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lobrien001
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roberta-base-finetuned-ner

Token Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
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  1. README.md +224 -13
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5385
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- - Precision: 0.8574
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- - Recall: 0.9049
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- - F1: 0.8805
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- - Accuracy: 0.8574
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  ## Model description
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@@ -44,25 +44,236 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 6
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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: 2
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.56 | 100 | 0.5431 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.12 | 200 | 0.5385 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.68 | 300 | 0.5434 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.36.2
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- - Pytorch 2.1.2
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- - Datasets 2.18.0
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  - Tokenizers 0.15.2
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2509
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+ - Precision: 0.9516
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+ - Recall: 0.9470
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+ - F1: 0.9493
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+ - Accuracy: 0.9421
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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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: 8
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.04 | 10 | 0.6589 | 0.7956 | 0.8579 | 0.8256 | 0.7956 |
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+ | No log | 0.07 | 20 | 0.6210 | 0.7957 | 0.8581 | 0.8257 | 0.7957 |
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+ | No log | 0.11 | 30 | 0.6019 | 0.7963 | 0.8588 | 0.8264 | 0.7963 |
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+ | No log | 0.15 | 40 | 0.5531 | 0.8059 | 0.8691 | 0.8364 | 0.8059 |
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+ | No log | 0.19 | 50 | 0.4662 | 0.8299 | 0.8949 | 0.8612 | 0.8299 |
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+ | No log | 0.22 | 60 | 0.4258 | 0.8314 | 0.8966 | 0.8628 | 0.8319 |
