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Kyle1668
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allenai-scibert_scivocab_uncased_20241230-091934

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Inference Endpoints
Model card Files Community

allenai-scibert_scivocab_uncased_20241230-091934

This model is a fine-tuned version of allenai/scibert_scivocab_uncased on an unknown dataset. It achieves the following results on the evaluation set:

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] 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0.6601 1.0 2436 0.6623 0.4313 1.0 0.6027 0.4313 0.4313 1.0 0.6027 0.4313 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.4498 0.9989 0.6203 0.4725 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687
0.2161 2.0 4872 0.3014 0.4313 1.0 0.6027 0.4313 0.4313 1.0 0.6027 0.4313 0.4313 1.0 0.6027 0.4313 0.8328 0.9731 0.8975 0.9042 0.8331 0.9722 0.8973 0.9040 0.8332 0.9722 0.8974 0.9041 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8332 0.9721 0.8973 0.9040 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9721 0.8973 0.9041 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8333 0.9720 0.8973 0.9040 0.8332 0.9719 0.8972 0.9040 0.8332 0.9719 0.8972 0.9040 0.8332 0.9719 0.8972 0.9040 0.8332 0.9718 0.8972 0.9039 0.8333 0.9716 0.8971 0.9039 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687 0.0 0.0 0.0 0.5687
0.0569 3.0 7308 0.1032 0.8166 0.9988 0.8985 0.9027 0.8430 0.9980 0.9140 0.9190 0.8560 0.9973 0.9213 0.9265 0.8666 0.9966 0.9271 0.9324 0.8740 0.9960 0.9310 0.9364 0.8810 0.9959 0.9350 0.9402 0.8870 0.9950 0.9379 0.9431 0.8914 0.9950 0.9404 0.9456 0.8959 0.9947 0.9427 0.9479 0.9000 0.9942 0.9448 0.9499 0.9031 0.9938 0.9463 0.9513 0.9066 0.9931 0.9479 0.9529 0.9089 0.9929 0.9491 0.9540 0.9108 0.9923 0.9498 0.9548 0.9138 0.9918 0.9512 0.9561 0.9163 0.9913 0.9524 0.9572 0.9184 0.9912 0.9534 0.9582 0.9206 0.9908 0.9544 0.9592 0.9218 0.9902 0.9548 0.9596 0.9231 0.9899 0.9553 0.9601 0.9244 0.9896 0.9559 0.9606 0.9257 0.9893 0.9564 0.9611 0.9268 0.9890 0.9569 0.9615 0.9274 0.9889 0.9571 0.9618 0.9284 0.9884 0.9575 0.9621 0.9293 0.9882 0.9579 0.9625 0.9302 0.9881 0.9583 0.9629 0.9310 0.9881 0.9587 0.9633 0.9320 0.9879 0.9591 0.9637 0.9331 0.9876 0.9596 0.9641 0.9338 0.9875 0.9599 0.9644 0.9346 0.9874 0.9603 0.9648 0.9353 0.9872 0.9605 0.9650 0.9357 0.9872 0.9608 0.9652 0.9364 0.9871 0.9611 0.9655 0.9367 0.9869 0.9612 0.9656 0.9373 0.9867 0.9613 0.9658 0.9380 0.9863 0.9616 0.9660 0.9388 0.9860 0.9618 0.9662 0.9397 0.9852 0.9619 0.9663 0.9408 0.9850 0.9624 0.9668 0.9414 0.9849 0.9627 0.9671 0.9414 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9840 0.9623 0.9667 0.9415 0.9839 0.9622 0.9667 0.9416 0.9839 0.9623 0.9667 0.9416 0.9839 0.9623 0.9667 0.9417 0.9839 0.9623 0.9668 0.9417 0.9838 0.9623 0.9667 0.9417 0.9838 0.9623 0.9667 0.9417 0.9837 0.9622 0.9667 0.9418 0.9837 0.9623 0.9667 0.9418 0.9835 0.9622 0.9667 0.9421 0.9834 0.9623 0.9668 0.9442 0.9827 0.9631 0.9675 0.9462 0.9812 0.9634 0.9678 0.9469 0.9806 0.9634 0.9679 0.9477 0.9801 0.9636 0.9681 0.9491 0.9794 0.9640 0.9685 0.9504 0.9792 0.9646 0.9690 0.9509 0.9787 0.9646 0.9690 0.9523 0.9782 0.9651 0.9695 0.9532 0.9775 0.9652 0.9696 0.9538 0.9771 0.9653 0.9697 0.9547 0.9767 0.9656 0.9700 0.9552 0.9757 0.9653 0.9698 0.9558 0.9752 0.9654 0.9698 0.9564 0.9741 0.9652 0.9697 0.9569 0.9737 0.9652 0.9697 0.9574 0.9729 0.9651 0.9697 0.9584 0.9726 0.9654 0.9700 0.9591 0.9719 0.9655 0.9700 0.9599 0.9714 0.9656 0.9701 0.9605 0.9705 0.9655 0.9701 0.9618 0.9693 0.9656 0.9702 0.9625 0.9681 0.9653 0.9700 0.9630 0.9670 0.9650 0.9697 0.9644 0.9662 0.9653 0.9700 0.9661 0.9649 0.9655 0.9703 0.9666 0.9636 0.9651 0.9699 0.9679 0.9612 0.9645 0.9695 0.9691 0.9585 0.9638 0.9689 0.9712 0.9550 0.9630 0.9684 0.9736 0.9493 0.9613 0.9670 0.9769 0.9414 0.9588 0.9651 0.9805 0.9291 0.9541 0.9615 0.9845 0.9055 0.9434 0.9531 0.9891 0.8632 0.9219 0.9369

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0
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Model size
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Tensor type
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Model tree for Kyle1668/allenai-scibert_scivocab_uncased_20241230-091934

Base model

allenai/scibert_scivocab_uncased
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Evaluation results

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