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layoutlmv3-large-model2ab-telephone-consultation-discharge
neuralit/layoutlmv3-large-model2ab-telephone-consultation-discharge
layoutlmv3-large-model2ab-telephone-consultation-discharge is a text classification model from neuralit. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-nc-sa-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- cc-by-nc-sa-4.0
- Model type
- layoutlmv3
- Access
- Public
- Created
- Mar 18, 2026
- Updated
- Mar 18, 2026
- SHA
- 12319307
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
layoutlmv3-large-model2ab-telephone-consultation-discharge
This model is a fine-tuned version of microsoft/layoutlmv3-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2897
- Accuracy: 0.9334
- Macro Precision: 0.9333
- Macro Recall: 0.9365
- Macro F1: 0.9344
- Weighted F1: 0.9332
- Precision Telephone Encounter: 0.9283
- Recall Telephone Encounter: 0.9510
- F1 Telephone Encounter: 0.9395
- Precision Consultation Note: 0.9439
- Recall Consultation Note: 0.8899
- F1 Consultation Note: 0.9161
- Precision Discharge Report: 0.9277
- Recall Discharge Report: 0.9686
- F1 Discharge Report: 0.9477
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1 | Weighted F1 | Precision Telephone Encounter | Recall Telephone Encounter | F1 Telephone Encounter | Precision Consultation Note | Recall Consultation Note | F1 Consultation Note | Precision Discharge Report | Recall Discharge Report | F1 Discharge Report |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1719 | 1.4216 | 500 | 0.2575 | 0.9303 | 0.9332 | 0.9330 | 0.9331 | 0.9303 | 0.9187 | 0.9224 | 0.9206 | 0.9248 | 0.9207 | 0.9227 | 0.9560 | 0.9560 | 0.9560 |
| 0.1268 | 2.8432 | 1000 | 0.2906 | 0.9334 | 0.9333 | 0.9365 | 0.9344 | 0.9332 | 0.9283 | 0.9510 | 0.9395 | 0.9439 | 0.8899 | 0.9161 | 0.9277 | 0.9686 | 0.9477 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.10.0+cu128
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- layoutlmv3
- License
- cc-by-nc-sa-4.0
- Created
- Mar 18, 2026
- Updated
- Mar 18, 2026