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edos-2023-baseline-roberta-base-label_category
lct-rug-2022/edos-2023-baseline-roberta-base-label_category
edos-2023-baseline-roberta-base-label_category is a text classification model from lct-rug-2022. 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
- Nov 28, 2022
- Updated
- Nov 29, 2022
- SHA
- 52b1995d
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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. -->
edos-2023-baseline-roberta-base-label_category
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0133
- F1: 0.5792
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 12
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 1.169 | 1.18 | 100 | 1.0580 | 0.2159 |
| 0.9143 | 2.35 | 200 | 0.9283 | 0.5405 |
| 0.7535 | 3.53 | 300 | 0.9387 | 0.5665 |
| 0.6085 | 4.71 | 400 | 0.9574 | 0.5664 |
| 0.53 | 5.88 | 500 | 1.0133 | 0.5792 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Nov 28, 2022
- Updated
- Nov 29, 2022