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---
library_name: transformers
base_model: ai-forever/ruBert-large
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ruBert-large-upos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: universal_dependencies
type: universal_dependencies
config: ru_syntagrus
split: validation
args: ru_syntagrus
metrics:
- name: Precision
type: precision
value: 0.8307441967265208
- name: Recall
type: recall
value: 0.7502322735093846
- name: F1
type: f1
value: 0.783084706036028
- name: Accuracy
type: accuracy
value: 0.868562326706389
---
<!-- 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-large-upos
This model is a fine-tuned version of [ai-forever/ruBert-large](https://huggingface.co/ai-forever/ruBert-large) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4344
- Precision: 0.8307
- Recall: 0.7502
- F1: 0.7831
- Accuracy: 0.8686
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 338 | 0.4759 | 0.7967 | 0.7249 | 0.7532 | 0.8557 |
| No log | 2.0 | 676 | 0.4344 | 0.8307 | 0.7502 | 0.7831 | 0.8686 |
| No log | 3.0 | 1014 | 0.6906 | 0.7842 | 0.7480 | 0.7563 | 0.8674 |
| No log | 4.0 | 1352 | 0.4757 | 0.8185 | 0.7578 | 0.7777 | 0.8816 |
| No log | 5.0 | 1690 | 0.6291 | 0.7791 | 0.7721 | 0.7670 | 0.8792 |
| No log | 6.0 | 2028 | 0.6466 | 0.7967 | 0.7677 | 0.7721 | 0.8863 |
| No log | 7.0 | 2366 | 0.7072 | 0.7751 | 0.7700 | 0.7704 | 0.8809 |
| No log | 8.0 | 2704 | 0.7623 | 0.7957 | 0.7678 | 0.7749 | 0.8838 |
| No log | 9.0 | 3042 | 0.7458 | 0.7922 | 0.7716 | 0.7773 | 0.8873 |
| No log | 10.0 | 3380 | 0.7560 | 0.7916 | 0.7709 | 0.7767 | 0.8869 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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