README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
inference: false
base_model: nielsr/lilt-roberta-en-base
model-index:
  - name: lilt-roberta-en-base-finetuned-funsd
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: funsd-layoutlmv3
          type: funsd-layoutlmv3
          config: funsd
          split: train
          args: funsd
        metrics:
          - type: precision
            value: 0.8761670761670761
            name: Precision
          - type: recall
            value: 0.8857426726279185
            name: Recall
          - type: f1
            value: 0.8809288537549407
            name: F1
          - type: accuracy
            value: 0.8068465470105789
            name: Accuracy

lilt-roberta-en-base-finetuned-funsd

This model is a fine-tuned version of nielsr/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

• Loss: 1.6552
• Precision: 0.8762
• Recall: 0.8857
• F1: 0.8809
• Accuracy: 0.8068

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.1
• training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	5.26	100	1.1789	0.8506	0.8485	0.8495	0.7869
No log	10.53	200	1.2382	0.8360	0.8788	0.8569	0.7970
No log	15.79	300	1.3766	0.8557	0.8897	0.8724	0.7909
No log	21.05	400	1.5590	0.8368	0.8763	0.8561	0.7792
0.04	26.32	500	1.4379	0.8562	0.8813	0.8685	0.7992
0.04	31.58	600	1.5397	0.8593	0.8947	0.8766	0.8054
0.04	36.84	700	1.6132	0.8621	0.8723	0.8672	0.7933
0.04	42.11	800	1.6483	0.8566	0.8872	0.8716	0.7777
0.04	47.37	900	1.6593	0.8641	0.8813	0.8726	0.7895
0.0044	52.63	1000	1.6704	0.8595	0.8718	0.8656	0.7925
0.0044	57.89	1100	1.6795	0.8495	0.8803	0.8646	0.7748
0.0044	63.16	1200	1.5515	0.8604	0.8912	0.8755	0.7991
0.0044	68.42	1300	1.6665	0.8573	0.8867	0.8718	0.7821
0.0044	73.68	1400	1.5893	0.8604	0.8877	0.8738	0.7895
0.0008	78.95	1500	1.5613	0.8603	0.8872	0.8736	0.8123
0.0008	84.21	1600	1.5853	0.8521	0.8872	0.8693	0.8040
0.0008	89.47	1700	1.6539	0.8707	0.8833	0.8769	0.8077
0.0008	94.74	1800	1.6634	0.8787	0.8813	0.8800	0.8079
0.0008	100.0	1900	1.6534	0.8810	0.8862	0.8836	0.8073
0.0004	105.26	2000	1.6552	0.8762	0.8857	0.8809	0.8068

Framework versions

• Transformers 4.23.0.dev0
• Pytorch 1.12.1+cu113
• Datasets 2.5.1
• Tokenizers 0.13.0