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basic_wnut

Token Classification
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distilbert
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  2. model.safetensors +1 -1
  3. runs/Nov18_18-01-21_3c6ca80a3aa8/events.out.tfevents.1700330481.3c6ca80a3aa8.4054.2 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - wnut_17
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: basic_wnut
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: wnut_17
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+ type: wnut_17
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+ config: wnut_17
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+ split: test
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+ args: wnut_17
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.5469543147208121
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+ - name: Recall
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+ type: recall
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+ value: 0.3994439295644115
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+ - name: F1
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+ type: f1
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+ value: 0.46170326727370103
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9469026548672567
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # basic_wnut
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3181
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+ - Precision: 0.5470
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+ - Recall: 0.3994
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+ - F1: 0.4617
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+ - Accuracy: 0.9469
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-07
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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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: 100
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+
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+ ### Training results
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+
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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 | 27 | 0.2984 | 0.5557 | 0.3930 | 0.4604 | 0.9463 |
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+ | No log | 2.0 | 54 | 0.2991 | 0.5547 | 0.3902 | 0.4581 | 0.9462 |
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+ | No log | 3.0 | 81 | 0.2993 | 0.5557 | 0.3930 | 0.4604 | 0.9463 |
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+ | No log | 4.0 | 108 | 0.3011 | 0.5550 | 0.3883 | 0.4569 | 0.9461 |
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+ | No log | 5.0 | 135 | 0.3015 | 0.5532 | 0.3902 | 0.4576 | 0.9462 |
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+ | No log | 6.0 | 162 | 0.2997 | 0.5467 | 0.3957 | 0.4591 | 0.9463 |
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+ | No log | 7.0 | 189 | 0.2997 | 0.5487 | 0.3967 | 0.4605 | 0.9462 |
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+ | No log | 8.0 | 216 | 0.2998 | 0.5439 | 0.3957 | 0.4582 | 0.9463 |
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+ | No log | 9.0 | 243 | 0.3024 | 0.5501 | 0.3920 | 0.4578 | 0.9462 |
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+ | No log | 10.0 | 270 | 0.3021 | 0.5470 | 0.3939 | 0.4580 | 0.9462 |
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+ | No log | 11.0 | 297 | 0.3027 | 0.5471 | 0.3930 | 0.4574 | 0.9463 |
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+ | No log | 12.0 | 324 | 0.3023 | 0.5453 | 0.3957 | 0.4586 | 0.9463 |
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+ | No log | 13.0 | 351 | 0.3028 | 0.5481 | 0.3957 | 0.4596 | 0.9463 |
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+ | No log | 14.0 | 378 | 0.3028 | 0.5467 | 0.3957 | 0.4591 | 0.9463 |
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+ | No log | 15.0 | 405 | 0.3034 | 0.5444 | 0.3976 | 0.4596 | 0.9464 |
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+ | No log | 16.0 | 432 | 0.3040 | 0.5431 | 0.3967 | 0.4585 | 0.9464 |
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+ | No log | 17.0 | 459 | 0.3068 | 0.5484 | 0.3939 | 0.4585 | 0.9464 |
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+ | No log | 18.0 | 486 | 0.3077 | 0.5501 | 0.3920 | 0.4578 | 0.9466 |
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+ | 0.0203 | 19.0 | 513 | 0.3057 | 0.5434 | 0.3948 | 0.4573 | 0.9463 |
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+ | 0.0203 | 20.0 | 540 | 0.3078 | 0.5494 | 0.3920 | 0.4575 | 0.9464 |
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+ | 0.0203 | 21.0 | 567 | 0.3074 | 0.5517 | 0.3957 | 0.4609 | 0.9465 |
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+ | 0.0203 | 22.0 | 594 | 0.3070 | 0.5499 | 0.3985 | 0.4621 | 0.9465 |
