---
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: 0.2633
- Precision: 0.7560
- Recall: 0.8032
- F1: 0.7789
- Accuracy: 0.9251

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4805        | 0.4   | 500  | 0.4017          | 0.6644    | 0.7072 | 0.6852 | 0.8788   |
| 0.3281        | 0.8   | 1000 | 0.2818          | 0.7416    | 0.7886 | 0.7644 | 0.9203   |
| 0.165         | 1.2   | 1500 | 0.2653          | 0.7573    | 0.8023 | 0.7792 | 0.9244   |
| 0.2539        | 1.6   | 2000 | 0.2633          | 0.7571    | 0.8040 | 0.7799 | 0.9252   |
| 0.252         | 2.0   | 2500 | 0.2633          | 0.7560    | 0.8032 | 0.7789 | 0.9251   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1