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answerdotai-ModernBERT-large_20250111-002259

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

answerdotai-ModernBERT-large_20250111-002259

This model is a fine-tuned version of answerdotai/ModernBERT-large 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: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 64
  • total_train_batch_size: 64
  • 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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5.1806 1.0 4160 0.1584 0.6257 0.9965 0.7687 0.7869 0.6946 0.9929 0.8174 0.8423 0.7320 0.9904 0.8418 0.8677 0.7554 0.9883 0.8563 0.8821 0.7747 0.9866 0.8679 0.8933 0.7876 0.9850 0.8753 0.9003 0.7993 0.9839 0.8820 0.9065 0.8079 0.9829 0.8868 0.9109 0.8153 0.9820 0.8909 0.9145 0.8225 0.9810 0.8948 0.9180 0.8286 0.9801 0.8980 0.9209 0.8336 0.9791 0.9005 0.9231 0.8386 0.9784 0.9031 0.9254 0.8423 0.9779 0.9050 0.9271 0.8457 0.9772 0.9067 0.9286 0.8482 0.9767 0.9079 0.9296 0.8513 0.9757 0.9093 0.9308 0.8554 0.9751 0.9113 0.9326 0.8572 0.9740 0.9119 0.9331 0.8599 0.9735 0.9132 0.9342 0.8628 0.9730 0.9146 0.9354 0.8652 0.9726 0.9157 0.9364 0.8677 0.9721 0.9170 0.9374 0.8703 0.9717 0.9182 0.9385 0.8719 0.9708 0.9187 0.9389 0.8747 0.97 0.9199 0.9399 0.8759 0.9692 0.9202 0.9402 0.8779 0.9685 0.9210 0.9410 0.8796 0.9681 0.9217 0.9416 0.8810 0.9677 0.9223 0.9421 0.8832 0.9675 0.9234 0.9430 0.8848 0.9670 0.9241 0.9435 0.8861 0.9668 0.9247 0.9441 0.8874 0.9664 0.9253 0.9445 0.8890 0.9661 0.9259 0.9451 0.8907 0.9655 0.9266 0.9457 0.8920 0.9652 0.9272 0.9461 0.8938 0.9649 0.9280 0.9468 0.8946 0.9646 0.9283 0.9470 0.8955 0.9639 0.9285 0.9472 0.8969 0.9629 0.9288 0.9475 0.8990 0.9628 0.9298 0.9483 0.9003 0.9623 0.9303 0.9487 0.9012 0.9616 0.9304 0.9489 0.9021 0.9609 0.9306 0.9490 0.9031 0.9604 0.9309 0.9493 0.9044 0.9594 0.9311 0.9495 0.9058 0.9593 0.9317 0.9501 0.9068 0.9587 0.9320 0.9503 0.9076 0.9583 0.9323 0.9505 0.9087 0.9579 0.9326 0.9508 0.9100 0.9574 0.9331 0.9512 0.9106 0.9571 0.9333 0.9514 0.9119 0.9565 0.9337 0.9517 0.9126 0.9562 0.9339 0.9519 0.9132 0.9557 0.9340 0.9520 0.9141 0.9553 0.9343 0.9522 0.9147 0.9551 0.9345 0.9524 0.9159 0.9547 0.9349 0.9528 0.9170 0.9546 0.9354 0.9531 0.9179 0.9541 0.9357 0.9534 0.9193 0.9534 0.9360 0.9537 0.9205 0.9529 0.9364 0.9540 0.9213 0.9525 0.9366 0.9542 0.9221 0.9517 0.9367 0.9543 0.9231 0.9513 0.9370 0.9545 0.9239 0.9506 0.9370 0.9546 0.9247 0.9493 0.9368 0.9545 0.9260 0.9484 0.9371 0.9547 0.9273 0.9477 0.9374 0.9550 0.9279 0.9467 0.9372 0.9549 0.9285 0.9461 0.9372 0.9549 0.9296 0.9455 0.9375 0.9552 0.9306 0.9447 0.9376 0.9553 0.9314 0.9439 0.9376 0.9553 0.9325 0.9434 0.9379 0.9556 0.9334 0.9424 0.9379 0.9556 0.9349 0.9413 0.9381 0.9559 0.9362 0.9404 0.9383 0.9560 0.9377 0.9397 0.9387 0.9564 0.9389 0.9390 0.9389 0.9566 0.9397 0.9386 0.9392 0.9568 0.9410 0.9372 0.9391 0.9568 0.9426 0.9361 0.9393 0.9570 0.9437 0.9344 0.9390 0.9569 0.9444 0.9335 0.9389 0.9568 0.9458 0.9320 0.9389 0.9569 0.9469 0.9306 0.9387 0.9568 0.9479 0.9290 0.9384 0.9566 0.9495 0.9272 0.9382 0.9566 0.9517 0.9248 0.9380 0.9566 0.9533 0.9233 0.9381 0.9567 0.9563 0.9205 0.9381 0.9568 0.9584 0.9172 0.9373 0.9564 0.9607 0.9143 0.9369 0.9562 0.9643 0.9107 0.9367 0.9563 0.9679 0.9048 0.9353 0.9555 0.9730 0.8962 0.9330 0.9543 0.9788 0.8804 0.9270 0.9507