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+ | 0.0203 | 23.0 | 621 | 0.3065 | 0.5497 | 0.3994 | 0.4627 | 0.9465 |
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+ | 0.0203 | 24.0 | 648 | 0.3064 | 0.5450 | 0.3985 | 0.4604 | 0.9464 |
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+ | 0.0203 | 25.0 | 675 | 0.3077 | 0.5467 | 0.3957 | 0.4591 | 0.9465 |
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+ | 0.0203 | 26.0 | 702 | 0.3070 | 0.5458 | 0.3976 | 0.4601 | 0.9464 |
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+ | 0.0203 | 27.0 | 729 | 0.3084 | 0.5494 | 0.3967 | 0.4607 | 0.9466 |
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+ | 0.0203 | 28.0 | 756 | 0.3086 | 0.5487 | 0.3967 | 0.4605 | 0.9465 |
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+ | 0.0203 | 29.0 | 783 | 0.3087 | 0.5486 | 0.3976 | 0.4610 | 0.9466 |
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+ | 0.0203 | 30.0 | 810 | 0.3087 | 0.5444 | 0.3976 | 0.4596 | 0.9464 |
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+ | 0.0203 | 31.0 | 837 | 0.3108 | 0.5510 | 0.3957 | 0.4606 | 0.9466 |
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+ | 0.0203 | 32.0 | 864 | 0.3107 | 0.5494 | 0.3967 | 0.4607 | 0.9466 |
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+ | 0.0203 | 33.0 | 891 | 0.3097 | 0.5429 | 0.3985 | 0.4596 | 0.9466 |
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+ | 0.0203 | 34.0 | 918 | 0.3114 | 0.5493 | 0.3976 | 0.4613 | 0.9466 |
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+ | 0.0203 | 35.0 | 945 | 0.3100 | 0.5430 | 0.3976 | 0.4591 | 0.9465 |
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+ | 0.0203 | 36.0 | 972 | 0.3100 | 0.5442 | 0.3994 | 0.4607 | 0.9466 |
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+ | 0.0203 | 37.0 | 999 | 0.3099 | 0.5428 | 0.3994 | 0.4602 | 0.9466 |
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+ | 0.0177 | 38.0 | 1026 | 0.3109 | 0.5450 | 0.3985 | 0.4604 | 0.9465 |
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+ | 0.0177 | 39.0 | 1053 | 0.3117 | 0.5488 | 0.3957 | 0.4599 | 0.9466 |
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+ | 0.0177 | 40.0 | 1080 | 0.3119 | 0.5493 | 0.3976 | 0.4613 | 0.9466 |
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+ | 0.0177 | 41.0 | 1107 | 0.3129 | 0.5528 | 0.3976 | 0.4625 | 0.9468 |
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+ | 0.0177 | 42.0 | 1134 | 0.3124 | 0.5473 | 0.3967 | 0.4600 | 0.9467 |
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+ | 0.0177 | 43.0 | 1161 | 0.3128 | 0.55 | 0.3976 | 0.4615 | 0.9468 |
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+ | 0.0177 | 44.0 | 1188 | 0.3132 | 0.5514 | 0.3976 | 0.4620 | 0.9469 |
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+ | 0.0177 | 45.0 | 1215 | 0.3119 | 0.5457 | 0.3985 | 0.4606 | 0.9467 |
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+ | 0.0177 | 46.0 | 1242 | 0.3115 | 0.5436 | 0.3985 | 0.4599 | 0.9467 |
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+ | 0.0177 | 47.0 | 1269 | 0.3127 | 0.5460 | 0.3957 | 0.4589 | 0.9466 |
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+ | 0.0177 | 48.0 | 1296 | 0.3132 | 0.5474 | 0.3957 | 0.4594 | 0.9467 |
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+ | 0.0177 | 49.0 | 1323 | 0.3137 | 0.5469 | 0.3948 | 0.4586 | 0.9467 |
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+ | 0.0177 | 50.0 | 1350 | 0.3147 | 0.5510 | 0.3957 | 0.4606 | 0.9468 |
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+ | 0.0177 | 51.0 | 1377 | 0.3133 | 0.5459 | 0.3967 | 0.4595 | 0.9468 |
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+ | 0.0177 | 52.0 | 1404 | 0.3129 | 0.5436 | 0.3985 | 0.4599 | 0.9468 |
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+ | 0.0177 | 53.0 | 1431 | 0.3138 | 0.5431 | 0.3967 | 0.4585 | 0.9467 |
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+ | 0.0177 | 54.0 | 1458 | 0.3141 | 0.5437 | 0.3976 | 0.4593 | 0.9468 |
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+ | 0.0177 | 55.0 | 1485 | 0.3141 | 0.5431 | 0.3967 | 0.4585 | 0.9467 |
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+ | 0.0162 | 56.0 | 1512 | 0.3156 | 0.5473 | 0.3967 | 0.4600 | 0.9469 |
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+ | 0.0162 | 57.0 | 1539 | 0.3147 | 0.5463 | 0.3994 | 0.4615 | 0.9469 |
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+ | 0.0162 | 58.0 | 1566 | 0.3150 | 0.5450 | 0.3985 | 0.4604 | 0.9469 |
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+ | 0.0162 | 59.0 | 1593 | 0.3154 | 0.5429 | 0.3985 | 0.4596 | 0.9468 |
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+ | 0.0162 | 60.0 | 1620 | 0.3165 | 0.5486 | 0.3976 | 0.4610 | 0.9468 |
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+ | 0.0162 | 61.0 | 1647 | 0.3150 | 0.5435 | 0.3994 | 0.4605 | 0.9468 |
