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answerdotai-ModernBERT-large_20250111-224237

Text Classification
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Safetensors
modernbert
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answerdotai-ModernBERT-large_20250111-224237

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 Precision@0.01 Recall@0.01 F1@0.01 Accuracy@0.01 Precision@0.02 Recall@0.02 F1@0.02 Accuracy@0.02 Precision@0.03 Recall@0.03 F1@0.03 Accuracy@0.03 Precision@0.04 Recall@0.04 F1@0.04 Accuracy@0.04 Precision@0.05 Recall@0.05 F1@0.05 Accuracy@0.05 Precision@0.06 Recall@0.06 F1@0.06 Accuracy@0.06 Precision@0.07 Recall@0.07 F1@0.07 Accuracy@0.07 Precision@0.08 Recall@0.08 F1@0.08 Accuracy@0.08 Precision@0.09 Recall@0.09 F1@0.09 Accuracy@0.09 Precision@0.1 Recall@0.1 F1@0.1 Accuracy@0.1 Precision@0.11 Recall@0.11 F1@0.11 Accuracy@0.11 Precision@0.12 Recall@0.12 F1@0.12 Accuracy@0.12 Precision@0.13 Recall@0.13 F1@0.13 Accuracy@0.13 Precision@0.14 Recall@0.14 F1@0.14 Accuracy@0.14 Precision@0.15 Recall@0.15 F1@0.15 Accuracy@0.15 Precision@0.16 Recall@0.16 F1@0.16 Accuracy@0.16 Precision@0.17 Recall@0.17 F1@0.17 Accuracy@0.17 Precision@0.18 Recall@0.18 F1@0.18 Accuracy@0.18 Precision@0.19 Recall@0.19 F1@0.19 Accuracy@0.19 Precision@0.2 Recall@0.2 F1@0.2 Accuracy@0.2 Precision@0.21 Recall@0.21 F1@0.21 Accuracy@0.21 Precision@0.22 Recall@0.22 F1@0.22 Accuracy@0.22 Precision@0.23 Recall@0.23 F1@0.23 Accuracy@0.23 Precision@0.24 Recall@0.24 F1@0.24 Accuracy@0.24 Precision@0.25 Recall@0.25 F1@0.25 Accuracy@0.25 Precision@0.26 Recall@0.26 F1@0.26 Accuracy@0.26 Precision@0.27 Recall@0.27 F1@0.27 Accuracy@0.27 Precision@0.28 Recall@0.28 F1@0.28 Accuracy@0.28 Precision@0.29 Recall@0.29 F1@0.29 Accuracy@0.29 Precision@0.3 Recall@0.3 F1@0.3 Accuracy@0.3 Precision@0.31 Recall@0.31 F1@0.31 Accuracy@0.31 Precision@0.32 Recall@0.32 F1@0.32 Accuracy@0.32 Precision@0.33 Recall@0.33 F1@0.33 Accuracy@0.33 Precision@0.34 Recall@0.34 F1@0.34 Accuracy@0.34 Precision@0.35 Recall@0.35 F1@0.35 Accuracy@0.35 Precision@0.36 Recall@0.36 F1@0.36 Accuracy@0.36 Precision@0.37 Recall@0.37 F1@0.37 Accuracy@0.37 Precision@0.38 Recall@0.38 F1@0.38 Accuracy@0.38 Precision@0.39 Recall@0.39 F1@0.39 Accuracy@0.39 Precision@0.4 Recall@0.4 F1@0.4 Accuracy@0.4 