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bert-base-uncased-tajik-ner

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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: bert-base-uncased-tajik-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: 0.5042016806722689
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+ - name: Recall
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+ type: recall
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+ value: 0.5769230769230769
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+ - name: F1
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+ type: f1
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+ value: 0.5381165919282511
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.848129958443521
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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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+ # bert-base-uncased-tajik-ner
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+
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the wikiann dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2137
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+ - Precision: 0.5042
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+ - Recall: 0.5769
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+ - F1: 0.5381
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+ - Accuracy: 0.8481
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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 | 0.9499 | 0.0450 | 0.0962 | 0.0613 | 0.6626 |
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+ | No log | 4.0 | 100 | 0.7348 | 0.1549 | 0.2115 | 0.1789 | 0.7401 |
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+ | No log | 6.0 | 150 | 0.6685 | 0.1916 | 0.3077 | 0.2362 | 0.8017 |
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+ | No log | 8.0 | 200 | 0.7875 | 0.3923 | 0.4904 | 0.4359 | 0.8036 |
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+ | No log | 10.0 | 250 | 0.7495 | 0.4225 | 0.5769 | 0.4878 | 0.8274 |
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+ | No log | 12.0 | 300 | 0.8934 | 0.4198 | 0.5288 | 0.4681 | 0.8085 |
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+ | No log | 14.0 | 350 | 0.9455 | 0.4758 | 0.5673 | 0.5175 | 0.8236 |
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+ | No log | 16.0 | 400 | 0.9469 | 0.5893 | 0.6346 | 0.6111 | 0.8410 |
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+ | No log | 18.0 | 450 | 0.9936 | 0.5333 | 0.6154 | 0.5714 | 0.8485 |
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+ | 0.2726 | 20.0 | 500 | 0.9804 | 0.5 | 0.6058 | 0.5478 | 0.8519 |
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+ | 0.2726 | 22.0 | 550 | 1.1035 | 0.5963 | 0.625 | 0.6103 | 0.8432 |
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+ | 0.2726 | 24.0 | 600 | 1.0318 | 0.5856 | 0.625 | 0.6047 | 0.8576 |
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+ | 0.2726 | 26.0 | 650 | 1.1820 | 0.4921 | 0.5962 | 0.5391 | 0.8221 |
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+ | 0.2726 | 28.0 | 700 | 1.1204 | 0.4878 | 0.5769 | 0.5286 | 0.8311 |
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+ | 0.2726 | 30.0 | 750 | 1.1911 | 0.5357 | 0.5769 | 0.5556 | 0.8376 |
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+ | 0.2726 | 32.0 | 800 | 1.1747 | 0.5259 | 0.5865 | 0.5545 | 0.8394 |
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+ | 0.2726 | 34.0 | 850 | 1.1403 | 0.5872 | 0.6154 | 0.6009 | 0.8542 |
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+ | 0.2726 | 36.0 | 900 | 1.1824 | 0.5370 | 0.5577 | 0.5472 | 0.8330 |
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+ | 0.2726 | 38.0 | 950 | 1.1467 | 0.5424 | 0.6154 | 0.5766 | 0.8440 |
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+ | 0.003 | 40.0 | 1000 | 1.2148 | 0.5268 | 0.5673 | 0.5463 | 0.8360 |
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+ | 0.003 | 42.0 | 1050 | 1.3478 | 0.5273 | 0.5577 | 0.5421 | 0.8266 |
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+ | 0.003 | 44.0 | 1100 | 1.2137 | 0.5042 | 0.5769 | 0.5381 | 0.8481 |
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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