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+ | 0.0162 | 62.0 | 1674 | 0.3161 | 0.5450 | 0.3985 | 0.4604 | 0.9468 |
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+ | 0.0162 | 63.0 | 1701 | 0.3159 | 0.5430 | 0.3976 | 0.4591 | 0.9467 |
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+ | 0.0162 | 64.0 | 1728 | 0.3168 | 0.5458 | 0.3976 | 0.4601 | 0.9467 |
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+ | 0.0162 | 65.0 | 1755 | 0.3168 | 0.5471 | 0.3985 | 0.4611 | 0.9468 |
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+ | 0.0162 | 66.0 | 1782 | 0.3160 | 0.5429 | 0.3985 | 0.4596 | 0.9467 |
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+ | 0.0162 | 67.0 | 1809 | 0.3166 | 0.5450 | 0.3985 | 0.4604 | 0.9467 |
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+ | 0.0162 | 68.0 | 1836 | 0.3172 | 0.5457 | 0.3985 | 0.4606 | 0.9468 |
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+ | 0.0162 | 69.0 | 1863 | 0.3168 | 0.5476 | 0.3994 | 0.4620 | 0.9468 |
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+ | 0.0162 | 70.0 | 1890 | 0.3167 | 0.5470 | 0.3994 | 0.4617 | 0.9468 |
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+ | 0.0162 | 71.0 | 1917 | 0.3167 | 0.5449 | 0.3994 | 0.4610 | 0.9468 |
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+ | 0.0162 | 72.0 | 1944 | 0.3153 | 0.5439 | 0.4022 | 0.4624 | 0.9469 |
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+ | 0.0162 | 73.0 | 1971 | 0.3155 | 0.5439 | 0.4022 | 0.4624 | 0.9469 |
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+ | 0.0162 | 74.0 | 1998 | 0.3160 | 0.5428 | 0.3994 | 0.4602 | 0.9468 |
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+ | 0.0153 | 75.0 | 2025 | 0.3167 | 0.5435 | 0.3994 | 0.4605 | 0.9469 |
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+ | 0.0153 | 76.0 | 2052 | 0.3171 | 0.5449 | 0.3994 | 0.4610 | 0.9469 |
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+ | 0.0153 | 77.0 | 2079 | 0.3176 | 0.5463 | 0.3994 | 0.4615 | 0.9469 |
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+ | 0.0153 | 78.0 | 2106 | 0.3177 | 0.5463 | 0.3994 | 0.4615 | 0.9469 |
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+ | 0.0153 | 79.0 | 2133 | 0.3172 | 0.5449 | 0.3994 | 0.4610 | 0.9469 |
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+ | 0.0153 | 80.0 | 2160 | 0.3171 | 0.5443 | 0.3985 | 0.4601 | 0.9469 |
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+ | 0.0153 | 81.0 | 2187 | 0.3171 | 0.5443 | 0.3985 | 0.4601 | 0.9469 |
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+ | 0.0153 | 82.0 | 2214 | 0.3173 | 0.5457 | 0.3985 | 0.4606 | 0.9469 |
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+ | 0.0153 | 83.0 | 2241 | 0.3174 | 0.5450 | 0.3985 | 0.4604 | 0.9468 |
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+ | 0.0153 | 84.0 | 2268 | 0.3174 | 0.5436 | 0.3985 | 0.4599 | 0.9467 |
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+ | 0.0153 | 85.0 | 2295 | 0.3170 | 0.5442 | 0.3994 | 0.4607 | 0.9467 |
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+ | 0.0153 | 86.0 | 2322 | 0.3172 | 0.5449 | 0.3994 | 0.4610 | 0.9468 |
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+ | 0.0153 | 87.0 | 2349 | 0.3181 | 0.5456 | 0.3994 | 0.4612 | 0.9468 |
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+ | 0.0153 | 88.0 | 2376 | 0.3179 | 0.5463 | 0.3994 | 0.4615 | 0.9468 |
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+ | 0.0153 | 89.0 | 2403 | 0.3181 | 0.5470 | 0.3994 | 0.4617 | 0.9469 |
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+ | 0.0153 | 90.0 | 2430 | 0.3179 | 0.5470 | 0.3994 | 0.4617 | 0.9469 |
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+ | 0.0153 | 91.0 | 2457 | 0.3181 | 0.5470 | 0.3994 | 0.4617 | 0.9469 |
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+ | 0.0153 | 92.0 | 2484 | 0.3182 | 0.5463 | 0.3994 | 0.4615 | 0.9468 |
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+ | 0.0145 | 93.0 | 2511 | 0.3182 | 0.5470 | 0.3994 | 0.4617 | 0.9469 |
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+ | 0.0145 | 94.0 | 2538 | 0.3181 | 0.5470 | 0.3994 | 0.4617 | 0.9469 |
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+ | 0.0145 | 95.0 | 2565 | 0.3182 | 0.5463 | 0.3994 | 0.4615 | 0.9468 |
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+ | 0.0145 | 96.0 | 2592 | 0.3180 | 0.5470 | 0.3994 | 0.4617 | 0.9469 |
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+ | 0.0145 | 97.0 | 2619 | 0.3180 | 0.5463 | 0.3994 | 0.4615 | 0.9469 |
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+ | 0.0145 | 98.0 | 2646 | 0.3180 | 0.5463 | 0.3994 | 0.4615 | 0.9469 |
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+ | 0.0145 | 99.0 | 2673 | 0.3181 | 0.5470 | 0.3994 | 0.4617 | 0.9469 |
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+ | 0.0145 | 100.0 | 2700 | 0.3181 | 0.5470 | 0.3994 | 0.4617 | 0.9469 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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