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sentiment-roberta-e6-b16-data2
YanJiangJerry/sentiment-roberta-e6-b16-data2
sentiment-roberta-e6-b16-data2 is a text classification model from YanJiangJerry. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Jul 14, 2023
- Updated
- Jul 14, 2023
- SHA
- a4d6a180
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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. -->
sentiment-roberta-e6-b16-data2
This model is a fine-tuned version of siebert/sentiment-roberta-large-english on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4505
- F1: 0.7682
- Recall: 0.7682
- Precision: 0.7682
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|---|---|---|---|---|---|---|
| No log | 1.0 | 375 | 0.7961 | 0.7089 | 0.7089 | 0.7089 |
| 0.6924 | 2.0 | 750 | 0.6880 | 0.7601 | 0.7601 | 0.7601 |
| 0.3191 | 3.0 | 1125 | 1.1324 | 0.7520 | 0.7520 | 0.7520 |
| 0.1802 | 4.0 | 1500 | 1.2056 | 0.7682 | 0.7682 | 0.7682 |
| 0.1802 | 5.0 | 1875 | 1.3942 | 0.7736 | 0.7736 | 0.7736 |
| 0.088 | 6.0 | 2250 | 1.4505 | 0.7682 | 0.7682 | 0.7682 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Jul 14, 2023
- Updated
- Jul 14, 2023