Precision@0.41 Recall@0.41 F1@0.41 Accuracy@0.41 Precision@0.42 Recall@0.42 F1@0.42 Accuracy@0.42 Precision@0.43 Recall@0.43 F1@0.43 Accuracy@0.43 Precision@0.44 Recall@0.44 F1@0.44 Accuracy@0.44 Precision@0.45 Recall@0.45 F1@0.45 Accuracy@0.45 Precision@0.46 Recall@0.46 F1@0.46 Accuracy@0.46 Precision@0.47 Recall@0.47 F1@0.47 Accuracy@0.47 Precision@0.48 Recall@0.48 F1@0.48 Accuracy@0.48 Precision@0.49 Recall@0.49 F1@0.49 Accuracy@0.49 Precision@0.5 Recall@0.5 F1@0.5 Accuracy@0.5 Precision@0.51 Recall@0.51 F1@0.51 Accuracy@0.51 Precision@0.52 Recall@0.52 F1@0.52 Accuracy@0.52 Precision@0.53 Recall@0.53 F1@0.53 Accuracy@0.53 Precision@0.54 Recall@0.54 F1@0.54 Accuracy@0.54 Precision@0.55 Recall@0.55 F1@0.55 Accuracy@0.55 Precision@0.56 Recall@0.56 F1@0.56 Accuracy@0.56 Precision@0.57 Recall@0.57 F1@0.57 Accuracy@0.57 Precision@0.58 Recall@0.58 F1@0.58 Accuracy@0.58 Precision@0.59 Recall@0.59 F1@0.59 Accuracy@0.59 Precision@0.6 Recall@0.6 F1@0.6 Accuracy@0.6 Precision@0.61 Recall@0.61 F1@0.61 Accuracy@0.61 Precision@0.62 Recall@0.62 F1@0.62 Accuracy@0.62 Precision@0.63 Recall@0.63 F1@0.63 Accuracy@0.63 Precision@0.64 Recall@0.64 F1@0.64 Accuracy@0.64 Precision@0.65 Recall@0.65 F1@0.65 Accuracy@0.65 Precision@0.66 Recall@0.66 F1@0.66 Accuracy@0.66 Precision@0.67 Recall@0.67 F1@0.67 Accuracy@0.67 Precision@0.68 Recall@0.68 F1@0.68 Accuracy@0.68 Precision@0.69 Recall@0.69 F1@0.69 Accuracy@0.69 Precision@0.7 Recall@0.7 F1@0.7 Accuracy@0.7 Precision@0.71 Recall@0.71 F1@0.71 Accuracy@0.71 Precision@0.72 Recall@0.72 F1@0.72 Accuracy@0.72 Precision@0.73 Recall@0.73 F1@0.73 Accuracy@0.73 Precision@0.74 Recall@0.74 F1@0.74 Accuracy@0.74 Precision@0.75 Recall@0.75 F1@0.75 Accuracy@0.75 Precision@0.76 Recall@0.76 F1@0.76 Accuracy@0.76 Precision@0.77 Recall@0.77 F1@0.77 Accuracy@0.77 Precision@0.78 Recall@0.78 F1@0.78 Accuracy@0.78 Precision@0.79 Recall@0.79 F1@0.79 Accuracy@0.79 Precision@0.8 Recall@0.8 F1@0.8 Accuracy@0.8 Precision@0.81 Recall@0.81 F1@0.81 Accuracy@0.81 Precision@0.82 Recall@0.82 F1@0.82 Accuracy@0.82 Precision@0.83 Recall@0.83 F1@0.83 Accuracy@0.83 Precision@0.84 Recall@0.84 F1@0.84 Accuracy@0.84 Precision@0.85 Recall@0.85 F1@0.85 Accuracy@0.85 Precision@0.86 Recall@0.86 F1@0.86 Accuracy@0.86 Precision@0.87 Recall@0.87 F1@0.87 Accuracy@0.87 