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bert-base-multilingual-uncased-finetuned-news
Tiamz/bert-base-multilingual-uncased-finetuned-news
bert-base-multilingual-uncased-finetuned-news is a text classification model from Tiamz. Use it when you need a label for a piece of text. 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
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Feb 2, 2023
- Updated
- Feb 2, 2023
- SHA
- db85be31
Try a prompt
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. -->
bert-base-multilingual-uncased-finetuned-news
This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5705
- Accuracy: 0.8896
- F1: 0.8904
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5702 | 1.0 | 159 | 0.5156 | 0.7445 | 0.7553 |
| 0.3384 | 2.0 | 318 | 0.2883 | 0.8612 | 0.8641 |
| 0.2045 | 3.0 | 477 | 0.3699 | 0.8991 | 0.8977 |
| 0.1177 | 4.0 | 636 | 0.5172 | 0.8959 | 0.8969 |
| 0.0465 | 5.0 | 795 | 0.5705 | 0.8896 | 0.8904 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Feb 2, 2023
- Updated
- Feb 2, 2023