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distilbert-sentiment-sft-cft
wsklee/distilbert-sentiment-sft-cft
distilbert-sentiment-sft-cft is a machine learning model from wsklee. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Nov 13, 2024
- Updated
- Nov 13, 2024
- SHA
- 0c8f894f
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. -->
distilbert-sentiment-sft-cft
This model is a fine-tuned version of wsklee/distilbert-sentiment-sft on the None dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.9960
- Pos Similarity: 0.9757
- Neg Similarity: 0.3175
- F1: 0.9980
- Precision: 1.0
- Recall: 0.9960
- Loss: 3.4705
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Accuracy | Pos Similarity | Neg Similarity | F1 | Precision | Recall | Validation Loss |
|---|---|---|---|---|---|---|---|---|---|
| 3.8387 | 0.5682 | 200 | 0.9928 | 0.9855 | 0.1089 | 0.9964 | 1.0 | 0.9928 | 3.5018 |
| 3.5835 | 1.1364 | 400 | 0.9896 | 0.9875 | 0.4204 | 0.9948 | 1.0 | 0.9896 | 3.4739 |
| 3.525 | 1.7045 | 600 | 0.9944 | 0.9903 | 0.3887 | 0.9972 | 1.0 | 0.9944 | 3.4476 |
| 3.5044 | 2.2727 | 800 | 0.9952 | 0.9878 | 0.4102 | 0.9976 | 1.0 | 0.9952 | 3.4468 |
| 3.4916 | 2.8409 | 1000 | 0.9944 | 0.9902 | 0.4468 | 0.9972 | 1.0 | 0.9944 | 3.4431 |
| 3.4697 | 3.4091 | 1200 | 0.9952 | 0.9814 | 0.3437 | 0.9976 | 1.0 | 0.9952 | 3.4546 |
| 3.4629 | 3.9773 | 1400 | 0.9960 | 0.9795 | 0.3308 | 0.9980 | 1.0 | 0.9960 | 3.4592 |
| 3.4465 | 4.5455 | 1600 | 0.9960 | 0.9757 | 0.3175 | 0.9980 | 1.0 | 0.9960 | 3.4705 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Nov 13, 2024
- Updated
- Nov 13, 2024