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+ | No log | 0.26 | 70 | 0.3505 | 0.8616 | 0.8971 | 0.8790 | 0.8645 |
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+ | No log | 0.3 | 80 | 0.3112 | 0.8915 | 0.8985 | 0.8950 | 0.8861 |
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+ | No log | 0.34 | 90 | 0.3130 | 0.8828 | 0.8873 | 0.8851 | 0.8788 |
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+ | No log | 0.37 | 100 | 0.3023 | 0.8968 | 0.8992 | 0.8980 | 0.8914 |
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+ | No log | 0.41 | 110 | 0.2629 | 0.8950 | 0.9175 | 0.9061 | 0.8991 |
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+ | No log | 0.45 | 120 | 0.3162 | 0.9203 | 0.8707 | 0.8948 | 0.8805 |
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+ | No log | 0.49 | 130 | 0.2644 | 0.9066 | 0.9260 | 0.9162 | 0.9070 |
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+ | No log | 0.52 | 140 | 0.2504 | 0.9305 | 0.9061 | 0.9181 | 0.9079 |
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+ | No log | 0.56 | 150 | 0.2418 | 0.9082 | 0.9233 | 0.9157 | 0.9084 |
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+ | No log | 0.6 | 160 | 0.2952 | 0.9199 | 0.8729 | 0.8958 | 0.8834 |
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+ | No log | 0.63 | 170 | 0.2392 | 0.8963 | 0.9223 | 0.9091 | 0.9038 |
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+ | No log | 0.67 | 180 | 0.2425 | 0.9291 | 0.9070 | 0.9179 | 0.9123 |
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+ | No log | 0.71 | 190 | 0.2673 | 0.9010 | 0.9022 | 0.9016 | 0.8999 |
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+ | No log | 0.75 | 200 | 0.2242 | 0.9353 | 0.9134 | 0.9243 | 0.9172 |
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+ | No log | 0.78 | 210 | 0.2446 | 0.9273 | 0.9089 | 0.9180 | 0.9130 |
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+ | No log | 0.82 | 220 | 0.2092 | 0.9396 | 0.9243 | 0.9319 | 0.9257 |
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+ | No log | 0.86 | 230 | 0.1965 | 0.9370 | 0.9358 | 0.9364 | 0.9317 |
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+ | No log | 0.9 | 240 | 0.2026 | 0.9348 | 0.9326 | 0.9337 | 0.9285 |
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+ | No log | 0.93 | 250 | 0.2119 | 0.9218 | 0.9308 | 0.9263 | 0.9216 |
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+ | No log | 0.97 | 260 | 0.2072 | 0.9420 | 0.9212 | 0.9315 | 0.9234 |
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+ | No log | 1.01 | 270 | 0.2058 | 0.9446 | 0.9255 | 0.9349 | 0.9287 |
