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PassbyGrocer
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hreb-msra

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
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Files changed (8) hide show
  1. README.md +166 -0
  2. config.json +52 -0
  3. model.safetensors +3 -0
  4. special_tokens_map.json +37 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +56 -0
  7. training_args.bin +3 -0
  8. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: hfl/chinese-roberta-wwm-ext-large
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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: robert_bilstm_mega_res-ner-msra-ner-ner-msra-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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+ # robert_bilstm_mega_res-ner-msra-ner-ner-msra-ner
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+
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+ This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0621
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+ - Precision: 0.9538
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+ - Recall: 0.9573
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+ - F1: 0.9555
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+ - Accuracy: 0.9940
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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: 5e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 100
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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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+ | 0.0239 | 1.0 | 725 | 0.0232 | 0.9242 | 0.9344 | 0.9293 | 0.9931 |
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+ | 0.0139 | 2.0 | 1450 | 0.0254 | 0.9373 | 0.9459 | 0.9416 | 0.9925 |
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+ | 0.006 | 3.0 | 2175 | 0.0294 | 0.9415 | 0.9480 | 0.9448 | 0.9930 |
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+ | 0.0052 | 4.0 | 2900 | 0.0303 | 0.9389 | 0.9486 | 0.9437 | 0.9937 |
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+ | 0.0049 | 5.0 | 3625 | 0.0303 | 0.9422 | 0.9498 | 0.9459 | 0.9933 |
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+ | 0.0034 | 6.0 | 4350 | 0.0353 | 0.9411 | 0.9594 | 0.9502 | 0.9934 |
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+ | 0.0015 | 7.0 | 5075 | 0.0372 | 0.9404 | 0.9498 | 0.9450 | 0.9927 |
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+ | 0.0013 | 8.0 | 5800 | 0.0379 | 0.9477 | 0.9492 | 0.9485 | 0.9938 |
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+ | 0.0006 | 9.0 | 6525 | 0.0405 | 0.9516 | 0.9502 | 0.9509 | 0.9937 |
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+ | 0.0039 | 10.0 | 7250 | 0.0442 | 0.9420 | 0.9536 | 0.9478 | 0.9931 |
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+ | 0.0013 | 11.0 | 7975 | 0.0393 | 0.9479 | 0.9528 | 0.9504 | 0.9936 |
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+ | 0.001 | 12.0 | 8700 | 0.0431 | 0.9455 | 0.9513 | 0.9484 | 0.9933 |
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+ | 0.0011 | 13.0 | 9425 | 0.0431 | 0.9487 | 0.9425 | 0.9455 | 0.9936 |
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+ | 0.0003 | 14.0 | 10150 | 0.0425 | 0.9392 | 0.9450 | 0.9421 | 0.9933 |
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+ | 0.0001 | 15.0 | 10875 | 0.0456 | 0.9475 | 0.9515 | 0.9495 | 0.9937 |
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+ | 0.0011 | 16.0 | 11600 | 0.0446 | 0.9467 | 0.9471 | 0.9469 | 0.9928 |
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+ | 0.0002 | 17.0 | 12325 | 0.0500 | 0.9532 | 0.9457 | 0.9495 | 0.9933 |
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+ | 0.0001 | 18.0 | 13050 | 0.0504 | 0.9479 | 0.9490 | 0.9485 | 0.9929 |
