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: 0.1466
- Precision: 0.8876
- Recall: 0.9075
- F1: 0.8975
- Accuracy: 0.9608
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: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 313 | 0.1607 | 0.8568 | 0.8831 | 0.8697 | 0.9542 |
| 0.3113 | 2.0 | 626 | 0.1510 | 0.8780 | 0.9025 | 0.8901 | 0.9579 |
| 0.3113 | 3.0 | 939 | 0.1466 | 0.8876 | 0.9075 | 0.8975 | 0.9608 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2