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+ | No log | 1.04 | 280 | 0.1891 | 0.9319 | 0.9343 | 0.9331 | 0.9297 |
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+ | No log | 1.08 | 290 | 0.1907 | 0.9412 | 0.9350 | 0.9381 | 0.9344 |
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+ | No log | 1.12 | 300 | 0.2044 | 0.9405 | 0.9343 | 0.9374 | 0.9333 |
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+ | No log | 1.16 | 310 | 0.2324 | 0.9394 | 0.9235 | 0.9314 | 0.9248 |
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+ | No log | 1.19 | 320 | 0.2054 | 0.9427 | 0.9321 | 0.9374 | 0.9317 |
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+ | No log | 1.23 | 330 | 0.1987 | 0.9399 | 0.9340 | 0.9369 | 0.9320 |
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+ | No log | 1.27 | 340 | 0.2008 | 0.9370 | 0.9369 | 0.9369 | 0.9312 |
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+ | No log | 1.31 | 350 | 0.2107 | 0.9438 | 0.9243 | 0.9340 | 0.9268 |
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+ | No log | 1.34 | 360 | 0.2128 | 0.9381 | 0.9357 | 0.9369 | 0.9306 |
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+ | No log | 1.38 | 370 | 0.2068 | 0.9441 | 0.9401 | 0.9421 | 0.9353 |
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+ | No log | 1.42 | 380 | 0.1969 | 0.9407 | 0.9394 | 0.9400 | 0.9355 |
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+ | No log | 1.46 | 390 | 0.2100 | 0.9392 | 0.9202 | 0.9296 | 0.9227 |
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+ | No log | 1.49 | 400 | 0.1763 | 0.9435 | 0.9414 | 0.9425 | 0.9377 |
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+ | No log | 1.53 | 410 | 0.1772 | 0.9417 | 0.9379 | 0.9398 | 0.9355 |
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+ | No log | 1.57 | 420 | 0.1824 | 0.9412 | 0.9345 | 0.9378 | 0.9330 |
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+ | No log | 1.6 | 430 | 0.1981 | 0.9388 | 0.9289 | 0.9338 | 0.9290 |
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+ | No log | 1.64 | 440 | 0.2020 | 0.9325 | 0.9282 | 0.9303 | 0.9248 |
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+ | No log | 1.68 | 450 | 0.1802 | 0.9467 | 0.9370 | 0.9418 | 0.9363 |
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+ | No log | 1.72 | 460 | 0.1772 | 0.9456 | 0.9413 | 0.9434 | 0.9374 |
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+ | No log | 1.75 | 470 | 0.1804 | 0.9461 | 0.9358 | 0.9410 | 0.9344 |
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+ | No log | 1.79 | 480 | 0.1889 | 0.9447 | 0.9335 | 0.9390 | 0.9326 |
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+ | No log | 1.83 | 490 | 0.1804 | 0.9407 | 0.9377 | 0.9392 | 0.9330 |
