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rubert-finetuned-collection3
lsanochkin/rubert-finetuned-collection3
rubert-finetuned-collection3 is a token classification model from lsanochkin. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Dec 26, 2022
- Updated
- Dec 26, 2022
- SHA
- 9327e945
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Datasets
Model card
From the Hugging Face model README
<!-- 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-collection3
This model is a fine-tuned version of sberbank-ai/ruBert-base on the collection3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0514
- Precision: 0.9355
- Recall: 0.9577
- F1: 0.9465
- Accuracy: 0.9865
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0794 | 1.0 | 1163 | 0.0536 | 0.9178 | 0.9466 | 0.9320 | 0.9825 |
| 0.0391 | 2.0 | 2326 | 0.0512 | 0.9228 | 0.9553 | 0.9388 | 0.9853 |
| 0.0191 | 3.0 | 3489 | 0.0514 | 0.9355 | 0.9577 | 0.9465 | 0.9865 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0.dev20220929+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Dec 26, 2022
- Updated
- Dec 26, 2022