2.6355 2.0 8320 0.2273 0.9172 0.9587 0.9375 0.9546 0.9258 0.9547 0.9400 0.9567 0.9285 0.9529 0.9406 0.9572 0.9309 0.9516 0.9411 0.9577 0.9325 0.9511 0.9417 0.9581 0.9340 0.9506 0.9422 0.9586 0.9345 0.9503 0.9423 0.9587 0.9355 0.9498 0.9426 0.9589 0.9362 0.9490 0.9425 0.9589 0.9364 0.9487 0.9425 0.9589 0.9371 0.9483 0.9427 0.9590 0.9378 0.9480 0.9429 0.9592 0.9381 0.9478 0.9429 0.9592 0.9384 0.9478 0.9431 0.9593 0.9388 0.9475 0.9432 0.9594 0.9394 0.9470 0.9432 0.9594 0.9395 0.9469 0.9432 0.9594 0.9397 0.9466 0.9431 0.9594 0.9398 0.9463 0.9430 0.9594 0.9399 0.9462 0.9431 0.9594 0.9404 0.9462 0.9433 0.9596 0.9405 0.9461 0.9433 0.9596 0.9407 0.9461 0.9434 0.9596 0.9407 0.9460 0.9433 0.9596 0.9408 0.9458 0.9433 0.9596 0.9409 0.9458 0.9433 0.9596 0.9409 0.9457 0.9433 0.9596 0.9410 0.9457 0.9434 0.9596 0.9411 0.9457 0.9434 0.9597 0.9412 0.9455 0.9433 0.9596 0.9412 0.9453 0.9432 0.9596 0.9413 0.9452 0.9432 0.9596 0.9413 0.9452 0.9432 0.9596 0.9417 0.9452 0.9435 0.9597 0.9420 0.9451 0.9436 0.9598 0.9424 0.9449 0.9436 0.9599 0.9424 0.9448 0.9436 0.9599 0.9425 0.9447 0.9436 0.9599 0.9426 0.9447 0.9436 0.9599 0.9428 0.9446 0.9437 0.9599 0.9428 0.9444 0.9436 0.9599 0.9428 0.9444 0.9436 0.9599 0.9429 0.9444 0.9437 0.9599 0.9431 0.9442 0.9437 0.9599 0.9432 0.9442 0.9437 0.9600 0.9433 0.9441 0.9437 0.9600 0.9434 0.9440 0.9437 0.9600 0.9434 0.9439 0.9437 0.9600 0.9434 0.9438 0.9436 0.9599 0.9437 0.9437 0.9437 0.9600 0.9437 0.9435 0.9436 0.9599 0.9438 0.9435 0.9437 0.9600 0.9439 0.9433 0.9436 0.9599 0.9441 0.9433 0.9437 0.9600 0.9443 0.9433 0.9438 0.9601 0.9444 0.9431 0.9438 0.9601 0.9446 0.9431 0.9439 0.9601 0.9447 0.9431 0.9439 0.9602 0.9448 0.9431 0.9439 0.9602 0.9449 0.9431 0.9440 0.9602 0.9450 0.9430 0.9440 0.9603 0.9451 0.9429 0.9440 0.9603 0.9451 0.9428 0.9439 0.9602 0.9451 0.9427 0.9439 0.9602 0.9452 0.9424 0.9438 0.9601 0.9453 0.9424 0.9439 0.9602 0.9455 0.9423 0.9439 0.9602 0.9457 0.9423 0.9440 0.9603 0.9458 0.9420 0.9439 0.9602 0.9460 0.9420 0.9440 0.9603 0.9461 0.9419 0.9440 0.9603 0.9463 0.9419 0.9441 0.9604 0.9465 0.9416 0.9440 0.9603 0.9467 0.9414 0.9440 0.9603 0.9468 0.9414 0.9441 0.9604 0.9468 0.9413 0.9440 0.9603 0.9469 0.9413 0.9441 0.9604 0.9471 0.9413 0.9442 0.9605 0.9473 0.9412 0.9442 0.9605 0.9476 0.9412 0.9444 0.9606 0.9478 0.9412 0.9445 0.9607 0.9480 0.9410 0.9445 0.9607 0.9482 0.9408 0.9445 0.9607 0.9483 0.9408 0.9446 0.9607 0.9485 0.9406 0.9445 0.9607 0.9486 0.9406 0.9446 0.9608 0.9489 0.9402 0.9445 0.9607 0.9494 0.9397 0.9445 0.9608 0.9500 0.9394 0.9446 0.9609 0.9504 0.9389 0.9446 0.9609 0.9508 0.9384 0.9446 0.9609 0.9517 0.9382 0.9449 0.9611 0.9523 0.9376 0.9449 0.9611 0.9530 0.9371 0.9450 0.9612 0.9540 0.9366 0.9452 0.9614 0.9549 0.9356 0.9451 0.9614 0.9557 0.9350 0.9453 0.9615 0.9574 0.9334 0.9452 0.9616 0.9608 0.9299 0.9451 0.9616