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+ | 0.3367 | 1.87 | 500 | 0.1824 | 0.9397 | 0.9421 | 0.9409 | 0.9352 |
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+ | 0.3367 | 1.9 | 510 | 0.1991 | 0.9417 | 0.9299 | 0.9358 | 0.9282 |
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+ | 0.3367 | 1.94 | 520 | 0.1835 | 0.9453 | 0.9411 | 0.9432 | 0.9359 |
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+ | 0.3367 | 1.98 | 530 | 0.1915 | 0.9453 | 0.9328 | 0.9390 | 0.9320 |
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+ | 0.3367 | 2.01 | 540 | 0.2191 | 0.9452 | 0.9365 | 0.9408 | 0.9342 |
112
+ | 0.3367 | 2.05 | 550 | 0.2003 | 0.9514 | 0.9437 | 0.9475 | 0.9411 |
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+ | 0.3367 | 2.09 | 560 | 0.2072 | 0.9491 | 0.9438 | 0.9465 | 0.9411 |
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+ | 0.3367 | 2.13 | 570 | 0.2081 | 0.9432 | 0.9409 | 0.9421 | 0.9370 |
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+ | 0.3367 | 2.16 | 580 | 0.1857 | 0.9409 | 0.9425 | 0.9417 | 0.9358 |
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+ | 0.3367 | 2.2 | 590 | 0.1822 | 0.9452 | 0.9397 | 0.9425 | 0.9370 |
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+ | 0.3367 | 2.24 | 600 | 0.1721 | 0.9438 | 0.9428 | 0.9433 | 0.9386 |
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+ | 0.3367 | 2.28 | 610 | 0.1874 | 0.9400 | 0.9440 | 0.9420 | 0.9377 |
119
+ | 0.3367 | 2.31 | 620 | 0.1716 | 0.9496 | 0.9430 | 0.9463 | 0.9400 |
120
+ | 0.3367 | 2.35 | 630 | 0.2026 | 0.9445 | 0.9272 | 0.9358 | 0.9292 |
121
+ | 0.3367 | 2.39 | 640 | 0.1974 | 0.9450 | 0.9360 | 0.9405 | 0.9345 |
122
+ | 0.3367 | 2.43 | 650 | 0.1721 | 0.95 | 0.9481 | 0.9490 | 0.9438 |
123
+ | 0.3367 | 2.46 | 660 | 0.1715 | 0.9504 | 0.9392 | 0.9448 | 0.9393 |
124
+ | 0.3367 | 2.5 | 670 | 0.1841 | 0.9502 | 0.9391 | 0.9446 | 0.9378 |
125
+ | 0.3367 | 2.54 | 680 | 0.1882 | 0.9486 | 0.9391 | 0.9438 | 0.9381 |
126
+ | 0.3367 | 2.57 | 690 | 0.1938 | 0.9519 | 0.9409 | 0.9464 | 0.9399 |
127
+ | 0.3367 | 2.61 | 700 | 0.2124 | 0.9475 | 0.9333 | 0.9403 | 0.9339 |
128
+ | 0.3367 | 2.65 | 710 | 0.1842 | 0.9484 | 0.9350 | 0.9416 | 0.9358 |
129
+ | 0.3367 | 2.69 | 720 | 0.1845 | 0.9529 | 0.9367 | 0.9447 | 0.9380 |
130
+ | 0.3367 | 2.72 | 730 | 0.1995 | 0.9366 | 0.9396 | 0.9381 | 0.9309 |
131
+ | 0.3367 | 2.76 | 740 | 0.1764 | 0.9511 | 0.9413 | 0.9462 | 0.9400 |
132
+ | 0.3367 | 2.8 | 750 | 0.1947 | 0.9532 | 0.9367 | 0.9449 | 0.9388 |
133
+ | 0.3367 | 2.84 | 760 | 0.2048 | 0.9529 | 0.9384 | 0.9456 | 0.9394 |
134
