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finetuning-sentiment
harshavsk/finetuning-sentiment
finetuning-sentiment is a text classification model from harshavsk. 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
- distilbert
- Access
- Public
- Created
- Mar 30, 2024
- Updated
- Mar 30, 2024
- SHA
- d3ab8ee0
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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
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8125
- Accuracy@en: 0.9033
- F1@en: 0.9002
- Precision@en: 0.8989
- Recall@en: 0.9018
- Loss@en: 0.8125
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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy@en | F1@en | Precision@en | Recall@en | Loss@en |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 375 | 0.4653 | 0.8933 | 0.8895 | 0.8895 | 0.8895 | 0.4653 |
| 0.2086 | 2.0 | 750 | 0.4367 | 0.9033 | 0.9011 | 0.8979 | 0.9069 | 0.4367 |
| 0.1622 | 3.0 | 1125 | 0.4866 | 0.91 | 0.9081 | 0.9047 | 0.9151 | 0.4866 |
| 0.0622 | 4.0 | 1500 | 0.6156 | 0.9 | 0.8982 | 0.8951 | 0.9067 | 0.6156 |
| 0.0622 | 5.0 | 1875 | 0.6790 | 0.9133 | 0.9102 | 0.9102 | 0.9102 | 0.6790 |
| 0.0193 | 6.0 | 2250 | 0.6822 | 0.9 | 0.8978 | 0.8945 | 0.9041 | 0.6822 |
| 0.0202 | 7.0 | 2625 | 0.6595 | 0.91 | 0.9077 | 0.9047 | 0.9126 | 0.6595 |
| 0.0148 | 8.0 | 3000 | 0.6538 | 0.9067 | 0.9042 | 0.9014 | 0.9085 | 0.6538 |
| 0.0148 | 9.0 | 3375 | 0.6869 | 0.9067 | 0.9050 | 0.9018 | 0.9136 | 0.6869 |
| 0.0036 | 10.0 | 3750 | 0.7016 | 0.9033 | 0.9007 | 0.8981 | 0.9044 | 0.7016 |
| 0.0038 | 11.0 | 4125 | 0.8170 | 0.9 | 0.8961 | 0.8972 | 0.8951 | 0.8170 |
| 0.008 | 12.0 | 4500 | 0.8169 | 0.9033 | 0.9002 | 0.8989 | 0.9018 | 0.8169 |
| 0.008 | 13.0 | 4875 | 0.8125 | 0.9033 | 0.9002 | 0.8989 | 0.9018 | 0.8125 |
Framework versions
- Transformers 4.17.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 30, 2024
- Updated
- Mar 30, 2024