README.md

metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - wild
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: P.E.R.S_WILD
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wild
          type: wild
          config: WildReceipt
          split: test
          args: WildReceipt
        metrics:
          - name: Precision
            type: precision
            value: 0.8621359223300971
          - name: Recall
            type: recall
            value: 0.8556090846524432
          - name: F1
            type: f1
            value: 0.8588601036269431
          - name: Accuracy
            type: accuracy
            value: 0.9165934548649243

P.E.R.S_WILD

This model is a fine-tuned version of microsoft/layoutlmv3-base on the wild dataset. It achieves the following results on the evaluation set:

• Loss: 0.3319
• Precision: 0.8621
• Recall: 0.8556
• F1: 0.8589
• Accuracy: 0.9166

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: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.0789	100	1.7570	0.4014	0.0807	0.1343	0.5202
No log	0.1579	200	1.2158	0.5444	0.3515	0.4272	0.6895
No log	0.2368	300	0.9862	0.6676	0.4763	0.5559	0.7534
No log	0.3157	400	0.8539	0.6740	0.5898	0.6291	0.7883
1.3192	0.3946	500	0.7150	0.7489	0.6325	0.6858	0.8208
1.3192	0.4736	600	0.6731	0.7487	0.6716	0.7080	0.8288
1.3192	0.5525	700	0.6345	0.7588	0.6848	0.7199	0.8357
1.3192	0.6314	800	0.5903	0.7671	0.7181	0.7418	0.8472
1.3192	0.7103	900	0.5273	0.7743	0.7718	0.7731	0.8690
0.7013	0.7893	1000	0.4923	0.7939	0.7555	0.7742	0.8689
0.7013	0.8682	1100	0.4811	0.8147	0.7619	0.7874	0.8742
0.7013	0.9471	1200	0.4694	0.8006	0.7985	0.7995	0.8812
0.7013	1.0260	1300	0.4429	0.8246	0.8058	0.8151	0.8866
0.7013	1.1050	1400	0.4302	0.8135	0.8051	0.8093	0.8863
0.4844	1.1839	1500	0.4364	0.7964	0.8245	0.8102	0.8875
0.4844	1.2628	1600	0.4445	0.8012	0.8299	0.8153	0.8857
0.4844	1.3418	1700	0.4021	0.8175	0.8244	0.8209	0.8918
0.4844	1.4207	1800	0.3886	0.8290	0.8193	0.8241	0.8958
0.4844	1.4996	1900	0.3708	0.8271	0.8372	0.8321	0.9000
0.411	1.5785	2000	0.3910	0.8356	0.8310	0.8333	0.8996
0.411	1.6575	2100	0.3550	0.8419	0.8399	0.8409	0.9069
0.411	1.7364	2200	0.3499	0.8374	0.8451	0.8413	0.9066
0.411	1.8153	2300	0.3532	0.8301	0.8512	0.8405	0.9050
0.411	1.8942	2400	0.3763	0.8285	0.8471	0.8377	0.9018
0.3641	1.9732	2500	0.3508	0.8529	0.8410	0.8469	0.9067
0.3641	2.0521	2600	0.3616	0.8507	0.8384	0.8445	0.9083
0.3641	2.1310	2700	0.3705	0.8485	0.8511	0.8498	0.9086
0.3641	2.2099	2800	0.3527	0.8436	0.8562	0.8498	0.9118
0.3641	2.2889	2900	0.3383	0.8658	0.8475	0.8566	0.9135
0.2824	2.3678	3000	0.3395	0.8527	0.8523	0.8525	0.9124
0.2824	2.4467	3100	0.3364	0.8622	0.8478	0.8549	0.9140
0.2824	2.5257	3200	0.3383	0.8431	0.8619	0.8524	0.9125
0.2824	2.6046	3300	0.3377	0.8530	0.8586	0.8558	0.9132
0.2824	2.6835	3400	0.3389	0.8481	0.8629	0.8554	0.9135
0.2928	2.7624	3500	0.3319	0.8621	0.8556	0.8589	0.9166
0.2928	2.8414	3600	0.3341	0.8555	0.8575	0.8565	0.9153
0.2928	2.9203	3700	0.3341	0.8536	0.8603	0.8569	0.9153
0.2928	2.9992	3800	0.3305	0.8556	0.8636	0.8596	0.9167
0.2928	3.0781	3900	0.3313	0.8579	0.8613	0.8596	0.9166
0.2487	3.1571	4000	0.3326	0.8550	0.8604	0.8577	0.9160

Framework versions

• Transformers 4.42.0.dev0
• Pytorch 2.3.1+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1