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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- named-entity-recognition |
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- token-classification |
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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: fine_tune_bertweet-base-lp-ft |
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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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args: semval |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6154830454254638 |
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- name: Recall |
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type: recall |
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value: 0.49844559585492226 |
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- name: F1 |
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type: f1 |
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value: 0.5508159175493844 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9499198834668608 |
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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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# Bertweet-base finetuned on wnut17_ner |
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This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the [wnut_17](https://huggingface.co/datasets/wnut_17) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3376 |
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- Overall Precision: 0.6803 |
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- Overall Recall: 0.6096 |
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- Overall F1: 0.6430 |
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- Overall Accuracy: 0.9509 |
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- Corporation F1: 0.2975 |
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- Creative-work F1: 0.4436 |
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- Group F1: 0.3624 |
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- Location F1: 0.6834 |
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- Person F1: 0.7902 |
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- Product F1: 0.3887 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Corporation F1 | Creative-work F1 | Group F1 | Location F1 | Person F1 | Product F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:--------:|:-----------:|:---------:|:----------:| |
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| 0.0215 | 1.0 | 213 | 0.2913 | 0.7026 | 0.5905 | 0.6417 | 0.9507 | 0.2832 | 0.4444 | 0.2975 | 0.6854 | 0.7788 | 0.4015 | |
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| 0.0213 | 2.0 | 426 | 0.3052 | 0.6774 | 0.5772 | 0.6233 | 0.9495 | 0.2830 | 0.3483 | 0.3231 | 0.6857 | 0.7728 | 0.3794 | |
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| 0.0288 | 3.0 | 639 | 0.3378 | 0.7061 | 0.5507 | 0.6188 | 0.9467 | 0.3077 | 0.4184 | 0.3529 | 0.6222 | 0.7532 | 0.3910 | |
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| 0.0124 | 4.0 | 852 | 0.2712 | 0.6574 | 0.6121 | 0.6340 | 0.9502 | 0.3077 | 0.4842 | 0.3167 | 0.6809 | 0.7735 | 0.3986 | |
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| 0.0208 | 5.0 | 1065 | 0.2905 | 0.7108 | 0.6063 | 0.6544 | 0.9518 | 0.3063 | 0.4286 | 0.3419 | 0.7052 | 0.7913 | 0.4223 | |
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| 0.0071 | 6.0 | 1278 | 0.3189 | 0.6756 | 0.5847 | 0.6269 | 0.9494 | 0.2759 | 0.4380 | 0.3256 | 0.6744 | 0.7781 | 0.3779 | |
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| 0.0073 | 7.0 | 1491 | 0.3593 | 0.7330 | 0.5540 | 0.6310 | 0.9476 | 0.3061 | 0.4388 | 0.3784 | 0.6946 | 0.7631 | 0.3374 | |
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| 0.0135 | 8.0 | 1704 | 0.3564 | 0.6875 | 0.5482 | 0.6100 | 0.9471 | 0.34 | 0.4179 | 0.3088 | 0.6632 | 0.7486 | 0.3695 | |
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| 0.0097 | 9.0 | 1917 | 0.3085 | 0.6598 | 0.6395 | 0.6495 | 0.9516 | 0.3111 | 0.4609 | 0.3836 | 0.7090 | 0.7906 | 0.4083 | |
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| 0.0108 | 10.0 | 2130 | 0.3045 | 0.6605 | 0.6478 | 0.6541 | 0.9509 | 0.3529 | 0.4580 | 0.3649 | 0.6897 | 0.7843 | 0.4387 | |
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| 0.013 | 11.0 | 2343 | 0.3383 | 0.6788 | 0.6179 | 0.6470 | 0.9507 | 0.2783 | 0.4248 | 0.3358 | 0.7368 | 0.7958 | 0.3655 | |
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| 0.0076 | 12.0 | 2556 | 0.3617 | 0.6920 | 0.5523 | 0.6143 | 0.9474 | 0.2708 | 0.3985 | 0.3333 | 0.6740 | 0.7566 | 0.3525 | |
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| 0.0042 | 13.0 | 2769 | 0.3747 | 0.6896 | 0.5664 | 0.6220 | 0.9473 | 0.2478 | 0.3915 | 0.3521 | 0.6561 | 0.7742 | 0.3539 | |
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| 0.0049 | 14.0 | 2982 | 0.3376 | 0.6803 | 0.6096 | 0.6430 | 0.9509 | 0.2975 | 0.4436 | 0.3624 | 0.6834 | 0.7902 | 0.3887 | |
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### Overall results |
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| metric_type | train | validation | test | |
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|:-------------------|-----------:|-----------:|-----------:| |
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| loss | 0.012030 | 0.271155 | 0.273943 | |
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| runtime | 16.292400 | 5.068800 | 8.596800 | |
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| samples_per_second | 208.318000 | 199.060000 | 149.707000 | |
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| steps_per_second | 13.074000 | 12.626000 | 9.422000 | |
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| corporation_f1 | 0.936877 | 0.307692 | 0.368627 | |
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| person_f1 | 0.984252 | 0.773455 | 0.689826 | |
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| product_f1 | 0.893246 | 0.398625 | 0.270423 | |
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| creative-work_f1 | 0.880562 | 0.484211 | 0.415274 | |
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| group_f1 | 0.975547 | 0.316667 | 0.411348 | |
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| location_f1 | 0.978887 | 0.680851 | 0.638695 | |
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| overall_accuracy | 0.997709 | 0.950244 | 0.949920 | |
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| overall_f1 | 0.961113 | 0.633978 | 0.550816 | |
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| overall_precision | 0.956337 | 0.657449 | 0.615483 | |
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| overall_recall | 0.965938 | 0.612126 | 0.498446 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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