+ | 0.3367 | 2.87 | 770 | 0.1922 | 0.9516 | 0.9420 | 0.9468 | 0.9405 |
135
+ | 0.3367 | 2.91 | 780 | 0.1872 | 0.9479 | 0.9442 | 0.9460 | 0.9404 |
136
+ | 0.3367 | 2.95 | 790 | 0.1808 | 0.9524 | 0.9379 | 0.9451 | 0.9383 |
137
+ | 0.3367 | 2.99 | 800 | 0.1765 | 0.9494 | 0.9403 | 0.9448 | 0.9383 |
138
+ | 0.3367 | 3.02 | 810 | 0.1989 | 0.9503 | 0.9414 | 0.9459 | 0.9393 |
139
+ | 0.3367 | 3.06 | 820 | 0.2081 | 0.9502 | 0.9382 | 0.9442 | 0.9366 |
140
+ | 0.3367 | 3.1 | 830 | 0.2044 | 0.9496 | 0.9377 | 0.9436 | 0.9363 |
141
+ | 0.3367 | 3.13 | 840 | 0.2019 | 0.9488 | 0.9413 | 0.9450 | 0.9391 |
142
+ | 0.3367 | 3.17 | 850 | 0.2129 | 0.9503 | 0.9355 | 0.9429 | 0.9348 |
143
+ | 0.3367 | 3.21 | 860 | 0.2410 | 0.9359 | 0.9440 | 0.9399 | 0.9319 |
144
+ | 0.3367 | 3.25 | 870 | 0.2004 | 0.9458 | 0.9396 | 0.9427 | 0.9361 |
145
+ | 0.3367 | 3.28 | 880 | 0.2046 | 0.9491 | 0.9375 | 0.9433 | 0.9359 |
146
+ | 0.3367 | 3.32 | 890 | 0.2013 | 0.9509 | 0.9409 | 0.9459 | 0.9394 |
147
+ | 0.3367 | 3.36 | 900 | 0.2059 | 0.9501 | 0.9437 | 0.9469 | 0.9411 |
148
+ | 0.3367 | 3.4 | 910 | 0.2091 | 0.9465 | 0.9425 | 0.9445 | 0.9377 |
149
+ | 0.3367 | 3.43 | 920 | 0.1983 | 0.9492 | 0.9389 | 0.9440 | 0.9374 |
150
+ | 0.3367 | 3.47 | 930 | 0.1956 | 0.9489 | 0.9386 | 0.9437 | 0.9369 |
151
+ | 0.3367 | 3.51 | 940 | 0.2067 | 0.9436 | 0.9448 | 0.9442 | 0.9366 |
152
+ | 0.3367 | 3.54 | 950 | 0.1906 | 0.9506 | 0.9438 | 0.9472 | 0.9402 |
153
+ | 0.3367 | 3.58 | 960 | 0.1905 | 0.9517 | 0.9469 | 0.9493 | 0.9424 |
154
+ | 0.3367 | 3.62 | 970 | 0.1979 | 0.9522 | 0.9501 | 0.9512 | 0.9449 |
155
+ | 0.3367 | 3.66 | 980 | 0.2024 | 0.9535 | 0.9442 | 0.9488 | 0.9424 |
156
+ | 0.3367 | 3.69 | 990 | 0.1967 | 0.9528 | 0.9491 | 0.9509 | 0.9454 |
157
+ | 0.1663 | 3.73 | 1000 | 0.1872 | 0.9526 | 0.9479 | 0.9502 | 0.9446 |
158
+ | 0.1663 | 3.77 | 1010 | 0.1871 | 0.9500 | 0.9457 | 0.9479 | 0.9424 |
159
+ | 0.1663 | 3.81 | 1020 | 0.2167 | 0.9541 | 0.9355 | 0.9447 | 0.9359 |
160
+ | 0.1663 | 3.84 | 1030 | 0.2093 | 0.9474 | 0.9379 | 0.9426 | 0.9350 |
161
+ | 0.1663 | 3.88 | 1040 | 0.1926 | 0.9498 | 0.9448 | 0.9473 | 0.9408 |
162
+ | 0.1663 | 3.92 | 1050 | 0.1832 | 0.9480 | 0.9467 | 0.9474 | 0.9421 |
163
+ | 0.1663 | 3.96 | 1060 | 0.1896 | 0.9518 | 0.9479 | 0.9498 | 0.9443 |
164
+ | 0.1663 | 3.99 | 1070 | 0.1981 | 0.9525 | 0.9430 | 0.9477 | 0.9405 |
165
+ | 0.1663 | 4.03 | 1080 | 0.1938 | 0.9536 | 0.9460 | 0.9498 | 0.9430 |
166
+ | 0.1663 | 4.07 | 1090 | 0.1994 | 0.9484 | 0.9511 | 0.9498 | 0.9426 |
167
+ | 0.1663 | 4.1 | 1100 | 0.2185 | 0.9542 | 0.9396 | 0.9468 | 0.9388 |
168
+ | 0.1663 | 4.14 | 1110 | 0.2393 | 0.9514 | 0.9431 | 0.9472 | 0.9402 |
169
