.5620 | 0.6538 | 0.6044 | 0.8896 |
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+ | 0.0007 | 46.0 | 1150 | 0.6129 | 0.576 | 0.6923 | 0.6288 | 0.8934 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.2
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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