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+ | 0.0002 | 19.0 | 13775 | 0.0455 | 0.9463 | 0.9527 | 0.9495 | 0.9933 |
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+ | 0.0013 | 20.0 | 14500 | 0.0471 | 0.9487 | 0.9544 | 0.9515 | 0.9933 |
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+ | 0.0005 | 21.0 | 15225 | 0.0425 | 0.9491 | 0.9584 | 0.9537 | 0.9936 |
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+ | 0.0009 | 22.0 | 15950 | 0.0503 | 0.9455 | 0.9555 | 0.9505 | 0.9931 |
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+ | 0.0003 | 23.0 | 16675 | 0.0474 | 0.9530 | 0.9555 | 0.9543 | 0.9938 |
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+ | 0.0006 | 24.0 | 17400 | 0.0481 | 0.9531 | 0.9538 | 0.9534 | 0.9937 |
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+ | 0.0013 | 25.0 | 18125 | 0.0502 | 0.9467 | 0.9534 | 0.9500 | 0.9934 |
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+ | 0.0001 | 26.0 | 18850 | 0.0517 | 0.9461 | 0.9492 | 0.9476 | 0.9933 |
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+ | 0.0001 | 27.0 | 19575 | 0.0410 | 0.9536 | 0.9530 | 0.9533 | 0.9937 |
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+ | 0.0011 | 28.0 | 20300 | 0.0453 | 0.9520 | 0.9498 | 0.9509 | 0.9937 |
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+ | 0.0007 | 29.0 | 21025 | 0.0444 | 0.9479 | 0.9480 | 0.9479 | 0.9935 |
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+ | 0.0 | 30.0 | 21750 | 0.0498 | 0.9529 | 0.9498 | 0.9513 | 0.9937 |
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+ | 0.0001 | 31.0 | 22475 | 0.0490 | 0.9514 | 0.9496 | 0.9505 | 0.9935 |
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+ | 0.001 | 32.0 | 23200 | 0.0499 | 0.9495 | 0.9486 | 0.9491 | 0.9934 |
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+ | 0.0001 | 33.0 | 23925 | 0.0451 | 0.9499 | 0.9557 | 0.9528 | 0.9939 |
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+ | 0.0002 | 34.0 | 24650 | 0.0469 | 0.9486 | 0.9563 | 0.9525 | 0.9937 |
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+ | 0.0001 | 35.0 | 25375 | 0.0505 | 0.9568 | 0.9496 | 0.9532 | 0.9938 |
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+ | 0.0003 | 36.0 | 26100 | 0.0491 | 0.9593 | 0.9525 | 0.9559 | 0.9942 |
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+ | 0.0005 | 37.0 | 26825 | 0.0432 | 0.9551 | 0.9532 | 0.9542 | 0.9939 |
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+ | 0.0003 | 38.0 | 27550 | 0.0465 | 0.9536 | 0.9486 | 0.9511 | 0.9937 |
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+ | 0.0019 | 39.0 | 28275 | 0.0491 | 0.9574 | 0.9469 | 0.9521 | 0.9937 |
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+ | 0.0 | 40.0 | 29000 | 0.0470 | 0.9582 | 0.9534 | 0.9558 | 0.9940 |
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+ | 0.0008 | 41.0 | 29725 | 0.0477 | 0.9505 | 0.9538 | 0.9522 | 0.9937 |
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+ | 0.0 | 42.0 | 30450 | 0.0544 | 0.9500 | 0.9542 | 0.9521 | 0.9937 |
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+ | 0.0002 | 43.0 | 31175 | 0.0527 | 0.9571 | 0.9492 | 0.9531 | 0.9938 |
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+ | 0.0005 | 44.0 | 31900 | 0.0510 | 0.9574 | 0.9513 | 0.9543 | 0.9939 |
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+ | 0.0006 | 45.0 | 32625 | 0.0478 | 0.9527 | 0.9536 | 0.9532 | 0.9938 |
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+ | 0.0001 | 46.0 | 33350 | 0.0464 | 0.9559 | 0.9517 | 0.9538 | 0.9937 |
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+ | 0.0001 | 47.0 | 34075 | 0.0478 | 0.9578 | 0.9530 | 0.9554 | 0.9939 |
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+ | 0.0 | 48.0 | 34800 | 0.0507 | 0.9574 | 0.9515 | 0.9544 | 0.9940 |
