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distilbert-base-uncased-tokenclassification_lora

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  2. adapter_model.safetensors +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3105
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- - Precision: 0.3066
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- - Recall: 0.0502
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- - F1: 0.0863
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- - Accuracy: 0.9250
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  ## Model description
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@@ -50,22 +50,62 @@ The following hyperparameters were used during training:
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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: 10
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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 | 1.0 | 213 | 0.4763 | 0.0 | 0.0 | 0.0 | 0.9205 |
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- | No log | 2.0 | 426 | 0.4288 | 0.0 | 0.0 | 0.0 | 0.9205 |
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- | 0.7889 | 3.0 | 639 | 0.3761 | 0.0 | 0.0 | 0.0 | 0.9205 |
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- | 0.7889 | 4.0 | 852 | 0.3432 | 0.0 | 0.0 | 0.0 | 0.9205 |
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- | 0.2304 | 5.0 | 1065 | 0.3292 | 0.0 | 0.0 | 0.0 | 0.9206 |
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- | 0.2304 | 6.0 | 1278 | 0.3225 | 0.2424 | 0.0096 | 0.0184 | 0.9216 |
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- | 0.2304 | 7.0 | 1491 | 0.3163 | 0.2727 | 0.0251 | 0.0460 | 0.9231 |
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- | 0.2049 | 8.0 | 1704 | 0.3125 | 0.275 | 0.0395 | 0.0690 | 0.9243 |
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- | 0.2049 | 9.0 | 1917 | 0.3102 | 0.3037 | 0.0490 | 0.0844 | 0.9249 |
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- | 0.1953 | 10.0 | 2130 | 0.3105 | 0.3066 | 0.0502 | 0.0863 | 0.9250 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2271
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+ - Precision: 0.6508
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+ - Recall: 0.4569
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+ - F1: 0.5369
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+ - Accuracy: 0.9499
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  ## Model description
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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: 50
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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 | 1.0 | 213 | 0.4720 | 0.0 | 0.0 | 0.0 | 0.9205 |
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+ | No log | 2.0 | 426 | 0.4277 | 0.0 | 0.0 | 0.0 | 0.9205 |
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+ | 0.7721 | 3.0 | 639 | 0.3571 | 0.0 | 0.0 | 0.0 | 0.9205 |
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+ | 0.7721 | 4.0 | 852 | 0.3260 | 0.1818 | 0.0024 | 0.0047 | 0.9207 |
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+ | 0.2205 | 5.0 | 1065 | 0.3117 | 0.2868 | 0.0443 | 0.0767 | 0.9245 |
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+ | 0.2205 | 6.0 | 1278 | 0.3038 | 0.3644 | 0.1077 | 0.1662 | 0.9285 |
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+ | 0.2205 | 7.0 | 1491 | 0.2997 | 0.3854 | 0.1328 | 0.1975 | 0.9301 |
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+ | 0.1905 | 8.0 | 1704 | 0.2960 | 0.4263 | 0.1902 | 0.2630 | 0.9331 |
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+ | 0.1905 | 9.0 | 1917 | 0.2795 | 0.4469 | 0.2620 | 0.3303 | 0.9369 |
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+ | 0.1725 | 10.0 | 2130 | 0.2806 | 0.4607 | 0.2667 | 0.3379 | 0.9376 |
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+ | 0.1725 | 11.0 | 2343 | 0.2693 | 0.4614 | 0.3002 | 0.3638 | 0.9392 |
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+ | 0.1651 | 12.0 | 2556 | 0.2736 | 0.4779 | 0.2978 | 0.3670 | 0.9392 |
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+ | 0.1651 | 13.0 | 2769 | 0.2638 | 0.4946 | 0.3301 | 0.3960 | 0.9407 |
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+ | 0.1651 | 14.0 | 2982 | 0.2645 | 0.5294 | 0.3230 | 0.4012 | 0.9407 |
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+ | 0.1507 | 15.0 | 3195 | 0.2619 | 0.5399 | 0.3397 | 0.4170 | 0.9416 |
