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hbertv1-emotion_no_pretrain
gokuls/hbertv1-emotion_no_pretrain
hbertv1-emotion_no_pretrain 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.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- hybridbert
- Access
- Public
- Created
- Nov 17, 2023
- Updated
- Nov 17, 2023
- SHA
- d6f82829
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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. -->
hbertv1-emotion_no_pretrain
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4460
- Accuracy: 0.87
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: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1546 | 1.0 | 6263 | 0.6487 | 0.8115 |
| 0.934 | 2.0 | 12526 | 0.5827 | 0.8245 |
| 0.8526 | 3.0 | 18789 | 0.4671 | 0.865 |
| 0.7542 | 4.0 | 25052 | 0.4460 | 0.87 |
| 0.6786 | 5.0 | 31315 | 0.4451 | 0.8625 |
| 0.6241 | 6.0 | 37578 | 0.4768 | 0.8535 |
| 0.5603 | 7.0 | 43841 | 0.5052 | 0.839 |
| 0.5114 | 8.0 | 50104 | 0.5268 | 0.8315 |
| 0.4735 | 9.0 | 56367 | 0.5613 | 0.8175 |
| 0.4465 | 10.0 | 62630 | 0.5862 | 0.814 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Created
- Nov 17, 2023
- Updated
- Nov 17, 2023