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bert-small-uncased-tajik-ner

Token Classification
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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-small-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: 0.43884892086330934
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+ - name: Recall
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+ type: recall
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+ value: 0.5865384615384616
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+ - name: F1
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+ type: f1
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+ value: 0.5020576131687243
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8269739327540612
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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-small-uncased-tajik-ner
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+
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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 wikiann dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1663
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+ - Precision: 0.4388
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+ - Recall: 0.5865
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+ - F1: 0.5021
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+ - Accuracy: 0.8270
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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 | 1.1113 | 0.0238 | 0.0481 | 0.0318 | 0.5984 |
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+ | No log | 4.0 | 100 | 0.9179 | 0.0976 | 0.1538 | 0.1194 | 0.6547 |
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+ | No log | 6.0 | 150 | 0.9254 | 0.08 | 0.1538 | 0.1053 | 0.6634 |
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+ | No log | 8.0 | 200 | 0.6607 | 0.1299 | 0.2212 | 0.1637 | 0.7707 |
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+ | No log | 10.0 | 250 | 0.6514 | 0.2583 | 0.375 | 0.3059 | 0.7896 |
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+ | No log | 12.0 | 300 | 0.6213 | 0.2836 | 0.3654 | 0.3193 | 0.8058 |
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+ | No log | 14.0 | 350 | 0.6696 | 0.3611 | 0.5 | 0.4194 | 0.8100 |
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+ | No log | 16.0 | 400 | 0.7094 | 0.3893 | 0.4904 | 0.4340 | 0.8187 |
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+ | No log | 18.0 | 450 | 0.7557 | 0.38 | 0.5481 | 0.4488 | 0.8243 |
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+ | 0.5061 | 20.0 | 500 | 0.7409 | 0.4222 | 0.5481 | 0.4770 | 0.8342 |
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+ | 0.5061 | 22.0 | 550 | 0.8003 | 0.4196 | 0.5769 | 0.4858 | 0.8349 |
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+ | 0.5061 | 24.0 | 600 | 0.8173 | 0.4275 | 0.5673 | 0.4876 | 0.8342 |
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+ | 0.5061 | 26.0 | 650 | 0.7942 | 0.4225 | 0.5769 | 0.4878 | 0.8323 |
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+ | 0.5061 | 28.0 | 700 | 0.8565 | 0.4067 | 0.5865 | 0.4803 | 0.8281 |
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+ | 0.5061 | 30.0 | 750 | 0.8040 | 0.4388 | 0.5865 | 0.5021 | 0.8406 |
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+ | 0.5061 | 32.0 | 800 | 0.9251 | 0.4286 | 0.5769 | 0.4918 | 0.8368 |
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+ | 0.5061 | 34.0 | 850 | 0.8421 | 0.4196 | 0.5769 | 0.4858 | 0.8394 |
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+ | 0.5061 | 36.0 | 900 | 0.8608 | 0.4207 | 0.5865 | 0.4900 | 0.8330 |
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+ | 0.5061 | 38.0 | 950 | 0.8622 | 0.5333 | 0.6154 | 0.5714 | 0.8489 |
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+ | 0.0304 | 40.0 | 1000 | 0.9901 | 0.4306 | 0.5962 | 0.5000 | 0.8240 |