+ | 0.1663 | 4.18 | 1120 | 0.2176 | 0.9465 | 0.9510 | 0.9487 | 0.9408 |
170
+ | 0.1663 | 4.22 | 1130 | 0.1888 | 0.9522 | 0.9459 | 0.9490 | 0.9432 |
171
+ | 0.1663 | 4.25 | 1140 | 0.1814 | 0.9557 | 0.9486 | 0.9521 | 0.9463 |
172
+ | 0.1663 | 4.29 | 1150 | 0.1957 | 0.9525 | 0.9431 | 0.9478 | 0.9418 |
173
+ | 0.1663 | 4.33 | 1160 | 0.1956 | 0.9516 | 0.9481 | 0.9498 | 0.9440 |
174
+ | 0.1663 | 4.37 | 1170 | 0.2078 | 0.9511 | 0.9476 | 0.9493 | 0.9429 |
175
+ | 0.1663 | 4.4 | 1180 | 0.2329 | 0.9533 | 0.9392 | 0.9462 | 0.9393 |
176
+ | 0.1663 | 4.44 | 1190 | 0.2207 | 0.9507 | 0.9460 | 0.9484 | 0.9418 |
177
+ | 0.1663 | 4.48 | 1200 | 0.2181 | 0.9456 | 0.9493 | 0.9474 | 0.9404 |
178
+ | 0.1663 | 4.51 | 1210 | 0.2079 | 0.9499 | 0.9455 | 0.9477 | 0.9416 |
179
+ | 0.1663 | 4.55 | 1220 | 0.2069 | 0.9532 | 0.9462 | 0.9497 | 0.9438 |
180
+ | 0.1663 | 4.59 | 1230 | 0.1981 | 0.9512 | 0.9457 | 0.9484 | 0.9429 |
181
+ | 0.1663 | 4.63 | 1240 | 0.2001 | 0.9443 | 0.9433 | 0.9438 | 0.9385 |
182
+ | 0.1663 | 4.66 | 1250 | 0.2096 | 0.9535 | 0.9430 | 0.9482 | 0.9421 |
183
+ | 0.1663 | 4.7 | 1260 | 0.2253 | 0.9554 | 0.9418 | 0.9485 | 0.9415 |
184
+ | 0.1663 | 4.74 | 1270 | 0.2142 | 0.9513 | 0.9455 | 0.9484 | 0.9418 |
185
+ | 0.1663 | 4.78 | 1280 | 0.2079 | 0.9479 | 0.9476 | 0.9477 | 0.9402 |
186
+ | 0.1663 | 4.81 | 1290 | 0.1946 | 0.9507 | 0.9496 | 0.9502 | 0.9437 |
187
+ | 0.1663 | 4.85 | 1300 | 0.1999 | 0.9510 | 0.9482 | 0.9496 | 0.9429 |
188
+ | 0.1663 | 4.89 | 1310 | 0.1958 | 0.9521 | 0.9477 | 0.9499 | 0.9440 |
189
+ | 0.1663 | 4.93 | 1320 | 0.1933 | 0.9537 | 0.9477 | 0.9507 | 0.9444 |
190
+ | 0.1663 | 4.96 | 1330 | 0.2032 | 0.9535 | 0.9474 | 0.9505 | 0.9438 |
191
+ | 0.1663 | 5.0 | 1340 | 0.2000 | 0.9516 | 0.9448 | 0.9482 | 0.9419 |
192
+ | 0.1663 | 5.04 | 1350 | 0.1969 | 0.9485 | 0.9448 | 0.9467 | 0.9404 |
193
+ | 0.1663 | 5.07 | 1360 | 0.2052 | 0.9486 | 0.9435 | 0.9461 | 0.9396 |
194
+ | 0.1663 | 5.11 | 1370 | 0.2199 | 0.9489 | 0.9459 | 0.9474 | 0.9405 |
195
+ | 0.1663 | 5.15 | 1380 | 0.2267 | 0.9529 | 0.9411 | 0.9470 | 0.9400 |
196
+ | 0.1663 | 5.19 | 1390 | 0.2190 | 0.9504 | 0.9462 | 0.9483 | 0.9424 |
197
+ | 0.1663 | 5.22 | 1400 | 0.2226 | 0.9462 | 0.9486 | 0.9474 | 0.9404 |
198
+ | 0.1663 | 5.26 | 1410 | 0.2206 | 0.9540 | 0.9464 | 0.9502 | 0.9437 |
199
+ | 0.1663 | 5.3 | 1420 | 0.2183 | 0.9527 | 0.9472 | 0.9500 | 0.9440 |
200
+ | 0.1663 | 5.34 | 1430 | 0.2101 | 0.9503 | 0.9501 | 0.9502 | 0.9448 |
201
+ | 0.1663 | 5.37 | 1440 | 0.2014 | 0.9507 | 0.9501 | 0.9504 | 0.9449 |
202
+ | 0.1663 | 5.41 | 1450 | 0.2057 | 0.9499 | 0.9494 | 0.9497 | 0.9443 |
203
+ | 0.1663 | 5.45 | 1460 | 0.2072 | 0.9499 | 0.9493 | 0.9496 | 0.9440 |
204
