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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- my_csv_dataset3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pharma_label_v3.1
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: my_csv_dataset3
type: my_csv_dataset3
config: discharge
split: test
args: discharge
metrics:
- name: Precision
type: precision
value: 0.9623287671232876
- name: Recall
type: recall
value: 0.9740034662045061
- name: F1
type: f1
value: 0.9681309216192937
- name: Accuracy
type: accuracy
value: 0.9890616004605642
---
<!-- 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. -->
# pharma_label_v3.1
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the my_csv_dataset3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0671
- Precision: 0.9623
- Recall: 0.9740
- F1: 0.9681
- Accuracy: 0.9891
## 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: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.2987 | 100 | 0.5492 | 0.7759 | 0.7140 | 0.7437 | 0.9102 |
| No log | 2.5974 | 200 | 0.1522 | 0.9281 | 0.9393 | 0.9337 | 0.9747 |
| No log | 3.8961 | 300 | 0.1063 | 0.9332 | 0.9445 | 0.9388 | 0.9793 |
| No log | 5.1948 | 400 | 0.0891 | 0.9448 | 0.9497 | 0.9473 | 0.9810 |
| 0.375 | 6.4935 | 500 | 0.0879 | 0.9435 | 0.9549 | 0.9492 | 0.9839 |
| 0.375 | 7.7922 | 600 | 0.0908 | 0.9485 | 0.9584 | 0.9534 | 0.9822 |
| 0.375 | 9.0909 | 700 | 0.0764 | 0.9636 | 0.9636 | 0.9636 | 0.9862 |
| 0.375 | 10.3896 | 800 | 0.0819 | 0.9671 | 0.9671 | 0.9671 | 0.9873 |
| 0.375 | 11.6883 | 900 | 0.0802 | 0.9686 | 0.9636 | 0.9661 | 0.9873 |
| 0.0225 | 12.9870 | 1000 | 0.0602 | 0.9722 | 0.9705 | 0.9714 | 0.9902 |
| 0.0225 | 14.2857 | 1100 | 0.0989 | 0.9438 | 0.9601 | 0.9519 | 0.9816 |
| 0.0225 | 15.5844 | 1200 | 0.0859 | 0.9538 | 0.9671 | 0.9604 | 0.9839 |
| 0.0225 | 16.8831 | 1300 | 0.0781 | 0.9554 | 0.9653 | 0.9603 | 0.9856 |
| 0.0225 | 18.1818 | 1400 | 0.0653 | 0.9605 | 0.9705 | 0.9655 | 0.9891 |
| 0.0105 | 19.4805 | 1500 | 0.0671 | 0.9623 | 0.9740 | 0.9681 | 0.9891 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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