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cehongw
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ner-fine-tune-bert-ner

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: dslim/bert-base-NER
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+ tags:
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+ - generated_from_trainer
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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: ner-fine-tune-bert-ner
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+ results: []
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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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+ # ner-fine-tune-bert-ner
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+
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+ This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3662
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+ - Precision: 0.2383
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+ - Recall: 0.2818
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+ - F1: 0.2582
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+ - Accuracy: 0.9406
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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: 1e-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: 30
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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 | 122 | 0.2295 | 0.1255 | 0.0716 | 0.0912 | 0.9514 |
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+ | No log | 2.0 | 244 | 0.2152 | 0.2022 | 0.1270 | 0.1560 | 0.9514 |
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+ | No log | 3.0 | 366 | 0.2044 | 0.1696 | 0.1547 | 0.1618 | 0.9497 |
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+ | No log | 4.0 | 488 | 0.2269 | 0.1980 | 0.1363 | 0.1614 | 0.9536 |
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+ | 0.2142 | 5.0 | 610 | 0.2335 | 0.1931 | 0.1547 | 0.1718 | 0.9521 |
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+ | 0.2142 | 6.0 | 732 | 0.2516 | 0.1959 | 0.1778 | 0.1864 | 0.9491 |
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+ | 0.2142 | 7.0 | 854 | 0.2446 | 0.2565 | 0.2517 | 0.2541 | 0.9542 |
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+ | 0.2142 | 8.0 | 976 | 0.2527 | 0.2273 | 0.2656 | 0.2449 | 0.9481 |
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+ | 0.0658 | 9.0 | 1098 | 0.2724 | 0.2459 | 0.2055 | 0.2239 | 0.9526 |
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+ | 0.0658 | 10.0 | 1220 | 0.2620 | 0.2895 | 0.2748 | 0.2820 | 0.9549 |
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+ | 0.0658 | 11.0 | 1342 | 0.2846 | 0.2102 | 0.2748 | 0.2382 | 0.9416 |
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+ | 0.0658 | 12.0 | 1464 | 0.2943 | 0.2292 | 0.2610 | 0.2441 | 0.9450 |
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+ | 0.0273 | 13.0 | 1586 | 0.3154 | 0.2064 | 0.2679 | 0.2332 | 0.9381 |
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+ | 0.0273 | 14.0 | 1708 | 0.3097 | 0.2254 | 0.2217 | 0.2235 | 0.9464 |
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+ | 0.0273 | 15.0 | 1830 | 0.3313 | 0.2375 | 0.2517 | 0.2444 | 0.9426 |
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+ | 0.0273 | 16.0 | 1952 | 0.3256 | 0.2098 | 0.2864 | 0.2422 | 0.9361 |
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+ | 0.0155 | 17.0 | 2074 | 0.3333 | 0.2162 | 0.2656 | 0.2383 | 0.9393 |
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+ | 0.0155 | 18.0 | 2196 | 0.3073 | 0.2446 | 0.2864 | 0.2638 | 0.9449 |
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+ | 0.0155 | 19.0 | 2318 | 0.3241 | 0.2418 | 0.2725 | 0.2562 | 0.9437 |
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+ | 0.0155 | 20.0 | 2440 | 0.3348 | 0.2338 | 0.2587 | 0.2456 | 0.9446 |
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+ | 0.0091 | 21.0 | 2562 | 0.3595 | 0.234 | 0.2702 | 0.2508 | 0.9402 |
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+ | 0.0091 | 22.0 | 2684 | 0.3658 | 0.2263 | 0.2818 | 0.2510 | 0.9387 |
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+ | 0.0091 | 23.0 | 2806 | 0.3495 | 0.2391 | 0.2794 | 0.2577 | 0.9419 |
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+ | 0.0091 | 24.0 | 2928 | 0.3545 | 0.2398 | 0.2841 | 0.2600 | 0.9409 |
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+ | 0.0066 | 25.0 | 3050 | 0.3557 | 0.2309 | 0.2864 | 0.2557 | 0.9402 |
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+ | 0.0066 | 26.0 | 3172 | 0.3498 | 0.2449 | 0.2748 | 0.2590 | 0.9432 |
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+ | 0.0066 | 27.0 | 3294 | 0.3586 | 0.2375 | 0.2841 | 0.2587 | 0.9416 |
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+ | 0.0066 | 28.0 | 3416 | 0.3676 | 0.2389 | 0.2725 | 0.2546 | 0.9417 |
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+ | 0.005 | 29.0 | 3538 | 0.3663 | 0.2412 | 0.2864 | 0.2619 | 0.9404 |
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+ | 0.005 | 30.0 | 3660 | 0.3662 | 0.2383 | 0.2818 | 0.2582 | 0.9406 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.1
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
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