+ | 0.1663 | 5.49 | 1470 | 0.2105 | 0.9517 | 0.9457 | 0.9487 | 0.9429 |
205
+ | 0.1663 | 5.52 | 1480 | 0.2173 | 0.9512 | 0.9470 | 0.9491 | 0.9435 |
206
+ | 0.1663 | 5.56 | 1490 | 0.2263 | 0.9530 | 0.9457 | 0.9493 | 0.9429 |
207
+ | 0.111 | 5.6 | 1500 | 0.2242 | 0.9535 | 0.9457 | 0.9496 | 0.9433 |
208
+ | 0.111 | 5.63 | 1510 | 0.2232 | 0.9496 | 0.9467 | 0.9482 | 0.9411 |
209
+ | 0.111 | 5.67 | 1520 | 0.2240 | 0.9477 | 0.9477 | 0.9477 | 0.9396 |
210
+ | 0.111 | 5.71 | 1530 | 0.2250 | 0.9474 | 0.9472 | 0.9473 | 0.9394 |
211
+ | 0.111 | 5.75 | 1540 | 0.2370 | 0.9431 | 0.9459 | 0.9445 | 0.9367 |
212
+ | 0.111 | 5.78 | 1550 | 0.2405 | 0.9504 | 0.9469 | 0.9486 | 0.9411 |
213
+ | 0.111 | 5.82 | 1560 | 0.2401 | 0.9543 | 0.9459 | 0.9501 | 0.9429 |
214
+ | 0.111 | 5.86 | 1570 | 0.2339 | 0.9538 | 0.9462 | 0.9500 | 0.9435 |
215
+ | 0.111 | 5.9 | 1580 | 0.2228 | 0.9522 | 0.9469 | 0.9495 | 0.9429 |
216
+ | 0.111 | 5.93 | 1590 | 0.2139 | 0.9515 | 0.9493 | 0.9504 | 0.9448 |
217
+ | 0.111 | 5.97 | 1600 | 0.2121 | 0.9517 | 0.9489 | 0.9503 | 0.9448 |
218
+ | 0.111 | 6.01 | 1610 | 0.2090 | 0.9496 | 0.9501 | 0.9499 | 0.9443 |
219
+ | 0.111 | 6.04 | 1620 | 0.2147 | 0.9519 | 0.9465 | 0.9492 | 0.9435 |
220
+ | 0.111 | 6.08 | 1630 | 0.2225 | 0.9536 | 0.9445 | 0.9490 | 0.9421 |
221
+ | 0.111 | 6.12 | 1640 | 0.2244 | 0.9514 | 0.9469 | 0.9491 | 0.9424 |
222
+ | 0.111 | 6.16 | 1650 | 0.2240 | 0.9515 | 0.9482 | 0.9498 | 0.9427 |
223
+ | 0.111 | 6.19 | 1660 | 0.2294 | 0.9514 | 0.9472 | 0.9493 | 0.9426 |
224
+ | 0.111 | 6.23 | 1670 | 0.2343 | 0.9524 | 0.9469 | 0.9496 | 0.9430 |
225
+ | 0.111 | 6.27 | 1680 | 0.2292 | 0.9507 | 0.9486 | 0.9496 | 0.9432 |
226
+ | 0.111 | 6.31 | 1690 | 0.2287 | 0.9508 | 0.9470 | 0.9489 | 0.9427 |
227
+ | 0.111 | 6.34 | 1700 | 0.2339 | 0.9523 | 0.9459 | 0.9491 | 0.9424 |
228
+ | 0.111 | 6.38 | 1710 | 0.2368 | 0.9502 | 0.9465 | 0.9484 | 0.9413 |
229
+ | 0.111 | 6.42 | 1720 | 0.2342 | 0.9489 | 0.9460 | 0.9475 | 0.9408 |
230
+ | 0.111 | 6.46 | 1730 | 0.2319 | 0.9502 | 0.9462 | 0.9482 | 0.9410 |
231
+ | 0.111 | 6.49 | 1740 | 0.2293 | 0.9507 | 0.9455 | 0.9481 | 0.9413 |
232
+ | 0.111 | 6.53 | 1750 | 0.2268 | 0.9497 | 0.9448 | 0.9473 | 0.9404 |
233
+ | 0.111 | 6.57 | 1760 | 0.2296 | 0.9495 | 0.9479 | 0.9487 | 0.9407 |
234
+ | 0.111 | 6.6 | 1770 | 0.2351 | 0.9469 | 0.9470 | 0.9470 | 0.9391 |
235
+ | 0.111 | 6.64 | 1780 | 0.2373 | 0.9501 | 0.9469 | 0.9485 | 0.9410 |
236
+ | 0.111 | 6.68 | 1790 | 0.2386 | 0.9477 | 0.9450 | 0.9464 | 0.9393 |
237
+ | 0.111 | 6.72 | 1800 | 0.2345 | 0.9490 | 0.9469 | 0.9479 | 0.9404 |
238
+ | 0.111 | 6.75 | 1810 | 0.2331 | 0.9512 | 0.9462 | 0.9487 | 0.9422 |
239
+ | 0.111 | 6.79 | 1820 | 0.2316 | 0.9519 | 0.9467 | 0.9493 | 0.9427 |
