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How_to_fine-tune_a_model_for_common_downstream_tasks_V2
Tural/How_to_fine-tune_a_model_for_common_downstream_tasks_V2
How_to_fine-tune_a_model_for_common_downstream_tasks_V2 is a question answering model from Tural. Use it when the input is a question plus a passage. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Question Answering
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Oct 11, 2023
- Updated
- Oct 11, 2023
- SHA
- 9b9f449d
Try a prompt
Base models
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. -->
How_to_fine-tune_a_model_for_common_downstream_tasks_V2
This model is a fine-tuned version of Tural/language-modeling-from-scratch on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 3.4298
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: 24
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.647 | 1.0 | 3650 | 3.6697 |
| 3.4239 | 2.0 | 7300 | 3.4835 |
| 3.2087 | 3.0 | 10950 | 3.4298 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.14.1
- Task
- Question Answering
- Library
- transformers
- Type
- bert
- Created
- Oct 11, 2023
- Updated
- Oct 11, 2023