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basic_wnut

eren23 / basic_wnut

Run on huggingface.co
huggingface.co
Total runs: 14
24-hour runs: 0
7-day runs: 1
30-day runs: 9
Model's Last Updated: November 19 2023
token-classification

Introduction of basic_wnut

Model Details of basic_wnut

basic_wnut

This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3181
  • Precision: 0.5470
  • Recall: 0.3994
  • F1: 0.4617
  • Accuracy: 0.9469
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: 5e-07
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100
Training results
Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 27 0.2984 0.5557 0.3930 0.4604 0.9463
No log 2.0 54 0.2991 0.5547 0.3902 0.4581 0.9462
No log 3.0 81 0.2993 0.5557 0.3930 0.4604 0.9463
No log 4.0 108 0.3011 0.5550 0.3883 0.4569 0.9461
No log 5.0 135 0.3015 0.5532 0.3902 0.4576 0.9462
No log 6.0 162 0.2997 0.5467 0.3957 0.4591 0.9463
No log 7.0 189 0.2997 0.5487 0.3967 0.4605 0.9462
No log 8.0 216 0.2998 0.5439 0.3957 0.4582 0.9463
No log 9.0 243 0.3024 0.5501 0.3920 0.4578 0.9462
No log 10.0 270 0.3021 0.5470 0.3939 0.4580 0.9462
No log 11.0 297 0.3027 0.5471 0.3930 0.4574 0.9463
No log 12.0 324 0.3023 0.5453 0.3957 0.4586 0.9463
No log 13.0 351 0.3028 0.5481 0.3957 0.4596 0.9463
No log 14.0 378 0.3028 0.5467 0.3957 0.4591 0.9463
No log 15.0 405 0.3034 0.5444 0.3976 0.4596 0.9464
No log 16.0 432 0.3040 0.5431 0.3967 0.4585 0.9464
No log 17.0 459 0.3068 0.5484 0.3939 0.4585 0.9464
No log 18.0 486 0.3077 0.5501 0.3920 0.4578 0.9466
0.0203 19.0 513 0.3057 0.5434 0.3948 0.4573 0.9463
0.0203 20.0 540 0.3078 0.5494 0.3920 0.4575 0.9464
0.0203 21.0 567 0.3074 0.5517 0.3957 0.4609 0.9465
0.0203 22.0 594 0.3070 0.5499 0.3985 0.4621 0.9465
0.0203 23.0 621 0.3065 0.5497 0.3994 0.4627 0.9465
0.0203 24.0 648 0.3064 0.5450 0.3985 0.4604 0.9464
0.0203 25.0 675 0.3077 0.5467 0.3957 0.4591 0.9465
0.0203 26.0 702 0.3070 0.5458 0.3976 0.4601 0.9464
0.0203 27.0 729 0.3084 0.5494 0.3967 0.4607 0.9466
0.0203 28.0 756 0.3086 0.5487 0.3967 0.4605 0.9465
0.0203 29.0 783 0.3087 0.5486 0.3976 0.4610 0.9466
0.0203 30.0 810 0.3087 0.5444 0.3976 0.4596 0.9464
0.0203 31.0 837 0.3108 0.5510 0.3957 0.4606 0.9466
0.0203 32.0 864 0.3107 0.5494 0.3967 0.4607 0.9466
0.0203 33.0 891 0.3097 0.5429 0.3985 0.4596 0.9466
0.0203 34.0 918 0.3114 0.5493 0.3976 0.4613 0.9466
0.0203 35.0 945 0.3100 0.5430 0.3976 0.4591 0.9465
0.0203 36.0 972 0.3100 0.5442 0.3994 0.4607 0.9466
0.0203 37.0 999 0.3099 0.5428 0.3994 0.4602 0.9466
0.0177 38.0 1026 0.3109 0.5450 0.3985 0.4604 0.9465
0.0177 39.0 1053 0.3117 0.5488 0.3957 0.4599 0.9466
0.0177 40.0 1080 0.3119 0.5493 0.3976 0.4613 0.9466
0.0177 41.0 1107 0.3129 0.5528 0.3976 0.4625 0.9468
0.0177 42.0 1134 0.3124 0.5473 0.3967 0.4600 0.9467
0.0177 43.0 1161 0.3128 0.55 0.3976 0.4615 0.9468
0.0177 44.0 1188 0.3132 0.5514 0.3976 0.4620 0.9469
0.0177 45.0 1215 0.3119 0.5457 0.3985 0.4606 0.9467
0.0177 46.0 1242 0.3115 0.5436 0.3985 0.4599 0.9467
0.0177 47.0 1269 0.3127 0.5460 0.3957 0.4589 0.9466
0.0177 48.0 1296 0.3132 0.5474 0.3957 0.4594 0.9467
0.0177 49.0 1323 0.3137 0.5469 0.3948 0.4586 0.9467
0.0177 50.0 1350 0.3147 0.5510 0.3957 0.4606 0.9468
0.0177 51.0 1377 0.3133 0.5459 0.3967 0.4595 0.9468
0.0177 52.0 1404 0.3129 0.5436 0.3985 0.4599 0.9468
0.0177 53.0 1431 0.3138 0.5431 0.3967 0.4585 0.9467
0.0177 54.0 1458 0.3141 0.5437 0.3976 0.4593 0.9468
0.0177 55.0 1485 0.3141 0.5431 0.3967 0.4585 0.9467
0.0162 56.0 1512 0.3156 0.5473 0.3967 0.4600 0.9469
0.0162 57.0 1539 0.3147 0.5463 0.3994 0.4615 0.9469
0.0162 58.0 1566 0.3150 0.5450 0.3985 0.4604 0.9469
0.0162 59.0 1593 0.3154 0.5429 0.3985 0.4596 0.9468
0.0162 60.0 1620 0.3165 0.5486 0.3976 0.4610 0.9468
0.0162 61.0 1647 0.3150 0.5435 0.3994 0.4605 0.9468
0.0162 62.0 1674 0.3161 0.5450 0.3985 0.4604 0.9468
0.0162 63.0 1701 0.3159 0.5430 0.3976 0.4591 0.9467
0.0162 64.0 1728 0.3168 0.5458 0.3976 0.4601 0.9467
0.0162 65.0 1755 0.3168 0.5471 0.3985 0.4611 0.9468
0.0162 66.0 1782 0.3160 0.5429 0.3985 0.4596 0.9467
0.0162 67.0 1809 0.3166 0.5450 0.3985 0.4604 0.9467
0.0162 68.0 1836 0.3172 0.5457 0.3985 0.4606 0.9468
0.0162 69.0 1863 0.3168 0.5476 0.3994 0.4620 0.9468
0.0162 70.0 1890 0.3167 0.5470 0.3994 0.4617 0.9468
0.0162 71.0 1917 0.3167 0.5449 0.3994 0.4610 0.9468
0.0162 72.0 1944 0.3153 0.5439 0.4022 0.4624 0.9469
0.0162 73.0 1971 0.3155 0.5439 0.4022 0.4624 0.9469
0.0162 74.0 1998 0.3160 0.5428 0.3994 0.4602 0.9468
0.0153 75.0 2025 0.3167 0.5435 0.3994 0.4605 0.9469
0.0153 76.0 2052 0.3171 0.5449 0.3994 0.4610 0.9469
0.0153 77.0 2079 0.3176 0.5463 0.3994 0.4615 0.9469
0.0153 78.0 2106 0.3177 0.5463 0.3994 0.4615 0.9469
0.0153 79.0 2133 0.3172 0.5449 0.3994 0.4610 0.9469
0.0153 80.0 2160 0.3171 0.5443 0.3985 0.4601 0.9469
0.0153 81.0 2187 0.3171 0.5443 0.3985 0.4601 0.9469
0.0153 82.0 2214 0.3173 0.5457 0.3985 0.4606 0.9469
0.0153 83.0 2241 0.3174 0.5450 0.3985 0.4604 0.9468
0.0153 84.0 2268 0.3174 0.5436 0.3985 0.4599 0.9467
0.0153 85.0 2295 0.3170 0.5442 0.3994 0.4607 0.9467
0.0153 86.0 2322 0.3172 0.5449 0.3994 0.4610 0.9468
0.0153 87.0 2349 0.3181 0.5456 0.3994 0.4612 0.9468
0.0153 88.0 2376 0.3179 0.5463 0.3994 0.4615 0.9468
0.0153 89.0 2403 0.3181 0.5470 0.3994 0.4617 0.9469
0.0153 90.0 2430 0.3179 0.5470 0.3994 0.4617 0.9469
0.0153 91.0 2457 0.3181 0.5470 0.3994 0.4617 0.9469
0.0153 92.0 2484 0.3182 0.5463 0.3994 0.4615 0.9468
0.0145 93.0 2511 0.3182 0.5470 0.3994 0.4617 0.9469
0.0145 94.0 2538 0.3181 0.5470 0.3994 0.4617 0.9469
0.0145 95.0 2565 0.3182 0.5463 0.3994 0.4615 0.9468
0.0145 96.0 2592 0.3180 0.5470 0.3994 0.4617 0.9469
0.0145 97.0 2619 0.3180 0.5463 0.3994 0.4615 0.9469
0.0145 98.0 2646 0.3180 0.5463 0.3994 0.4615 0.9469
0.0145 99.0 2673 0.3181 0.5470 0.3994 0.4617 0.9469
0.0145 100.0 2700 0.3181 0.5470 0.3994 0.4617 0.9469
Framework versions
  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0

