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bert-base-cased-finetuned-ner

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Files changed (8) hide show
  1. README.md +129 -0
  2. config.json +72 -0
  3. pytorch_model.bin +3 -0
  4. special_tokens_map.json +7 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +13 -0
  7. training_args.bin +3 -0
  8. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: bert-base-cased
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - harem
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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-cased-finetuned-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: harem
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+ type: harem
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+ config: default
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+ split: validation
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+ args: default
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.3251366120218579
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+ - name: Recall
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+ type: recall
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+ value: 0.34097421203438394
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+ - name: F1
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+ type: f1
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+ value: 0.3328671328671328
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8684278684278685
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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-cased-finetuned-ner
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+
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+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the harem dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5103
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+ - Precision: 0.3251
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+ - Recall: 0.3410
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+ - F1: 0.3329
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+ - Accuracy: 0.8684
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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: 32
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+ - eval_batch_size: 32
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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: 40
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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 | 1.0 | 4 | 1.1734 | 0.0 | 0.0 | 0.0 | 0.8083 |
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+ | No log | 2.0 | 8 | 0.9781 | 0.0 | 0.0 | 0.0 | 0.8086 |
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+ | No log | 3.0 | 12 | 0.8915 | 0.0 | 0.0 | 0.0 | 0.8086 |
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+ | No log | 4.0 | 16 | 0.7901 | 0.0 | 0.0 | 0.0 | 0.8086 |
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+ | No log | 5.0 | 20 | 0.7202 | 0.0 | 0.0 | 0.0 | 0.8086 |
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+ | No log | 6.0 | 24 | 0.6846 | 0.4286 | 0.0344 | 0.0637 | 0.8130 |
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+ | No log | 7.0 | 28 | 0.6596 | 0.2014 | 0.0802 | 0.1148 | 0.8306 |
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+ | No log | 8.0 | 32 | 0.6355 | 0.1615 | 0.0745 | 0.1020 | 0.8324 |
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+ | No log | 9.0 | 36 | 0.6193 | 0.1571 | 0.0946 | 0.1181 | 0.8345 |
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+ | No log | 10.0 | 40 | 0.6106 | 0.1295 | 0.1032 | 0.1148 | 0.8335 |
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+ | No log | 11.0 | 44 | 0.5919 | 0.1680 | 0.1232 | 0.1421 | 0.8350 |
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+ | No log | 12.0 | 48 | 0.5789 | 0.2051 | 0.1375 | 0.1647 | 0.8384 |
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+ | No log | 13.0 | 52 | 0.5827 | 0.1611 | 0.1375 | 0.1484 | 0.8355 |
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+ | No log | 14.0 | 56 | 0.5638 | 0.2281 | 0.1862 | 0.2050 | 0.8433 |
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+ | No log | 15.0 | 60 | 0.5576 | 0.1879 | 0.1691 | 0.1780 | 0.8420 |
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+ | No log | 16.0 | 64 | 0.5485 | 0.2110 | 0.1862 | 0.1979 | 0.8456 |
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+ | No log | 17.0 | 68 | 0.5479 | 0.2401 | 0.2264 | 0.2330 | 0.8500 |
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+ | No log | 18.0 | 72 | 0.5460 | 0.2406 | 0.2378 | 0.2392 | 0.8503 |
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+ | No log | 19.0 | 76 | 0.5374 | 0.2531 | 0.2350 | 0.2437 | 0.8542 |
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+ | No log | 20.0 | 80 | 0.5365 | 0.2364 | 0.2493 | 0.2427 | 0.8539 |
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+ | No log | 21.0 | 84 | 0.5284 | 0.2462 | 0.2350 | 0.2405 | 0.8552 |
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+ | No log | 22.0 | 88 | 0.5306 | 0.2812 | 0.2837 | 0.2825 | 0.8601 |
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+ | No log | 23.0 | 92 | 0.5262 | 0.2722 | 0.2722 | 0.2722 | 0.8573 |
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+ | No log | 24.0 | 96 | 0.5306 | 0.2447 | 0.2665 | 0.2551 | 0.8555 |
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+ | No log | 25.0 | 100 | 0.5249 | 0.2785 | 0.3009 | 0.2893 | 0.8594 |
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+ | No log | 26.0 | 104 | 0.5201 | 0.2801 | 0.2865 | 0.2833 | 0.8586 |
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+ | No log | 27.0 | 108 | 0.5213 | 0.2806 | 0.2894 | 0.2849 | 0.8604 |
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+ | No log | 28.0 | 112 | 0.5207 | 0.2732 | 0.2951 | 0.2837 | 0.8612 |
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+ | No log | 29.0 | 116 | 0.5144 | 0.3027 | 0.3209 | 0.3115 | 0.8630 |
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+ | No log | 30.0 | 120 | 0.5135 | 0.3073 | 0.3381 | 0.3220 | 0.8648 |
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+ | No log | 31.0 | 124 | 0.5147 | 0.2953 | 0.3266 | 0.3102 | 0.8651 |
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+ | No log | 32.0 | 128 | 0.5121 | 0.2937 | 0.3181 | 0.3054 | 0.8645 |
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+ | No log | 33.0 | 132 | 0.5092 | 0.3061 | 0.3324 | 0.3187 | 0.8645 |
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+ | No log | 34.0 | 136 | 0.5064 | 0.3342 | 0.3696 | 0.3510 | 0.8677 |
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+ | No log | 35.0 | 140 | 0.5056 | 0.3191 | 0.3438 | 0.3310 | 0.8674 |
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+ | No log | 36.0 | 144 | 0.5091 | 0.3023 | 0.3352 | 0.3179 | 0.8661 |
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+ | No log | 37.0 | 148 | 0.5104 | 0.3061 | 0.3324 | 0.3187 | 0.8658 |
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+ | No log | 38.0 | 152 | 0.5100 | 0.3152 | 0.3324 | 0.3236 | 0.8677 |
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+ | No log | 39.0 | 156 | 0.5102 | 0.3243 | 0.3410 | 0.3324 | 0.8684 |
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+ | No log | 40.0 | 160 | 0.5103 | 0.3251 | 0.3410 | 0.3329 | 0.8684 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.1
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+ - Pytorch 2.0.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.13.3
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+ "BertForTokenClassification"
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tokenizer.json ADDED
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