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distilroberta-base-finetuned-rightarticles-mlm
happybusinessperson/distilroberta-base-finetuned-rightarticles-mlm
distilroberta-base-finetuned-rightarticles-mlm is a fill-mask model from happybusinessperson. Use it when you need the model to fill a missing word. 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
- Fill-Mask
- Library
- transformers
- License
- apache-2.0
- Model type
- roberta
- Access
- Public
- Created
- Mar 7, 2024
- Updated
- Mar 7, 2024
- SHA
- 51f73037
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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. -->
distilroberta-base-finetuned-rightarticles-mlm
This model is a fine-tuned version of distilroberta-base on a dataset of right wing news articles, happybusinessperson/rightarticles, adapted from https://www.kaggle.com/datasets/mhoali/right-and-left-wing-news-articles-with-nlp. It achieves the following results on the evaluation set:
It achieves the following results on the evaluation set:
- Loss: 1.7292
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9611 | 1.0 | 806 | 1.7669 |
| 1.8516 | 2.0 | 1612 | 1.7297 |
| 1.798 | 3.0 | 2418 | 1.7239 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Fill-Mask
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Created
- Mar 7, 2024
- Updated
- Mar 7, 2024