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+ | 0.1507 | 16.0 | 3408 | 0.2579 | 0.5630 | 0.3529 | 0.4338 | 0.9423 |
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+ | 0.1426 | 17.0 | 3621 | 0.2513 | 0.5717 | 0.3624 | 0.4436 | 0.9430 |
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+ | 0.1426 | 18.0 | 3834 | 0.2476 | 0.5686 | 0.3768 | 0.4532 | 0.9437 |
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+ | 0.136 | 19.0 | 4047 | 0.2463 | 0.5871 | 0.3911 | 0.4695 | 0.9445 |
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+ | 0.136 | 20.0 | 4260 | 0.2487 | 0.6185 | 0.3840 | 0.4738 | 0.9445 |
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+ | 0.136 | 21.0 | 4473 | 0.2448 | 0.6157 | 0.3947 | 0.4810 | 0.9451 |
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+ | 0.1302 | 22.0 | 4686 | 0.2431 | 0.6271 | 0.3983 | 0.4872 | 0.9459 |
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+ | 0.1302 | 23.0 | 4899 | 0.2352 | 0.6195 | 0.4187 | 0.4996 | 0.9464 |
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+ | 0.1268 | 24.0 | 5112 | 0.2393 | 0.6362 | 0.4163 | 0.5033 | 0.9467 |
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+ | 0.1268 | 25.0 | 5325 | 0.2413 | 0.6477 | 0.4091 | 0.5015 | 0.9467 |
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+ | 0.1216 | 26.0 | 5538 | 0.2344 | 0.6444 | 0.4270 | 0.5137 | 0.9475 |
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+ | 0.1216 | 27.0 | 5751 | 0.2351 | 0.6491 | 0.4270 | 0.5152 | 0.9475 |
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+ | 0.1216 | 28.0 | 5964 | 0.2306 | 0.6357 | 0.4426 | 0.5219 | 0.9484 |
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+ | 0.1174 | 29.0 | 6177 | 0.2341 | 0.65 | 0.4354 | 0.5215 | 0.9482 |
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+ | 0.1174 | 30.0 | 6390 | 0.2334 | 0.6501 | 0.4378 | 0.5232 | 0.9483 |
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+ | 0.1149 | 31.0 | 6603 | 0.2316 | 0.6390 | 0.4426 | 0.5230 | 0.9488 |
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+ | 0.1149 | 32.0 | 6816 | 0.2324 | 0.6435 | 0.4426 | 0.5245 | 0.9486 |
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+ | 0.1129 | 33.0 | 7029 | 0.2345 | 0.6570 | 0.4330 | 0.5220 | 0.9484 |
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+ | 0.1129 | 34.0 | 7242 | 0.2339 | 0.6465 | 0.4354 | 0.5204 | 0.9482 |
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+ | 0.1129 | 35.0 | 7455 | 0.2326 | 0.6490 | 0.4402 | 0.5246 | 0.9489 |
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+ | 0.1112 | 36.0 | 7668 | 0.2288 | 0.6489 | 0.4510 | 0.5321 | 0.9494 |
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+ | 0.1112 | 37.0 | 7881 | 0.2295 | 0.6512 | 0.4533 | 0.5346 | 0.9495 |
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+ | 0.1089 | 38.0 | 8094 | 0.2292 | 0.6462 | 0.4522 | 0.5320 | 0.9496 |
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+ | 0.1089 | 39.0 | 8307 | 0.2268 | 0.6491 | 0.4557 | 0.5355 | 0.9498 |
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+ | 0.1076 | 40.0 | 8520 | 0.2317 | 0.6510 | 0.4486 | 0.5312 | 0.9494 |
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+ | 0.1076 | 41.0 | 8733 | 0.2283 | 0.6467 | 0.4510 | 0.5314 | 0.9495 |
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+ | 0.1076 | 42.0 | 8946 | 0.2281 | 0.6519 | 0.4569 | 0.5373 | 0.9500 |
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+ | 0.1079 | 43.0 | 9159 | 0.2276 | 0.6492 | 0.4581 | 0.5372 | 0.9500 |
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+ | 0.1079 | 44.0 | 9372 | 0.2265 | 0.6486 | 0.4593 | 0.5378 | 0.9500 |
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+ | 0.1039 | 45.0 | 9585 | 0.2272 | 0.6486 | 0.4569 | 0.5361 | 0.9499 |
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+ | 0.1039 | 46.0 | 9798 | 0.2274 | 0.6546 | 0.4533 | 0.5357 | 0.9499 |
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+ | 0.1058 | 47.0 | 10011 | 0.2261 | 0.6514 | 0.4605 | 0.5396 | 0.9501 |
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+ | 0.1058 | 48.0 | 10224 | 0.2268 | 0.6503 | 0.4581 | 0.5375 | 0.9500 |
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+ | 0.1058 | 49.0 | 10437 | 0.2274 | 0.6519 | 0.4569 | 0.5373 | 0.9500 |
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+ | 0.1064 | 50.0 | 10650 | 0.2271 | 0.6508 | 0.4569 | 0.5369 | 0.9499 |
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  ### Framework versions
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