Precision@0.88 Recall@0.88 F1@0.88 Accuracy@0.88 Precision@0.89 Recall@0.89 F1@0.89 Accuracy@0.89 Precision@0.9 Recall@0.9 F1@0.9 Accuracy@0.9 Precision@0.91 Recall@0.91 F1@0.91 Accuracy@0.91 Precision@0.92 Recall@0.92 F1@0.92 Accuracy@0.92 Precision@0.93 Recall@0.93 F1@0.93 Accuracy@0.93 Precision@0.94 Recall@0.94 F1@0.94 Accuracy@0.94 Precision@0.95 Recall@0.95 F1@0.95 Accuracy@0.95 Precision@0.96 Recall@0.96 F1@0.96 Accuracy@0.96 Precision@0.97 Recall@0.97 F1@0.97 Accuracy@0.97 Precision@0.98 Recall@0.98 F1@0.98 Accuracy@0.98 Precision@0.99 Recall@0.99 F1@0.99 Accuracy@0.99
5.8618 1.0 4160 0.1579 0.6785 0.9929 0.8061 0.8303 0.7597 0.9835 0.8573 0.8836 0.7958 0.9772 0.8772 0.9028 0.8144 0.9734 0.8868 0.9117 0.8279 0.9710 0.8938 0.9180 0.8403 0.9684 0.8998 0.9234 0.8481 0.9660 0.9032 0.9264 0.8546 0.9641 0.9061 0.9290 0.8602 0.9631 0.9088 0.9313 0.8649 0.9617 0.9107 0.9330 0.8691 0.9604 0.9125 0.9345 0.8721 0.9593 0.9136 0.9355 0.8747 0.9579 0.9144 0.9363 0.8774 0.9571 0.9155 0.9372 0.8802 0.9561 0.9166 0.9382 0.8830 0.9556 0.9179 0.9392 0.8854 0.9552 0.9190 0.9402 0.8873 0.9543 0.9196 0.9407 0.8891 0.9537 0.9202 0.9412 0.8909 0.9534 0.9211 0.9419 0.8926 0.9525 0.9216 0.9424 0.8944 0.9521 0.9224 0.9430 0.8955 0.9513 0.9225 0.9432 0.8965 0.9502 0.9225 0.9433 0.8969 0.9496 0.9225 0.9433 0.8982 0.9484 0.9226 0.9435 0.8993 0.9475 0.9228 0.9437 0.9004 0.9469 0.9230 0.9439 0.9019 0.9467 0.9237 0.9444 0.9034 0.9461 0.9243 0.9449 0.9047 0.9454 0.9246 0.9452 0.9056 0.9450 0.9248 0.9454 0.9065 0.9446 0.9252 0.9457 0.9074 0.9440 0.9254 0.9459 0.9085 0.9438 0.9258 0.9462 0.9093 0.9433 0.9260 0.9464 0.9100 0.9428 0.9261 0.9466 0.9109 0.9423 0.9263 0.9467 0.9118 0.9418 0.9266 0.9470 0.9124 0.9417 0.9268 0.9472 0.9130 0.9412 0.9269 0.9472 0.9140 0.9406 0.9271 0.9474 0.9146 0.9398 0.9271 0.9474 0.9158 0.9396 0.9275 0.9478 0.9170 0.9392 0.9279 0.9482 0.9177 0.9387 0.9281 0.9483 0.9185 0.9383 0.9283 0.9485 0.9193 0.9376 0.9284 0.9486 0.9200 0.9370 0.9284 0.9487 0.9206 0.9367 0.9286 0.9488 0.9214 0.9363 0.9288 0.9490 0.9219 0.9356 0.9287 0.9489 0.9224 0.9352 0.9288 0.9490 0.9228 0.9350 0.9289 0.9491 0.9234 0.9350 0.9292 0.9494 0.9241 0.9345 0.9293 0.9494 0.9250 0.9341 0.9295 0.9497 0.9256 0.9339 0.9298 0.9499 0.9269 0.9336 0.9302 0.9502 0.9273 0.9331 0.9302 0.9502 