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bert-large-uncased-finetuned-mrpc
VitaliiVrublevskyi/bert-large-uncased-finetuned-mrpc
bert-large-uncased-finetuned-mrpc is a text classification model from VitaliiVrublevskyi. 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
- Dec 3, 2023
- Updated
- Dec 3, 2023
- SHA
- 610e1493
Try a prompt
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. -->
bert-large-uncased-finetuned-mrpc
This model is a fine-tuned version of bert-large-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6016
- Accuracy: 0.8578
- F1: 0.9007
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: 32
- eval_batch_size: 32
- seed: 91
- 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 |
|---|---|---|---|---|---|
| No log | 1.0 | 115 | 0.4435 | 0.8162 | 0.8777 |
| No log | 2.0 | 230 | 0.3542 | 0.8407 | 0.8870 |
| No log | 3.0 | 345 | 0.4246 | 0.8652 | 0.9063 |
| No log | 4.0 | 460 | 0.5290 | 0.8578 | 0.9010 |
| 0.2887 | 5.0 | 575 | 0.6016 | 0.8578 | 0.9007 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Dec 3, 2023
- Updated
- Dec 3, 2023