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bert-base-multilingual-cased-finetuned-twitter_sentiment
Gerti/bert-base-multilingual-cased-finetuned-twitter_sentiment
bert-base-multilingual-cased-finetuned-twitter_sentiment is a text classification model from Gerti. 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
- Jan 23, 2024
- Updated
- Jan 26, 2024
- SHA
- 6f3e4785
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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. -->
bert-base-multilingual-cased-finetuned-twitter_sentiment
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0045
- F1-score: 0.9985
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 | F1-score |
|---|---|---|---|---|
| 0.1961 | 1.0 | 1080 | 0.0873 | 0.9819 |
| 0.0918 | 2.0 | 2160 | 0.0252 | 0.9935 |
| 0.0737 | 3.0 | 3240 | 0.0073 | 0.9985 |
| 0.0298 | 4.0 | 4320 | 0.0087 | 0.9981 |
| 0.01 | 5.0 | 5400 | 0.0045 | 0.9985 |
Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jan 23, 2024
- Updated
- Jan 26, 2024