File size: 2,481 Bytes
8f411cd 12be85e 8f411cd 12be85e 8f411cd 12be85e 8f411cd 12be85e 8f411cd 12be85e 8f411cd 12be85e 8f411cd 12be85e 8f411cd 10902c9 8f411cd 12be85e 8f411cd |
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 77 78 79 80 81 82 83 84 85 86 87 |
---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-srb-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: sr
metric:
name: Accuracy
type: accuracy
value: 0.9542715764169646
---
<!-- 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. -->
# bert-srb-ner
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3045
- Precision: 0.8922
- Recall: 0.9050
- F1: 0.8986
- Accuracy: 0.9543
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.276 | 1.0 | 1250 | 0.2359 | 0.8355 | 0.8334 | 0.8344 | 0.9276 |
| 0.1722 | 2.0 | 2500 | 0.2016 | 0.8731 | 0.8685 | 0.8708 | 0.9426 |
| 0.1155 | 3.0 | 3750 | 0.1897 | 0.8707 | 0.8860 | 0.8783 | 0.9463 |
| 0.0849 | 4.0 | 5000 | 0.2151 | 0.8755 | 0.8980 | 0.8866 | 0.9494 |
| 0.0554 | 5.0 | 6250 | 0.2373 | 0.8820 | 0.8923 | 0.8871 | 0.9495 |
| 0.039 | 6.0 | 7500 | 0.2644 | 0.8808 | 0.8953 | 0.8880 | 0.9505 |
| 0.0286 | 7.0 | 8750 | 0.2737 | 0.8915 | 0.8961 | 0.8938 | 0.9520 |
| 0.018 | 8.0 | 10000 | 0.2879 | 0.8860 | 0.9039 | 0.8948 | 0.9526 |
| 0.0116 | 9.0 | 11250 | 0.2973 | 0.8930 | 0.9032 | 0.8981 | 0.9542 |
| 0.0079 | 10.0 | 12500 | 0.3045 | 0.8922 | 0.9050 | 0.8986 | 0.9543 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
|