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+ | 0.0 | 49.0 | 35525 | 0.0534 | 0.9531 | 0.9534 | 0.9532 | 0.9939 |
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+ | 0.0004 | 50.0 | 36250 | 0.0512 | 0.9541 | 0.9530 | 0.9536 | 0.9941 |
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+ | 0.0001 | 51.0 | 36975 | 0.0478 | 0.9549 | 0.9532 | 0.9541 | 0.9940 |
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+ | 0.0001 | 52.0 | 37700 | 0.0446 | 0.9541 | 0.9555 | 0.9548 | 0.9942 |
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+ | 0.0 | 53.0 | 38425 | 0.0522 | 0.9529 | 0.9509 | 0.9519 | 0.9935 |
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+ | 0.0001 | 54.0 | 39150 | 0.0507 | 0.9552 | 0.9525 | 0.9538 | 0.9937 |
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+ | 0.0003 | 55.0 | 39875 | 0.0493 | 0.9466 | 0.9484 | 0.9475 | 0.9930 |
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+ | 0.0 | 56.0 | 40600 | 0.0496 | 0.9507 | 0.9496 | 0.9501 | 0.9934 |
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+ | 0.0 | 57.0 | 41325 | 0.0502 | 0.9512 | 0.9559 | 0.9535 | 0.9940 |
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+ | 0.0 | 58.0 | 42050 | 0.0528 | 0.9465 | 0.9525 | 0.9494 | 0.9932 |
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+ | 0.0 | 59.0 | 42775 | 0.0578 | 0.9480 | 0.9503 | 0.9492 | 0.9931 |
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+ | 0.0 | 60.0 | 43500 | 0.0557 | 0.9506 | 0.9486 | 0.9496 | 0.9935 |
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+ | 0.0 | 61.0 | 44225 | 0.0487 | 0.9539 | 0.9521 | 0.9530 | 0.9936 |
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+ | 0.0 | 62.0 | 44950 | 0.0519 | 0.9534 | 0.9536 | 0.9535 | 0.9938 |
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+ | 0.0 | 63.0 | 45675 | 0.0532 | 0.9531 | 0.9554 | 0.9542 | 0.9939 |
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+ | 0.0 | 64.0 | 46400 | 0.0572 | 0.9534 | 0.9527 | 0.9530 | 0.9938 |
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+ | 0.0001 | 65.0 | 47125 | 0.0563 | 0.9550 | 0.9527 | 0.9538 | 0.9940 |
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+ | 0.0 | 66.0 | 47850 | 0.0550 | 0.9568 | 0.9507 | 0.9538 | 0.9940 |
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+ | 0.0 | 67.0 | 48575 | 0.0585 | 0.9480 | 0.9542 | 0.9511 | 0.9935 |
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+ | 0.0003 | 68.0 | 49300 | 0.0607 | 0.9501 | 0.9496 | 0.9499 | 0.9936 |
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+ | 0.0 | 69.0 | 50025 | 0.0577 | 0.9529 | 0.9548 | 0.9539 | 0.9939 |
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+ | 0.0 | 70.0 | 50750 | 0.0583 | 0.9541 | 0.9569 | 0.9555 | 0.9941 |
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+ | 0.0001 | 71.0 | 51475 | 0.0549 | 0.9530 | 0.9486 | 0.9508 | 0.9938 |
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+ | 0.0 | 72.0 | 52200 | 0.0592 | 0.9546 | 0.9509 | 0.9528 | 0.9937 |
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+ | 0.0 | 73.0 | 52925 | 0.0598 | 0.9524 | 0.9502 | 0.9513 | 0.9936 |
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+ | 0.0 | 74.0 | 53650 | 0.0583 | 0.9530 | 0.9517 | 0.9523 | 0.9937 |
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+ | 0.0 | 75.0 | 54375 | 0.0602 | 0.9513 | 0.9513 | 0.9513 | 0.9936 |
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+ | 0.0 | 76.0 | 55100 | 0.0624 | 0.9510 | 0.9527 | 0.9518 | 0.9934 |
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+ | 0.0 | 77.0 | 55825 | 0.0622 | 0.9523 | 0.9527 | 0.9525 | 0.9935 |
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+ | 0.0 | 78.0 | 56550 | 0.0599 | 0.9509 | 0.9536 | 0.9522 | 0.9938 |
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+ | 0.0 | 79.0 | 57275 | 0.0599 | 0.9509 | 0.9550 | 0.9529 | 0.9937 |
