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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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<!-- 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-uncased-tajik-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 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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## 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: 200 |
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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 | 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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### Framework versions |
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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 |
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