metadata
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
- medical
- pharmacy
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rubert-ner-drugname
results: []
language:
- ru
rubert-ner-drugname
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0365
- Precision: 0.7055
- Recall: 0.7658
- F1: 0.7344
- Accuracy: 0.9885
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 61 | 0.0524 | 0.7588 | 0.5475 | 0.6360 | 0.9850 |
| No log | 2.0 | 122 | 0.0485 | 0.56 | 0.7975 | 0.6580 | 0.9825 |
| No log | 3.0 | 183 | 0.0361 | 0.7029 | 0.7563 | 0.7287 | 0.9884 |
| No log | 4.0 | 244 | 0.0368 | 0.7591 | 0.7278 | 0.7431 | 0.9894 |
| No log | 5.0 | 305 | 0.0365 | 0.7055 | 0.7658 | 0.7344 | 0.9885 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1