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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-er-ner
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. -->
# layoutlmv3-er-ner
This model is a fine-tuned version of [renjithks/layoutlmv3-cord-ner](https://huggingface.co/renjithks/layoutlmv3-cord-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2025
- Precision: 0.6442
- Recall: 0.6761
- F1: 0.6598
- Accuracy: 0.9507
## 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
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 22 | 0.2940 | 0.4214 | 0.2956 | 0.3475 | 0.9147 |
| No log | 2.0 | 44 | 0.2487 | 0.4134 | 0.4526 | 0.4321 | 0.9175 |
| No log | 3.0 | 66 | 0.1922 | 0.5399 | 0.5460 | 0.5429 | 0.9392 |
| No log | 4.0 | 88 | 0.1977 | 0.5653 | 0.5813 | 0.5732 | 0.9434 |
| No log | 5.0 | 110 | 0.2018 | 0.6173 | 0.6252 | 0.6212 | 0.9477 |
| No log | 6.0 | 132 | 0.1823 | 0.6232 | 0.6153 | 0.6192 | 0.9485 |
| No log | 7.0 | 154 | 0.1972 | 0.6203 | 0.6238 | 0.6220 | 0.9477 |
| No log | 8.0 | 176 | 0.1952 | 0.6292 | 0.6407 | 0.6349 | 0.9511 |
| No log | 9.0 | 198 | 0.2070 | 0.6331 | 0.6492 | 0.6411 | 0.9489 |
| No log | 10.0 | 220 | 0.2025 | 0.6442 | 0.6761 | 0.6598 | 0.9507 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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