README.md

metadata

language:
  - ko
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
widget:
  - text: 나스닥투자증권에서 시작된 발동성 가치 상태 효과는 투자자들에게 좋은 기회를 제공합니다.
    example_title: example01
  - text: TM머니가 베를린증권거래소에서 미국 보험 유가를 거래하고 있습니다.
    example_title: example02
base_model: klue/roberta-small
model-index:
  - name: ko_fin_ner_roberta_small_model
    results: []

ko_fin_ner_roberta_small_model

This model is a fine-tuned version of klue/roberta-small on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2873
• Precision: 0.7436
• Recall: 0.8774
• F1: 0.8050
• Accuracy: 0.9374

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	25	1.0272	0.1215	0.1662	0.1404	0.7237
No log	2.0	50	0.7136	0.2360	0.4033	0.2978	0.7695
No log	3.0	75	0.5289	0.3422	0.5586	0.4244	0.8285
No log	4.0	100	0.4404	0.4184	0.6076	0.4956	0.8730
No log	5.0	125	0.3768	0.4124	0.6540	0.5058	0.8866
No log	6.0	150	0.3484	0.4758	0.6975	0.5657	0.8953
No log	7.0	175	0.3236	0.5477	0.7357	0.6279	0.9039
No log	8.0	200	0.3097	0.5702	0.7520	0.6486	0.9015
No log	9.0	225	0.3168	0.6167	0.7629	0.6821	0.9096
No log	10.0	250	0.2950	0.6176	0.8011	0.6975	0.9145
No log	11.0	275	0.2806	0.6674	0.8147	0.7337	0.9195
No log	12.0	300	0.2749	0.6853	0.8365	0.7534	0.9266
No log	13.0	325	0.2743	0.7002	0.8338	0.7612	0.9292
No log	14.0	350	0.2862	0.6774	0.8011	0.7341	0.9238
No log	15.0	375	0.2703	0.6879	0.8529	0.7616	0.9276
No log	16.0	400	0.2752	0.7036	0.8474	0.7689	0.9293
No log	17.0	425	0.2721	0.6998	0.8447	0.7654	0.9305
No log	18.0	450	0.2831	0.6979	0.8311	0.7587	0.9299
No log	19.0	475	0.2857	0.7252	0.8556	0.7850	0.9319
0.2786	20.0	500	0.2792	0.7260	0.8665	0.7901	0.9319
0.2786	21.0	525	0.2604	0.7355	0.8638	0.7945	0.9349
0.2786	22.0	550	0.2603	0.7092	0.8638	0.7789	0.9359
0.2786	23.0	575	0.3026	0.7227	0.8665	0.7881	0.9342
0.2786	24.0	600	0.2800	0.7431	0.8747	0.8035	0.9375
0.2786	25.0	625	0.2838	0.7283	0.8692	0.7925	0.9361
0.2786	26.0	650	0.2813	0.7339	0.8719	0.7970	0.9371
0.2786	27.0	675	0.2881	0.7407	0.8719	0.8010	0.9358
0.2786	28.0	700	0.2894	0.7379	0.8747	0.8005	0.9362
0.2786	29.0	725	0.2889	0.7483	0.8747	0.8065	0.9368
0.2786	30.0	750	0.2873	0.7436	0.8774	0.8050	0.9374

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3