1.0262 2.9995 12477 0.2869 0.9376 0.9503 0.9439 0.9599 0.9402 0.9485 0.9443 0.9603 0.9412 0.9482 0.9447 0.9605 0.9423 0.9476 0.9450 0.9608 0.9429 0.9473 0.9451 0.9609 0.9431 0.9467 0.9449 0.9607 0.9433 0.9459 0.9446 0.9606 0.9433 0.9456 0.9445 0.9605 0.9435 0.9455 0.9445 0.9605 0.9436 0.9455 0.9446 0.9606 0.9437 0.9455 0.9446 0.9606 0.9439 0.9455 0.9447 0.9607 0.9441 0.9453 0.9447 0.9607 0.9443 0.9451 0.9447 0.9607 0.9443 0.9450 0.9447 0.9606 0.9443 0.9450 0.9447 0.9606 0.9446 0.9449 0.9447 0.9607 0.9447 0.9449 0.9448 0.9607 0.9447 0.9448 0.9447 0.9607 0.9449 0.9447 0.9448 0.9608 0.9452 0.9447 0.9449 0.9609 0.9453 0.9446 0.9449 0.9609 0.9454 0.9445 0.9450 0.9609 0.9456 0.9443 0.9450 0.9609 0.9458 0.9442 0.9450 0.9609 0.9458 0.9442 0.9450 0.9609 0.9459 0.9441 0.9450 0.9609 0.9459 0.9441 0.9450 0.9609 0.9460 0.9441 0.9450 0.9610 0.9460 0.9441 0.9451 0.9610 0.9461 0.9441 0.9451 0.9610 0.9462 0.9441 0.9452 0.9611 0.9462 0.9438 0.9450 0.9609 0.9462 0.9436 0.9449 0.9609 0.9462 0.9435 0.9448 0.9608 0.9463 0.9435 0.9449 0.9609 0.9463 0.9434 0.9448 0.9608 0.9463 0.9434 0.9448 0.9608 0.9463 0.9432 0.9448 0.9608 0.9463 0.9432 0.9448 0.9608 0.9463 0.9432 0.9448 0.9608 0.9465 0.9432 0.9449 0.9609 0.9465 0.9431 0.9448 0.9608 0.9466 0.9431 0.9449 0.9609 0.9466 0.9431 0.9449 0.9609 0.9467 0.9431 0.9449 0.9609 0.9468 0.9430 0.9449 0.9609 0.9470 0.9429 0.9450 0.9610 0.9472 0.9429 0.9451 0.9610 0.9472 0.9429 0.9451 0.9610 0.9473 0.9429 0.9451 0.9611 0.9473 0.9429 0.9451 0.9611 0.9473 0.9429 0.9451 0.9611 0.9474 0.9428 0.9450 0.9610 0.9474 0.9427 0.9450 0.9610 0.9476 0.9426 0.9451 0.9611 0.9478 0.9426 0.9452 0.9611 0.9478 0.9426 0.9452 0.9611 0.9478 0.9425 0.9451 0.9611 0.9480 0.9425 0.9452 0.9612 0.9480 0.9424 0.9452 0.9612 0.9481 0.9424 0.9452 0.9612 0.9482 0.9423 0.9452 0.9612 0.9483 0.9423 0.9453 0.9612 0.9484 0.9423 0.9453 0.9613 0.9484 0.9423 0.9453 0.9613 0.9484 0.9423 0.9453 0.9613 0.9484 0.9423 0.9453 0.9613 0.9484 0.9420 0.9452 0.9612 0.9485 0.9420 0.9453 0.9612 0.9485 0.9420 0.9453 0.9612 0.9485 0.9419 0.9452 0.9612 0.9485 0.9418 0.9452 0.9612 0.9485 0.9418 0.9452 0.9612 0.9485 0.9417 0.9451 0.9611 0.9486 0.9417 0.9452 0.9612 0.9486 0.9417 0.9451 0.9611 0.9487 0.9417 0.9452 0.9612 0.9489 0.9416 0.9452 0.9612 0.9489 0.9416 0.9452 0.9612 0.9490 0.9415 0.9452 0.9612 0.9490 0.9414 0.9452 0.9612 0.9491 0.9414 0.9452 0.9612 0.9493 0.9413 0.9453 0.9613 0.9496 0.9412 0.9454 0.9613 0.9498 0.9410 0.9454 0.9614 0.9500 0.9410 0.9455 0.9614 0.9501 0.9410 0.9455 0.9615 0.9502 0.9409 0.9455 0.9615 0.9503 0.9406 0.9454 0.9614 0.9506 0.9404 0.9454 0.9614 0.9506 0.9399 0.9452 0.9613 0.9512 0.9396 0.9454 0.9614 0.9514 0.9393 0.9453 0.9614 0.9517 0.9390 0.9453 0.9614 0.9521 0.9387 0.9454 0.9614 0.9529 0.9384 0.9456 0.9616 0.9538 0.9382 0.9459 0.9619 0.9555 0.9362 0.9458 0.9619

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0
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Safetensors
Model size
396M params
Tensor type
F32
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Evaluation results

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