Named Entity Recognition model for swedish
This model is a fine-tuned version of KBLab/bert-base-swedish-cased-nerfor only Swedish. It has been fine-tuned on the concatenation of a smaller version of SUC 3.0 and some medical text from the Swedish website 1177.
The model will predict the following entities:
| Tag | Name | Exampel |
|---|---|---|
| PER | Person | (e.g., Johan and Sofia) |
| LOC | Location | (e.g., Göteborg and Spanien) |
| ORG | Organisation | (e.g., Volvo and Skatteverket) \ |
| PHARMA_DRUGS | Medication | (e.g., Paracetamol and Omeprazol) |
| HEALTH | Illness/Diseases | (e.g., Cancer, sjuk and diabetes) |
| Relation | Family members | (e.g., Mamma and Farmor) |
<!-- 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-finetuned-ner_swedish_small_set_health_and_standart
It achieves the following results on the evaluation set:
- Loss: 0.0963
- Precision: 0.7548
- Recall: 0.7811
- F1: 0.7677
- Accuracy: 0.9756
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 219 | 0.1123 | 0.7674 | 0.6567 | 0.7078 | 0.9681 |
| No log | 2.0 | 438 | 0.0934 | 0.7643 | 0.7662 | 0.7652 | 0.9738 |
| 0.1382 | 3.0 | 657 | 0.0963 | 0.7548 | 0.7811 | 0.7677 | 0.9756 |
Framework versions
- Transformers 4.19.3
- Pytorch 1.7.1
- Datasets 2.2.2
- Tokenizers 0.12.1
