File size: 6,294 Bytes
2a6c12f e882096 2a6c12f e882096 2a6c12f eb5a86b 2a6c12f 5582a81 2a6c12f 5582a81 2a6c12f 5582a81 2a6c12f e882096 2a6c12f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- seqeval
model-index:
name: fine_tune_bert_output_LP_FP
results:
task:
type: token-classification # Required. Example: automatic-speech-recognition
name: named-entity-recognition # Optional. Example: Speech Recognition
dataset:
type: wnut_17 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: wnut_17 # Optional. Example: Common Voice zh-CN
metrics:
type: seqeval # Required. Example: wer
value: 0.5508159175493844 # Required. Example: 20.90
name: test_overall_f1 # Optional. Example: Test WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bertweet-base finetuned on wnut17_ner
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.
It achieves the following results on the evaluation set:
- Loss: 0.3376
- Overall Precision: 0.6803
- Overall Recall: 0.6096
- Overall F1: 0.6430
- Overall Accuracy: 0.9509
- Corporation F1: 0.2975
- Creative-work F1: 0.4436
- Group F1: 0.3624
- Location F1: 0.6834
- Person F1: 0.7902
- Product F1: 0.3887
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Corporation F1 | Creative-work F1 | Group F1 | Location F1 | Person F1 | Product F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:--------:|:-----------:|:---------:|:----------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Overall results
| metric_type | train | validation | test |
|:-------------------|-----------:|-----------:|-----------:|
| loss | 0.012030 | 0.271155 | 0.273943 |
| runtime | 16.292400 | 5.068800 | 8.596800 |
| samples_per_second | 208.318000 | 199.060000 | 149.707000 |
| steps_per_second | 13.074000 | 12.626000 | 9.422000 |
| corporation_f1 | 0.936877 | 0.307692 | 0.368627 |
| person_f1 | 0.984252 | 0.773455 | 0.689826 |
| product_f1 | 0.893246 | 0.398625 | 0.270423 |
| creative-work_f1 | 0.880562 | 0.484211 | 0.415274 |
| group_f1 | 0.975547 | 0.316667 | 0.411348 |
| location_f1 | 0.978887 | 0.680851 | 0.638695 |
| overall_accuracy | 0.997709 | 0.950244 | 0.949920 |
| overall_f1 | 0.961113 | 0.633978 | 0.550816 |
| overall_precision | 0.956337 | 0.657449 | 0.615483 |
| overall_recall | 0.965938 | 0.612126 | 0.498446 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|