0.9276 0.9323 0.9299 0.9501 0.9284 0.9318 0.9301 0.9502 0.9290 0.9313 0.9301 0.9503 0.9296 0.9309 0.9303 0.9504 0.9306 0.9307 0.9307 0.9507 0.9320 0.9302 0.9311 0.9511 0.9327 0.9293 0.9310 0.9510 0.9333 0.9288 0.9310 0.9511 0.9340 0.9281 0.9310 0.9511 0.9347 0.9273 0.9310 0.9512 0.9354 0.9263 0.9309 0.9511 0.9364 0.9260 0.9311 0.9513 0.9373 0.9250 0.9311 0.9514 0.9383 0.9242 0.9312 0.9515 0.9392 0.9235 0.9313 0.9516 0.9402 0.9230 0.9315 0.9518 0.9410 0.9218 0.9313 0.9517 0.9421 0.9204 0.9311 0.9516 0.9428 0.9198 0.9312 0.9517 0.9439 0.9192 0.9314 0.9519 0.9448 0.9179 0.9312 0.9518 0.9460 0.9164 0.9310 0.9517 0.9479 0.9152 0.9313 0.9520 0.9493 0.9140 0.9313 0.9521 0.9504 0.9125 0.9311 0.9520 0.9517 0.9101 0.9304 0.9516 0.9529 0.9085 0.9302 0.9515 0.9542 0.9062 0.9296 0.9512 0.9555 0.9041 0.9291 0.9510 0.9566 0.9028 0.9289 0.9509 0.9577 0.9005 0.9282 0.9505 0.9600 0.8975 0.9277 0.9503 0.9620 0.8948 0.9272 0.9501 0.9639 0.89 0.9255 0.9491 0.9662 0.8857 0.9242 0.9484 0.9697 0.8790 0.9221 0.9472 0.9744 0.8685 0.9184 0.9452 0.9794 0.8508 0.9106 0.9406 0.9860 0.8112 0.8901 0.9288
3.574 2.0 8320 0.2679 0.9139 0.9550 0.9340 0.9520 0.9191 0.9520 0.9352 0.9531 0.9225 0.9505 0.9363 0.9540 0.9246 0.9493 0.9368 0.9545 0.9251 0.9479 0.9364 0.9542 0.9262 0.9472 0.9365 0.9544 0.9271 0.9468 0.9368 0.9546 0.9282 0.9463 0.9372 0.9549 0.9284 0.9460 0.9371 0.9549 0.9287 0.9453 0.9369 0.9548 0.9293 0.9449 0.9370 0.9549 0.9298 0.9449 0.9373 0.9550 0.9301 0.9445 0.9372 0.9550 0.9302 0.9440 0.9371 0.9549 0.9304 0.9437 0.9370 0.9549 0.9304 0.9435 0.9369 0.9548 0.9308 0.9434 0.9370 0.9549 0.9312 0.9432 0.9371 0.9550 0.9312 0.9431 0.9371 0.9550 0.9314 0.9429 0.9371 0.9550 0.9316 0.9428 0.9372 0.9551 0.9317 0.9428 0.9372 0.9551 0.9317 0.9423 0.9370 0.9549 0.9319 0.9421 0.9370 0.9550 0.9321 0.9421 0.9371 0.9550 0.9321 0.9420 0.9370 0.9550 0.9324 0.9420 0.9372 0.9551 0.9327 0.9419 0.9373 0.9552 0.9328 0.9418 0.9373 0.9552 0.9329 0.9418 0.9374 0.9553 0.9329 0.9417 0.9373 0.9552 0.9330 0.9415 0.9372 0.9552 0.9333 0.9412 0.9372 0.9552 0.9336 0.9409 0.9372 0.9552 0.9336 0.9409 0.9373 0.9552 0.9337 0.9408 0.9373 0.9552 0.9339 0.9408 0.9373 0.9553 0.9340 0.9407 0.9373 0.9553 0.9343 0.9406 0.9375 0.9554 0.9344 0.9405 0.9374 0.9554 0.9345 0.9404 0.9374 0.9554 0.9345 0.9402 0.9373 0.9553 