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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12
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. -->
# distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0245
- Precision: 0.8602
- Recall: 0.9015
- F1: 0.8804
- Accuracy: 0.9921
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0157 | 0.06 | 500 | 0.0286 | 0.8569 | 0.8605 | 0.8587 | 0.9908 |
| 0.0156 | 0.11 | 1000 | 0.0287 | 0.8340 | 0.8940 | 0.8630 | 0.9910 |
| 0.0141 | 0.17 | 1500 | 0.0296 | 0.8368 | 0.8853 | 0.8604 | 0.9908 |
| 0.014 | 0.23 | 2000 | 0.0296 | 0.8356 | 0.8915 | 0.8627 | 0.9910 |
| 0.0144 | 0.28 | 2500 | 0.0306 | 0.8310 | 0.8896 | 0.8593 | 0.9906 |
| 0.0136 | 0.34 | 3000 | 0.0290 | 0.8384 | 0.8842 | 0.8607 | 0.9910 |
| 0.0134 | 0.4 | 3500 | 0.0306 | 0.8514 | 0.8779 | 0.8645 | 0.9912 |
| 0.0138 | 0.46 | 4000 | 0.0307 | 0.8475 | 0.8790 | 0.8630 | 0.9910 |
| 0.0139 | 0.51 | 4500 | 0.0301 | 0.8208 | 0.9002 | 0.8587 | 0.9908 |
| 0.014 | 0.57 | 5000 | 0.0320 | 0.8307 | 0.8981 | 0.8631 | 0.9909 |
| 0.0155 | 0.63 | 5500 | 0.0307 | 0.8329 | 0.8992 | 0.8648 | 0.9909 |
| 0.0154 | 0.68 | 6000 | 0.0268 | 0.8403 | 0.8971 | 0.8677 | 0.9913 |
| 0.0178 | 0.74 | 6500 | 0.0269 | 0.8548 | 0.8869 | 0.8705 | 0.9916 |
| 0.0177 | 0.8 | 7000 | 0.0268 | 0.8552 | 0.8904 | 0.8725 | 0.9917 |
| 0.0178 | 0.85 | 7500 | 0.0267 | 0.8498 | 0.8972 | 0.8729 | 0.9917 |
| 0.017 | 0.91 | 8000 | 0.0259 | 0.8517 | 0.8969 | 0.8737 | 0.9917 |
| 0.0176 | 0.97 | 8500 | 0.0249 | 0.8523 | 0.8921 | 0.8717 | 0.9916 |
| 0.0157 | 1.02 | 9000 | 0.0274 | 0.8535 | 0.8990 | 0.8757 | 0.9918 |
| 0.0134 | 1.08 | 9500 | 0.0293 | 0.8375 | 0.9060 | 0.8704 | 0.9913 |
| 0.0132 | 1.14 | 10000 | 0.0278 | 0.8648 | 0.8864 | 0.8755 | 0.9919 |
| 0.0135 | 1.19 | 10500 | 0.0273 | 0.8540 | 0.8958 | 0.8744 | 0.9917 |
| 0.0133 | 1.25 | 11000 | 0.0277 | 0.8442 | 0.9034 | 0.8728 | 0.9917 |
| 0.0138 | 1.31 | 11500 | 0.0276 | 0.8484 | 0.9035 | 0.8751 | 0.9917 |
| 0.0141 | 1.37 | 12000 | 0.0274 | 0.8501 | 0.9016 | 0.8751 | 0.9918 |
| 0.0137 | 1.42 | 12500 | 0.0274 | 0.8529 | 0.9010 | 0.8763 | 0.9918 |
| 0.014 | 1.48 | 13000 | 0.0269 | 0.8509 | 0.9022 | 0.8758 | 0.9919 |
| 0.0141 | 1.54 | 13500 | 0.0260 | 0.8653 | 0.8926 | 0.8787 | 0.9920 |
| 0.0149 | 1.59 | 14000 | 0.0258 | 0.8521 | 0.9048 | 0.8777 | 0.9919 |
| 0.0149 | 1.65 | 14500 | 0.0257 | 0.8607 | 0.8980 | 0.8790 | 0.9921 |
| 0.0152 | 1.71 | 15000 | 0.0257 | 0.8596 | 0.9001 | 0.8794 | 0.9920 |
| 0.015 | 1.76 | 15500 | 0.0257 | 0.8556 | 0.9032 | 0.8788 | 0.9920 |
| 0.0157 | 1.82 | 16000 | 0.0248 | 0.8620 | 0.8993 | 0.8802 | 0.9922 |
| 0.0158 | 1.88 | 16500 | 0.0251 | 0.8573 | 0.9036 | 0.8798 | 0.9921 |
| 0.0163 | 1.93 | 17000 | 0.0248 | 0.8579 | 0.9034 | 0.8800 | 0.9921 |
| 0.017 | 1.99 | 17500 | 0.0245 | 0.8602 | 0.9015 | 0.8804 | 0.9921 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0
- Datasets 2.3.2
- Tokenizers 0.10.3
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