Runs of eren23 basic_wnut on huggingface.co

14
Total runs
0
24-hour runs
0
3-day runs
1
7-day runs
9
30-day runs

More Information About basic_wnut huggingface.co Model

More basic_wnut license Visit here:

https://choosealicense.com/licenses/apache-2.0

basic_wnut huggingface.co

basic_wnut huggingface.co is an AI model on huggingface.co that provides basic_wnut's model effect (), which can be used instantly with this eren23 basic_wnut model. huggingface.co supports a free trial of the basic_wnut model, and also provides paid use of the basic_wnut. Support call basic_wnut model through api, including Node.js, Python, http.

basic_wnut huggingface.co Url

https://huggingface.co/eren23/basic_wnut

eren23 basic_wnut online free

basic_wnut huggingface.co is an online trial and call api platform, which integrates basic_wnut's modeling effects, including api services, and provides a free online trial of basic_wnut, you can try basic_wnut online for free by clicking the link below.

eren23 basic_wnut online free url in huggingface.co:

https://huggingface.co/eren23/basic_wnut

basic_wnut install

basic_wnut is an open source model from GitHub that offers a free installation service, and any user can find basic_wnut on GitHub to install. At the same time, huggingface.co provides the effect of basic_wnut install, users can directly use basic_wnut installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

basic_wnut install url in huggingface.co:

https://huggingface.co/eren23/basic_wnut

Url of basic_wnut

basic_wnut huggingface.co Url

Provider of basic_wnut huggingface.co

eren23
ORGANIZATIONS
huggingface.co

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