metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - named-entity-recognition
  - token-classification
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: fine_tune_bertweet-base-lp-ft
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wnut_17
          type: wnut_17
          args: semval
        metrics:
          - name: Precision
            type: precision
            value: 0.6154830454254638
          - name: Recall
            type: recall
            value: 0.49844559585492226
          - name: F1
            type: f1
            value: 0.5508159175493844
          - name: Accuracy
            type: accuracy
            value: 0.9499198834668608

Bertweet-base finetuned on wnut17_ner

This model is a fine-tuned version of vinai/bertweet-base on the 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