240
+ | 0.111 | 6.83 | 1830 | 0.2346 | 0.9501 | 0.9470 | 0.9486 | 0.9415 |
241
+ | 0.111 | 6.87 | 1840 | 0.2307 | 0.9503 | 0.9470 | 0.9487 | 0.9418 |
242
+ | 0.111 | 6.9 | 1850 | 0.2342 | 0.9518 | 0.9453 | 0.9486 | 0.9418 |
243
+ | 0.111 | 6.94 | 1860 | 0.2373 | 0.9514 | 0.9445 | 0.9480 | 0.9410 |
244
+ | 0.111 | 6.98 | 1870 | 0.2364 | 0.9502 | 0.9462 | 0.9482 | 0.9411 |
245
+ | 0.111 | 7.01 | 1880 | 0.2354 | 0.9501 | 0.9477 | 0.9489 | 0.9415 |
246
+ | 0.111 | 7.05 | 1890 | 0.2374 | 0.9505 | 0.9474 | 0.9489 | 0.9418 |
247
+ | 0.111 | 7.09 | 1900 | 0.2394 | 0.9509 | 0.9474 | 0.9492 | 0.9421 |
248
+ | 0.111 | 7.13 | 1910 | 0.2398 | 0.9503 | 0.9476 | 0.9489 | 0.9421 |
249
+ | 0.111 | 7.16 | 1920 | 0.2416 | 0.9513 | 0.9477 | 0.9495 | 0.9427 |
250
+ | 0.111 | 7.2 | 1930 | 0.2440 | 0.9516 | 0.9469 | 0.9492 | 0.9421 |
251
+ | 0.111 | 7.24 | 1940 | 0.2472 | 0.9519 | 0.9470 | 0.9495 | 0.9422 |
252
+ | 0.111 | 7.28 | 1950 | 0.2483 | 0.9524 | 0.9474 | 0.9499 | 0.9422 |
253
+ | 0.111 | 7.31 | 1960 | 0.2493 | 0.9516 | 0.9479 | 0.9497 | 0.9421 |
254
+ | 0.111 | 7.35 | 1970 | 0.2485 | 0.9514 | 0.9477 | 0.9496 | 0.9419 |
255
+ | 0.111 | 7.39 | 1980 | 0.2484 | 0.9526 | 0.9486 | 0.9506 | 0.9432 |
256
+ | 0.111 | 7.43 | 1990 | 0.2522 | 0.9532 | 0.9477 | 0.9505 | 0.9432 |
257
+ | 0.0708 | 7.46 | 2000 | 0.2532 | 0.9532 | 0.9477 | 0.9505 | 0.9435 |
258
+ | 0.0708 | 7.5 | 2010 | 0.2544 | 0.9536 | 0.9482 | 0.9509 | 0.9438 |
259
+ | 0.0708 | 7.54 | 2020 | 0.2534 | 0.9520 | 0.9484 | 0.9502 | 0.9432 |
260
+ | 0.0708 | 7.57 | 2030 | 0.2524 | 0.9510 | 0.9479 | 0.9494 | 0.9419 |
261
+ | 0.0708 | 7.61 | 2040 | 0.2519 | 0.9495 | 0.9472 | 0.9483 | 0.9407 |
262
+ | 0.0708 | 7.65 | 2050 | 0.2520 | 0.9503 | 0.9484 | 0.9494 | 0.9416 |
263
+ | 0.0708 | 7.69 | 2060 | 0.2516 | 0.9512 | 0.9486 | 0.9499 | 0.9422 |
264
+ | 0.0708 | 7.72 | 2070 | 0.2514 | 0.9513 | 0.9487 | 0.9500 | 0.9426 |
265
+ | 0.0708 | 7.76 | 2080 | 0.2504 | 0.9517 | 0.9487 | 0.9502 | 0.9429 |
266
+ | 0.0708 | 7.8 | 2090 | 0.2500 | 0.9511 | 0.9472 | 0.9491 | 0.9421 |
267
+ | 0.0708 | 7.84 | 2100 | 0.2502 | 0.9514 | 0.9469 | 0.9491 | 0.9419 |
268
+ | 0.0708 | 7.87 | 2110 | 0.2504 | 0.9521 | 0.9474 | 0.9497 | 0.9426 |
269
+ | 0.0708 | 7.91 | 2120 | 0.2504 | 0.9517 | 0.9472 | 0.9495 | 0.9422 |
270
+ | 0.0708 | 7.95 | 2130 | 0.2507 | 0.9517 | 0.9472 | 0.9495 | 0.9422 |
271
+ | 0.0708 | 7.99 | 2140 | 0.2509 | 0.9516 | 0.9470 | 0.9493 | 0.9421 |
272
 
273
 
274
  ### Framework versions
275
 
276
  - Transformers 4.36.2
277
+ - Pytorch 2.2.1+cu121
278
+ - Datasets 2.19.0
279
  - Tokenizers 0.15.2
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