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finetuning-sentiment-analysis-en
viviastaari/finetuning-sentiment-analysis-en
finetuning-sentiment-analysis-en is a text classification model from viviastaari. 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
- May 23, 2022
- Updated
- May 23, 2022
- SHA
- c1eaf383
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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. -->
finetuning-sentiment-analysis-en
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0792
- Accuracy: 0.9803
- F1: 0.9856
- Precision: 0.9875
- Recall: 0.9837
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: 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 | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.426 | 1.0 | 1408 | 0.2718 | 0.8910 | 0.9201 | 0.9251 | 0.9151 |
| 0.3247 | 2.0 | 2816 | 0.1552 | 0.9540 | 0.9665 | 0.9656 | 0.9674 |
| 0.1582 | 3.0 | 4224 | 0.0792 | 0.9803 | 0.9856 | 0.9875 | 0.9837 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 23, 2022
- Updated
- May 23, 2022