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norbert2_sentiment_norec_13
NTCAL/norbert2_sentiment_norec_13
norbert2_sentiment_norec_13 is a text classification model from NTCAL. 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
- Mar 7, 2023
- Updated
- Mar 7, 2023
- SHA
- 14e6ce2d
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. -->
norbert2_sentiment_norec_13
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6303
- Compute Metrics: :
- Accuracy: 0.6
- Balanced Accuracy: 0.375
- F1 Score: 0.75
- Recall: 0.75
- Precision: 0.75
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Compute Metrics | Accuracy | Balanced Accuracy | F1 Score | Recall | Precision |
|---|---|---|---|---|---|---|---|---|---|
| 0.6492 | 1.0 | 3 | 0.6466 | : | 0.6 | 0.375 | 0.75 | 0.75 | 0.75 |
| 0.3086 | 2.0 | 6 | 0.6303 | : | 0.6 | 0.375 | 0.75 | 0.75 | 0.75 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 7, 2023
- Updated
- Mar 7, 2023