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+ | 0.0 | 80.0 | 58000 | 0.0588 | 0.9551 | 0.9536 | 0.9544 | 0.9939 |
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+ | 0.0 | 81.0 | 58725 | 0.0581 | 0.9547 | 0.9561 | 0.9554 | 0.9941 |
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+ | 0.0 | 82.0 | 59450 | 0.0587 | 0.9574 | 0.9567 | 0.9571 | 0.9940 |
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+ | 0.0 | 83.0 | 60175 | 0.0592 | 0.9533 | 0.9582 | 0.9558 | 0.9940 |
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+ | 0.0 | 84.0 | 60900 | 0.0602 | 0.9534 | 0.9569 | 0.9551 | 0.9939 |
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+ | 0.0 | 85.0 | 61625 | 0.0601 | 0.9530 | 0.9554 | 0.9542 | 0.9938 |
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+ | 0.0 | 86.0 | 62350 | 0.0608 | 0.9528 | 0.9561 | 0.9545 | 0.9939 |
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+ | 0.0 | 87.0 | 63075 | 0.0606 | 0.9560 | 0.9538 | 0.9549 | 0.9939 |
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+ | 0.0 | 88.0 | 63800 | 0.0590 | 0.9514 | 0.9575 | 0.9544 | 0.9940 |
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+ | 0.0 | 89.0 | 64525 | 0.0611 | 0.9542 | 0.9577 | 0.9559 | 0.9940 |
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+ | 0.0002 | 90.0 | 65250 | 0.0617 | 0.9563 | 0.9567 | 0.9565 | 0.9940 |
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+ | 0.0 | 91.0 | 65975 | 0.0611 | 0.9578 | 0.9555 | 0.9566 | 0.9940 |
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+ | 0.0004 | 92.0 | 66700 | 0.0628 | 0.9510 | 0.9567 | 0.9539 | 0.9939 |
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+ | 0.0 | 93.0 | 67425 | 0.0634 | 0.9523 | 0.9561 | 0.9542 | 0.9939 |
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+ | 0.0 | 94.0 | 68150 | 0.0629 | 0.9534 | 0.9571 | 0.9552 | 0.9940 |
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+ | 0.0 | 95.0 | 68875 | 0.0627 | 0.9523 | 0.9565 | 0.9544 | 0.9940 |
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+ | 0.0 | 96.0 | 69600 | 0.0627 | 0.9528 | 0.9565 | 0.9547 | 0.9940 |
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+ | 0.0 | 97.0 | 70325 | 0.0625 | 0.9536 | 0.9565 | 0.9550 | 0.9940 |
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+ | 0.0 | 98.0 | 71050 | 0.0620 | 0.9558 | 0.9561 | 0.9559 | 0.9941 |
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+ | 0.0 | 99.0 | 71775 | 0.0620 | 0.9543 | 0.9573 | 0.9558 | 0.9940 |
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+ | 0.0 | 100.0 | 72500 | 0.0621 | 0.9538 | 0.9573 | 0.9555 | 0.9940 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.1
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+ - Pytorch 2.3.0+cu118
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
config.json ADDED
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+ {
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+ "_name_or_path": "hfl/chinese-roberta-wwm-ext-large",
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+ "BertForTokenClassification"
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+ ],
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+ "6": "I-PER"
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+ "model_type": "bert",
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+ "num_attention_heads": 16,
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+ "output_past": true,
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+ "pooler_num_fc_layers": 3,
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+ "pooler_type": "first_token_transform",
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.47.1",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ }
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tokenizer.json ADDED
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