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micro_base_help_class_tapt_seed_2
BigTMiami/micro_base_help_class_tapt_seed_2
micro_base_help_class_tapt_seed_2 is a text classification model from BigTMiami. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Apr 20, 2024
- Updated
- Apr 20, 2024
- SHA
- 85c0fc88
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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. -->
micro_base_help_class_tapt_seed_2
This model is a fine-tuned version of BigTMiami/micro_base_help_tapt_pretrain_model on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3416
- Accuracy: 0.8534
- F1 Macro: 0.6723
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: 16
- eval_batch_size: 16
- seed: 2
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.3574 | 1.0 | 313 | 0.3770 | 0.8534 | 0.4605 |
| 0.3055 | 2.0 | 626 | 0.3547 | 0.847 | 0.6722 |
| 0.2465 | 3.0 | 939 | 0.4434 | 0.8644 | 0.6004 |
| 0.1874 | 4.0 | 1252 | 0.5127 | 0.8588 | 0.6173 |
| 0.1542 | 5.0 | 1565 | 0.7644 | 0.8424 | 0.6561 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Apr 20, 2024
- Updated
- Apr 20, 2024