0.9349 0.94 0.9374 0.9554 0.9353 0.9399 0.9376 0.9555 0.9356 0.9399 0.9378 0.9557 0.9356 0.9395 0.9376 0.9555 0.9357 0.9395 0.9376 0.9556 0.9358 0.9390 0.9374 0.9554 0.9360 0.9388 0.9374 0.9554 0.9360 0.9385 0.9373 0.9554 0.9363 0.9384 0.9374 0.9554 0.9364 0.9384 0.9374 0.9555 0.9366 0.9384 0.9375 0.9555 0.9366 0.9384 0.9375 0.9556 0.9368 0.9383 0.9376 0.9556 0.9370 0.9383 0.9377 0.9557 0.9374 0.9383 0.9379 0.9558 0.9374 0.9382 0.9378 0.9558 0.9374 0.9382 0.9378 0.9558 0.9374 0.9382 0.9378 0.9558 0.9375 0.9382 0.9378 0.9558 0.9376 0.9382 0.9379 0.9558 0.9379 0.9380 0.9379 0.9559 0.9381 0.9379 0.9380 0.9559 0.9381 0.9377 0.9379 0.9559 0.9383 0.9375 0.9379 0.9559 0.9386 0.9375 0.9380 0.9560 0.9387 0.9372 0.9379 0.9559 0.9387 0.9372 0.9379 0.9559 0.9388 0.9370 0.9379 0.9559 0.9389 0.9368 0.9378 0.9559 0.9391 0.9368 0.9380 0.9560 0.9393 0.9364 0.9378 0.9559 0.9393 0.9364 0.9378 0.9559 0.9393 0.9363 0.9378 0.9559 0.9396 0.9361 0.9378 0.9559 0.9398 0.9360 0.9379 0.9560 0.9404 0.9360 0.9382 0.9562 0.9407 0.9359 0.9383 0.9562 0.9409 0.9359 0.9384 0.9563 0.9410 0.9358 0.9384 0.9563 0.9411 0.9357 0.9384 0.9563 0.9413 0.9355 0.9384 0.9563 0.9413 0.9354 0.9383 0.9563 0.9420 0.9354 0.9387 0.9566 0.9423 0.9352 0.9388 0.9566 0.9428 0.9352 0.9390 0.9568 0.9432 0.9350 0.9391 0.9569 0.9438 0.9350 0.9393 0.9571 0.9442 0.9347 0.9394 0.9572 0.9449 0.9339 0.9394 0.9572 0.9454 0.9337 0.9395 0.9573 0.9459 0.9330 0.9394 0.9572 0.9463 0.9321 0.9392 0.9571 0.9469 0.9316 0.9392 0.9571 0.9481 0.9308 0.9394 0.9573 0.9491 0.9295 0.9392 0.9573 0.9501 0.9273 0.9386 0.9569 0.9549 0.9229 0.9387 0.9571
1.7597 2.9995 12477 0.3203 0.9384 0.9372 0.9378 0.9558 0.9411 0.9359 0.9385 0.9564 0.9417 0.9350 0.9384 0.9563 0.9421 0.9349 0.9385 0.9564 0.9429 0.9348 0.9388 0.9567 0.9431 0.9348 0.9389 0.9568 0.9432 0.9347 0.9389 0.9568 0.9437 0.9345 0.9391 0.9569 0.9437 0.9344 0.9390 0.9569 0.9438 0.9343 0.9391 0.9569 0.9439 0.9342 0.9390 0.9569 0.9440 0.9339 0.9389 0.9568 0.9442 0.9337 0.9389 0.9568 0.9444 0.9334 0.9389 0.9568 0.9448 0.9334 0.9391 0.9570 0.9449 0.9334 0.9391 0.9570 0.9450 0.9332 0.9391 0.9570 0.9450 0.9332 0.9391 0.9570 0.9452 0.9330 0.9391 0.9570 0.9454 0.9330 0.9392 0.9571 0.9455 0.9329 0.9392 0.9571 0.9455 0.9328 0.9391 0.9570 0.9455 0.9328 0.9391 0.9570 0.9456 0.9328 0.9391 