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+ | 0.0304 | 42.0 | 1050 | 0.9677 | 0.4286 | 0.6058 | 0.5020 | 0.8345 |
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+ | 0.0304 | 44.0 | 1100 | 0.9203 | 0.4429 | 0.5962 | 0.5082 | 0.8440 |
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+ | 0.0304 | 46.0 | 1150 | 0.9368 | 0.4559 | 0.5962 | 0.5167 | 0.8428 |
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+ | 0.0304 | 48.0 | 1200 | 0.9747 | 0.4420 | 0.5865 | 0.5041 | 0.8342 |
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+ | 0.0304 | 50.0 | 1250 | 0.9033 | 0.4266 | 0.5865 | 0.4939 | 0.8360 |
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+ | 0.0304 | 52.0 | 1300 | 0.9242 | 0.4806 | 0.5962 | 0.5322 | 0.8519 |
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+ | 0.0304 | 54.0 | 1350 | 0.9496 | 0.4150 | 0.5865 | 0.4861 | 0.8406 |
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+ | 0.0304 | 56.0 | 1400 | 1.0157 | 0.4388 | 0.5865 | 0.5021 | 0.8274 |
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+ | 0.0304 | 58.0 | 1450 | 1.0069 | 0.3789 | 0.5865 | 0.4604 | 0.8357 |
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+ | 0.0041 | 60.0 | 1500 | 1.0159 | 0.4593 | 0.5962 | 0.5188 | 0.8413 |
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+ | 0.0041 | 62.0 | 1550 | 1.0138 | 0.488 | 0.5865 | 0.5328 | 0.8428 |
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+ | 0.0041 | 64.0 | 1600 | 1.0406 | 0.4526 | 0.5962 | 0.5145 | 0.8398 |
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+ | 0.0041 | 66.0 | 1650 | 1.0672 | 0.504 | 0.6058 | 0.5502 | 0.8413 |
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+ | 0.0041 | 68.0 | 1700 | 1.0713 | 0.4257 | 0.6058 | 0.5 | 0.8334 |
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+ | 0.0041 | 70.0 | 1750 | 1.0001 | 0.5079 | 0.6154 | 0.5565 | 0.8515 |
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+ | 0.0041 | 72.0 | 1800 | 0.9986 | 0.4632 | 0.6058 | 0.525 | 0.8451 |
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+ | 0.0041 | 74.0 | 1850 | 1.0523 | 0.4643 | 0.625 | 0.5328 | 0.8357 |
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+ | 0.0041 | 76.0 | 1900 | 1.1331 | 0.4437 | 0.6058 | 0.5122 | 0.8281 |
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+ | 0.0041 | 78.0 | 1950 | 1.0217 | 0.4667 | 0.6058 | 0.5272 | 0.8406 |
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+ | 0.0023 | 80.0 | 2000 | 1.0296 | 0.4519 | 0.5865 | 0.5105 | 0.8372 |
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+ | 0.0023 | 82.0 | 2050 | 1.0603 | 0.5207 | 0.6058 | 0.56 | 0.8512 |
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+ | 0.0023 | 84.0 | 2100 | 1.1181 | 0.4733 | 0.5962 | 0.5277 | 0.8319 |
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+ | 0.0023 | 86.0 | 2150 | 1.0858 | 0.4701 | 0.6058 | 0.5294 | 0.8383 |
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+ | 0.0023 | 88.0 | 2200 | 1.0947 | 0.4779 | 0.625 | 0.5417 | 0.8394 |
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+ | 0.0023 | 90.0 | 2250 | 1.0671 | 0.4539 | 0.6154 | 0.5224 | 0.8391 |
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+ | 0.0023 | 92.0 | 2300 | 1.0958 | 0.4444 | 0.6154 | 0.5161 | 0.8372 |
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+ | 0.0023 | 94.0 | 2350 | 1.1221 | 0.4397 | 0.5962 | 0.5061 | 0.8319 |
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+ | 0.0023 | 96.0 | 2400 | 1.0861 | 0.5 | 0.6058 | 0.5478 | 0.8508 |
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+ | 0.0023 | 98.0 | 2450 | 1.1522 | 0.4545 | 0.5769 | 0.5085 | 0.8258 |
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+ | 0.0015 | 100.0 | 2500 | 1.1426 | 0.4688 | 0.5769 | 0.5172 | 0.8304 |
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+ | 0.0015 | 102.0 | 2550 | 1.1663 | 0.4388 | 0.5865 | 0.5021 | 0.8270 |
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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