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distilbert-base-uncased-finetuned-H2Physics
danielcwq/distilbert-base-uncased-finetuned-H2Physics
distilbert-base-uncased-finetuned-H2Physics is a question answering model from danielcwq. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Question Answering
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Feb 25, 2023
- Updated
- Mar 6, 2023
- SHA
- 9be941ab
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. -->
distilbert-base-uncased-finetuned-H2Physics
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8149
Model description
This model was pretrained on my Anki cards for the H2 GCE A Levels (Singapore) syllabus, in the hopes of making it a Question and Answer chatbot.
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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 20 | 3.4296 |
| No log | 2.0 | 40 | 2.0993 |
| No log | 3.0 | 60 | 1.1277 |
| No log | 4.0 | 80 | 0.8149 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
- Task
- Question Answering
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Feb 25, 2023
- Updated
- Mar 6, 2023