0.9570 0.9456 0.9328 0.9391 0.9570 0.9457 0.9327 0.9391 0.9570 0.9458 0.9326 0.9392 0.9571 0.9459 0.9326 0.9392 0.9571 0.9460 0.9326 0.9392 0.9571 0.9461 0.9325 0.9392 0.9571 0.9462 0.9325 0.9393 0.9572 0.9462 0.9325 0.9393 0.9572 0.9464 0.9325 0.9394 0.9572 0.9465 0.9324 0.9394 0.9572 0.9464 0.9323 0.9393 0.9572 0.9467 0.9323 0.9394 0.9573 0.9467 0.9323 0.9394 0.9573 0.9467 0.9322 0.9394 0.9573 0.9467 0.9322 0.9394 0.9573 0.9467 0.9321 0.9393 0.9572 0.9467 0.9321 0.9393 0.9572 0.9467 0.9321 0.9393 0.9572 0.9468 0.9320 0.9393 0.9572 0.9470 0.9319 0.9394 0.9573 0.9469 0.9317 0.9393 0.9572 0.9469 0.9317 0.9393 0.9572 0.9469 0.9317 0.9393 0.9572 0.9470 0.9317 0.9393 0.9572 0.9471 0.9316 0.9393 0.9572 0.9471 0.9316 0.9393 0.9572 0.9471 0.9316 0.9393 0.9572 0.9472 0.9316 0.9393 0.9572 0.9473 0.9314 0.9393 0.9572 0.9473 0.9314 0.9393 0.9572 0.9474 0.9314 0.9393 0.9572 0.9474 0.9312 0.9392 0.9572 0.9474 0.9312 0.9392 0.9572 0.9473 0.9310 0.9391 0.9571 0.9473 0.9310 0.9391 0.9571 0.9474 0.9310 0.9392 0.9571 0.9474 0.9310 0.9392 0.9571 0.9475 0.9309 0.9391 0.9571 0.9476 0.9307 0.9391 0.9571 0.9476 0.9306 0.9390 0.9571 0.9477 0.9306 0.9390 0.9571 0.9477 0.9306 0.9390 0.9571 0.9477 0.9304 0.9389 0.9570 0.9478 0.9304 0.9390 0.9570 0.9477 0.9302 0.9389 0.9570 0.9479 0.9302 0.9390 0.9570 0.9479 0.9301 0.9389 0.9570 0.9480 0.9299 0.9389 0.9570 0.9481 0.9299 0.9389 0.9570 0.9481 0.9299 0.9389 0.9570 0.9482 0.9299 0.9390 0.9570 0.9482 0.9299 0.9390 0.9570 0.9483 0.9298 0.9389 0.9570 0.9483 0.9297 0.9389 0.9570 0.9484 0.9297 0.9390 0.9571 0.9485 0.9296 0.9390 0.9571 0.9485 0.9294 0.9389 0.9570 0.9485 0.9294 0.9389 0.9570 0.9486 0.9294 0.9389 0.9570 0.9488 0.9294 0.9390 0.9571 0.9491 0.9293 0.9391 0.9572 0.9495 0.9293 0.9393 0.9573 0.9495 0.9292 0.9392 0.9573 0.9496 0.9292 0.9393 0.9573 0.9497 0.9290 0.9392 0.9573 0.9499 0.9289 0.9393 0.9573 0.9501 0.9287 0.9393 0.9573 0.9503 0.9287 0.9394 0.9574 0.9507 0.9285 0.9395 0.9575 0.9509 0.9283 0.9394 0.9575 0.9512 0.9281 0.9395 0.9575 0.9515 0.9277 0.9395 0.9575 0.9520 0.9270 0.9393 0.9575 0.9525 0.9267 0.9394 0.9575 0.9537 0.9246 0.9389 0.9573

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0
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Evaluation results

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