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RoBERTa-GPT2_EmpAI
LuangMV97/RoBERTa-GPT2_EmpAI
RoBERTa-GPT2_EmpAI is a machine learning model from LuangMV97. 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.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- encoder-decoder
- Access
- Public
- Created
- Mar 30, 2024
- Updated
- Mar 31, 2024
- SHA
- ec7e8d38
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. -->
RoBERTa-GPT2_EmpAI_FineTuned
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9871
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.3753 | 1.0 | 3867 | 4.3100 |
| 2.873 | 2.0 | 7734 | 2.8619 |
| 2.0204 | 3.0 | 11601 | 2.0694 |
| 1.6845 | 4.0 | 15468 | 1.7321 |
| 1.5994 | 5.0 | 19335 | 1.6773 |
| 1.3579 | 6.0 | 23202 | 1.3416 |
| 1.0921 | 7.0 | 27069 | 1.1302 |
| 0.975 | 8.0 | 30936 | 1.0375 |
| 0.9599 | 9.0 | 34803 | 0.9928 |
| 0.9228 | 10.0 | 38670 | 0.9871 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Library
- transformers
- Type
- encoder-decoder
- Created
- Mar 30, 2024
- Updated
- Mar 31, 2024