File size: 2,370 Bytes
806cc83 |
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 |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: primo_test
results: []
---
<!-- 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. -->
# primo_test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0105
- Precision: 0.9744
- Recall: 0.9902
- F1: 0.9822
- Accuracy: 0.9979
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.03 | 100 | 0.4646 | 0.8275 | 0.8656 | 0.8461 | 0.9306 |
| No log | 0.06 | 200 | 0.0824 | 0.9614 | 0.9722 | 0.9667 | 0.9948 |
| No log | 0.08 | 300 | 0.0363 | 0.9622 | 0.9859 | 0.9739 | 0.9951 |
| No log | 0.11 | 400 | 0.0182 | 0.9756 | 0.9912 | 0.9833 | 0.9980 |
| 0.3067 | 0.14 | 500 | 0.0217 | 0.9578 | 0.9813 | 0.9694 | 0.9960 |
| 0.3067 | 0.17 | 600 | 0.0106 | 0.9913 | 0.9946 | 0.9929 | 0.9988 |
| 0.3067 | 0.19 | 700 | 0.0121 | 0.9733 | 0.9894 | 0.9812 | 0.9977 |
| 0.3067 | 0.22 | 800 | 0.0126 | 0.9699 | 0.9881 | 0.9789 | 0.9975 |
| 0.3067 | 0.25 | 900 | 0.0098 | 0.9778 | 0.9915 | 0.9846 | 0.9982 |
| 0.0105 | 0.28 | 1000 | 0.0105 | 0.9744 | 0.9902 | 0.9822 | 0.9979 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|