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rubert-finetuned-ner
dondosss/rubert-finetuned-ner
rubert-finetuned-ner is a token classification model from dondosss. Use it when you need labels on individual words, such as names. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Apr 18, 2024
- Updated
- Apr 19, 2024
- SHA
- abf9f52d
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Base models
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-ner
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1538
- Precision: 0.8891
- Recall: 0.9071
- F1: 0.8980
- Accuracy: 0.9591
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: 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 |
|---|---|---|---|---|---|---|---|
| 0.088 | 0.5 | 625 | 0.2382 | 0.8027 | 0.8614 | 0.8310 | 0.9320 |
| 0.1155 | 1.0 | 1250 | 0.1831 | 0.8518 | 0.8830 | 0.8671 | 0.9474 |
| 0.1477 | 1.5 | 1875 | 0.1770 | 0.8814 | 0.9012 | 0.8912 | 0.9561 |
| 0.0629 | 2.0 | 2500 | 0.1538 | 0.8891 | 0.9071 | 0.8980 | 0.9591 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Created
- Apr 18, 2024
- Updated
- Apr 19, 2024