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robbert-twitter-sentiment-custom
btjiong/robbert-twitter-sentiment-custom
robbert-twitter-sentiment-custom is a text classification model from btjiong. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Apr 7, 2022
- Updated
- Apr 8, 2022
- SHA
- 65831608
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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. -->
robbert-twitter-sentiment-custom
This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base on the dutch_social dataset. It achieves the following results on the evaluation set:
- Loss: 0.6656
- Accuracy: 0.788
- F1: 0.7878
- Precision: 0.7877
- Recall: 0.788
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: 16
- eval_batch_size: 16
- 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 | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.8287 | 1.0 | 282 | 0.7178 | 0.7007 | 0.6958 | 0.6973 | 0.7007 |
| 0.4339 | 2.0 | 564 | 0.5873 | 0.7667 | 0.7668 | 0.7681 | 0.7667 |
| 0.2045 | 3.0 | 846 | 0.6656 | 0.788 | 0.7878 | 0.7877 | 0.788 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Apr 7, 2022
- Updated
- Apr 8, 2022