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roberta256_gossip
umangsharmacs/roberta256_gossip
roberta256_gossip is a text classification model from umangsharmacs. 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
- Oct 23, 2024
- Updated
- Oct 23, 2024
- SHA
- 7008450c
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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. -->
roberta256_gossip
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3287
- Accuracy: 0.9031
- F1: 0.8572
- Precision: 0.8157
- Recall: 0.9031
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: 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: 500
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3004 | 1.0 | 199 | 0.2558 | 0.9138 | 0.8931 | 0.8975 | 0.9138 |
| 0.3547 | 2.0 | 398 | 0.3287 | 0.9031 | 0.8572 | 0.8157 | 0.9031 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Oct 23, 2024
- Updated
- Oct 23, 2024