File size: 3,157 Bytes
f1cc377 d291600 f1cc377 324c3b0 f1cc377 324c3b0 f1cc377 d291600 f1cc377 d291600 f1cc377 324c3b0 f1cc377 d291600 f1cc377 d291600 f1cc377 d291600 f1cc377 d291600 f1cc377 d291600 324c3b0 d291600 324c3b0 d291600 324c3b0 d291600 324c3b0 d291600 324c3b0 d291600 324c3b0 d291600 f1cc377 e743730 f1cc377 324c3b0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
---
language:
- ru
license: apache-2.0
tags:
- generated_from_trainer
- named-entity-recognition
- russian
- ner
datasets:
- RCC-MSU/collection3
metrics:
- precision
- recall
- f1
- accuracy
thumbnail: Sberbank RuBERT-base fintuned on Collection3 dataset
base_model: sberbank-ai/ruBert-base
model-index:
- name: sberbank-rubert-base-collection3
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: RCC-MSU/collection3
type: named-entity-recognition
config: default
split: validation
args: default
metrics:
- type: precision
value: 0.938019472809309
name: Precision
- type: recall
value: 0.9594364828758805
name: Recall
- type: f1
value: 0.9486071085494716
name: F1
- type: accuracy
value: 0.9860420020488805
name: Accuracy
- task:
type: token-classification
name: Token Classification
dataset:
name: RCC-MSU/collection3
type: named-entity-recognition
config: default
split: test
args: default
metrics:
- type: precision
value: 0.9419896321895829
name: Precision
- type: recall
value: 0.9537615596100975
name: Recall
- type: f1
value: 0.947839046199702
name: F1
- type: accuracy
value: 0.9847255179564897
name: Accuracy
---
<!-- 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. -->
# sberbank-rubert-base-collection3
This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base) on the collection3 dataset.
It achieves the following results on the validation set:
- Loss: 0.0772
- Precision: 0.9380
- Recall: 0.9594
- F1: 0.9486
- Accuracy: 0.9860
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0899 | 1.0 | 2326 | 0.0760 | 0.9040 | 0.9330 | 0.9182 | 0.9787 |
| 0.0522 | 2.0 | 4652 | 0.0680 | 0.9330 | 0.9339 | 0.9335 | 0.9821 |
| 0.0259 | 3.0 | 6978 | 0.0745 | 0.9308 | 0.9512 | 0.9409 | 0.9838 |
| 0.0114 | 4.0 | 9304 | 0.0731 | 0.9372 | 0.9573 | 0.9471 | 0.9857 |
| 0.0027 | 5.0 | 11630 | 0.0772 | 0.9380 | 0.9594 | 0.9486 | 0.9860 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.7.0
- Datasets 2.10.1
- Tokenizers 0.13.2 |