---
library_name: transformers
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rubert-finetuned-ner
  results: []
---

<!-- 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. -->

# rubert-finetuned-ner

This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co./DeepPavlov/rubert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1555
- Precision: 0.8890
- Recall: 0.9087
- F1: 0.8988
- Accuracy: 0.9590

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0665        | 0.5   | 625  | 0.2322          | 0.8077    | 0.8335 | 0.8204 | 0.9336   |
| 0.1781        | 1.0   | 1250 | 0.1786          | 0.8379    | 0.8815 | 0.8592 | 0.9483   |
| 0.1083        | 1.5   | 1875 | 0.1828          | 0.8845    | 0.9043 | 0.8943 | 0.9568   |
| 0.0609        | 2.0   | 2500 | 0.1555          | 0.8890    | 0.9087 | 0.8988 | 0.9590   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3