metadata
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rubert-finetuned-ner
results: []
rubert-finetuned-ner
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: 0.1538
- Precision: 0.8891
- Recall: 0.9071
- F1: 0.8980
- Accuracy: 0.9591
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.088 | 0.5 | 625 | 0.2382 | 0.8027 | 0.8614 | 0.8310 | 0.9320 |
| 0.1155 | 1.0 | 1250 | 0.1831 | 0.8518 | 0.8830 | 0.8671 | 0.9474 |
| 0.1477 | 1.5 | 1875 | 0.1770 | 0.8814 | 0.9012 | 0.8912 | 0.9561 |
| 0.0629 | 2.0 | 2500 | 0.1538 | 0.8891 | 0.9071 | 0.8980 | 0.9591 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1