File size: 2,817 Bytes
df60745 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- drug_bill_layoutv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-vinai
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: drug_bill_layoutv3
type: drug_bill_layoutv3
config: Vin_Drug_Bill
split: train
args: Vin_Drug_Bill
metrics:
- name: Precision
type: precision
value: 1.0
- name: Recall
type: recall
value: 1.0
- name: F1
type: f1
value: 1.0
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- 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-finetuned-vinai
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the drug_bill_layoutv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.33 | 250 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.014 | 2.66 | 500 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.014 | 3.99 | 750 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0099 | 5.32 | 1000 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0099 | 6.65 | 1250 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0035 | 7.98 | 1500 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0035 | 9.31 | 1750 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 10.64 | 2000 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 11.97 | 2250 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 13.3 | 2500 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|