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---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-srb-ner-setimes
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9660941783583293
---
<!-- 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-srb-ner-setimes
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1522
- Precision: 0.8280
- Recall: 0.8607
- F1: 0.8440
- Accuracy: 0.9661
## 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: 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 | 207 | 0.2240 | 0.6996 | 0.7200 | 0.7097 | 0.9375 |
| No log | 2.0 | 414 | 0.1538 | 0.7501 | 0.7930 | 0.7710 | 0.9546 |
| 0.2348 | 3.0 | 621 | 0.1459 | 0.7756 | 0.8115 | 0.7931 | 0.9576 |
| 0.2348 | 4.0 | 828 | 0.1465 | 0.7918 | 0.8456 | 0.8178 | 0.9611 |
| 0.0782 | 5.0 | 1035 | 0.1310 | 0.7981 | 0.8352 | 0.8162 | 0.9636 |
| 0.0782 | 6.0 | 1242 | 0.1466 | 0.8103 | 0.8510 | 0.8301 | 0.9646 |
| 0.0782 | 7.0 | 1449 | 0.1441 | 0.8222 | 0.8503 | 0.8360 | 0.9655 |
| 0.0343 | 8.0 | 1656 | 0.1493 | 0.8265 | 0.8600 | 0.8429 | 0.9666 |
| 0.0343 | 9.0 | 1863 | 0.1524 | 0.8236 | 0.8570 | 0.8400 | 0.9656 |
| 0.0169 | 10.0 | 2070 | 0.1522 | 0.8280 | 0.8607 | 0.8440 | 0.9661 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
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