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distilbert_sa_GLUE_Experiment_logit_kd_pretrain_stsb
gokuls/distilbert_sa_GLUE_Experiment_logit_kd_pretrain_stsb
distilbert_sa_GLUE_Experiment_logit_kd_pretrain_stsb is a text classification model from gokuls. 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
- Jan 30, 2023
- Updated
- Jan 30, 2023
- SHA
- 02b5ea41
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Datasets
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_sa_GLUE_Experiment_logit_kd_pretrain_stsb
This model is a fine-tuned version of gokuls/distilbert_sa_pre-training-complete on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 0.2956
- Pearson: 0.8628
- Spearmanr: 0.8598
- Combined Score: 0.8613
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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|---|---|---|---|---|---|---|
| 1.8052 | 1.0 | 23 | 1.2355 | 0.2355 | 0.2368 | 0.2361 |
| 0.8213 | 2.0 | 46 | 0.5382 | 0.7641 | 0.7680 | 0.7660 |
| 0.4216 | 3.0 | 69 | 0.3781 | 0.8401 | 0.8371 | 0.8386 |
| 0.2818 | 4.0 | 92 | 0.3205 | 0.8486 | 0.8448 | 0.8467 |
| 0.1988 | 5.0 | 115 | 0.3463 | 0.8489 | 0.8498 | 0.8494 |
| 0.1583 | 6.0 | 138 | 0.3100 | 0.8574 | 0.8539 | 0.8557 |
| 0.1249 | 7.0 | 161 | 0.3252 | 0.8556 | 0.8527 | 0.8542 |
| 0.111 | 8.0 | 184 | 0.3495 | 0.8529 | 0.8497 | 0.8513 |
| 0.099 | 9.0 | 207 | 0.2956 | 0.8628 | 0.8598 | 0.8613 |
| 0.0825 | 10.0 | 230 | 0.3060 | 0.8587 | 0.8555 | 0.8571 |
| 0.0682 | 11.0 | 253 | 0.2985 | 0.8584 | 0.8564 | 0.8574 |
| 0.0671 | 12.0 | 276 | 0.3001 | 0.8568 | 0.8538 | 0.8553 |
| 0.0555 | 13.0 | 299 | 0.3107 | 0.8600 | 0.8568 | 0.8584 |
| 0.0575 | 14.0 | 322 | 0.3221 | 0.8592 | 0.8560 | 0.8576 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Languages
- en
- Created
- Jan 30, 2023
- Updated
- Jan 30, 2023