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roberta-finetuned-domains
redscroll/roberta-finetuned-domains
roberta-finetuned-domains is a text classification model from redscroll. 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
- Sep 28, 2023
- Updated
- Oct 6, 2023
- SHA
- 237aba47
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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. -->
roberta-finetuned-domains
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8502
- F1: 0.3317
- Roc Auc: 0.5777
- Accuracy: 0.1883
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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.2412 | 1.0 | 24797 | 0.8502 | 0.3317 | 0.5777 | 0.1883 |
| 0.129 | 2.0 | 49594 | 0.9576 | 0.3219 | 0.5724 | 0.1962 |
| 0.1072 | 3.0 | 74391 | 1.2442 | 0.3260 | 0.5718 | 0.1906 |
| 0.0422 | 4.0 | 99188 | 1.4241 | 0.3259 | 0.5723 | 0.1927 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Sep 28, 2023
- Updated
- Oct 6, 2023