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DEBUGG-DatasetQAS-DEBUGdataset-with-DEBUGmodel
muhammadravi251001/DEBUGG-DatasetQAS-DEBUGdataset-with-DEBUGmodel
DEBUGG-DatasetQAS-DEBUGdataset-with-DEBUGmodel is a question answering model from muhammadravi251001. Use it when the input is a question plus a passage. 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
- Question Answering
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Apr 10, 2023
- Updated
- Apr 17, 2023
- SHA
- 240ebad0
Try a prompt
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. -->
DEBUGG-DatasetQAS-DEBUGdataset-with-DEBUGmodel
This model is a fine-tuned version of indolem/indobert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6261
- Exact Match: 46.0733
- F1: 54.0012
- Precision: 53.9579
- Recall: 55.6254
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 1.9553 | 0.5 | 306 | 1.9202 | 36.3002 | 44.3381 | 44.0903 | 46.4319 |
| 1.6765 | 1.0 | 612 | 1.6261 | 46.0733 | 54.0012 | 53.9579 | 55.6254 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.2.0
- Tokenizers 0.13.3
- Task
- Question Answering
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Apr 10, 2023
- Updated
- Apr 17, 2023