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--- |
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license: apache-2.0 |
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base_model: facebook/bart-base |
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datasets: |
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- squad_v2 |
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- drop |
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language: |
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- en |
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library_name: peft |
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tags: |
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- General purpose |
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- Text2text Generation |
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metrics: |
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- bertscore |
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- accuracy |
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- rouge |
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--- |
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# Model Card |
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Base Model: facebook/bart-base |
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Fine-tuned : using PEFT-LoRa |
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Datasets : squad_v2, drop |
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Task: Generating questions from context and answers |
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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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HUGGING_FACE_USER_NAME = "mou3az" |
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model_name = "Question-Generation" |
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peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto') |
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QG_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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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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input_text = f"Given the context '{context}' and the answer '{answer}', what question can be asked?" |
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encoding = QG_tokenizer.encode_plus(input_text, padding=True, return_tensors="pt").to(device) |
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output_tokens = QG_model.generate(**encoding, early_stopping=True, num_beams=5, num_return_sequences=1, no_repeat_ngram_size=2, max_length=100) |
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out = QG_tokenizer.decode(output_tokens[0], skip_special_tokens=True).replace("question:", "").strip() |
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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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r=18 |
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alpha=8 |
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For training arguments: |
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gradient_accumulation_steps=16 |
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per_device_train_batch_size=8 |
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per_device_eval_batch_size=8 |
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max_steps=3000 |
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warmup_steps=75 |
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weight_decay=0.05 |
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learning_rate=1e-3 |
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lr_scheduler_type="linear" |
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# Performance Metrics on Evaluation Set: |
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for 3000 optimization steps: |
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Training Loss: 1.292400 |
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Evaluation Loss: 1.244928 |
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Bertscore: 0.8123 |
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Rouge: 0.532144 |
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Fuzzywizzy similarity: 0.74209 |
