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README.md
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Language: English
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# Performance Metrics on Evaluation Set:
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Training Loss: 1.1.1958
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Evaluation Loss: 1.109059
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Bertscore: 0.82
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Rouge: 0.56
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Fuzzywizzy similarity: 0.75
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# Loading the model
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```python
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from peft import PeftModel, PeftConfig
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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QG_model = PeftModel.from_pretrained(model, peft_model_id)
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```
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# At inference time
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```python
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def get_question(context, answer):
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device = next(QG_model.parameters()).device
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return out
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```
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# Training parameters and hyperparameters
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The following were used during training:
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# For Lora:
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lr_scheduler_type="linear"
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# Training Results
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| Epoch | Training Loss | Validation Loss |
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|-------|---------------|-----------------|
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| 0.0 | 4.6426 | 4.704238 |
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| 3.0 | 1.5094 | 1.202135 |
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| 6.0 | 1.2677 | 1.146177 |
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| 9.0 | 1.2613 | 1.112074 |
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| 12.0 | 1.1958 | 1.109059 |
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Language: English
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# Loading the model
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```python
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from peft import PeftModel, PeftConfig
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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QG_model = PeftModel.from_pretrained(model, peft_model_id)
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```
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# At inference time
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```python
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def get_question(context, answer):
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device = next(QG_model.parameters()).device
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return out
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```
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# Training parameters and hyperparameters
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The following were used during training:
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# For Lora:
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lr_scheduler_type="linear"
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# Training Results
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| Epoch | Training Loss | Validation Loss |
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|-------|---------------|-----------------|
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| 0.0 | 4.6426 | 4.704238 |
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| 3.0 | 1.5094 | 1.202135 |
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| 6.0 | 1.2677 | 1.146177 |
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| 9.0 | 1.2613 | 1.112074 |
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| 12.0 | 1.1958 | 1.109059 |
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# Performance Metrics on Evaluation Set:
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Training Loss: 1.1.1958
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Evaluation Loss: 1.109059
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Bertscore: 0.82
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Rouge: 0.56
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Fuzzywizzy similarity: 0.75
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