Update model weights and inference script

results/runs/Jul26_06-11-45_HarshRaj/events.out.tfevents.1753490507.HarshRaj.35712.0

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+version https://git-lfs.github.com/spec/v1
+oid sha256:766761953873ad81fd44ab797e7caa4a84fa76690af11dece744f7007dc7ad25
+size 5064

train.py

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+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, Trainer, TrainingArguments
+
+# 1) Load dataset from the Hub
+ds = load_dataset("HarshMortal/personal-facts")
+
+# 2) Tokenizer & model
+model_name = "distilbert-base-cased-distilled-squad"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model     = AutoModelForQuestionAnswering.from_pretrained(model_name)
+
+# 3) Preprocess
+def preprocess(ex):
+    inputs = tokenizer(
+        ex["question"], ex["context"],
+        truncation=True, padding="max_length", max_length=384
+    )
+    # map answer start/end here...
+    inputs["start_positions"] = ex["answers"]["answer_start"][0]
+    inputs["end_positions"]   = ex["answers"]["answer_start"][0] + len(ex["answers"]["text"][0])
+    return inputs
+
+tokenized = ds["train"].map(preprocess, batched=False)
+
+# 4) Train
+args = TrainingArguments(
+    output_dir="results",
+    num_train_epochs=2,
+    per_device_train_batch_size=1,
+    logging_steps=5,
+    push_to_hub=True,
+    hub_model_id="HarshMortal/personal-qa",
+    hub_strategy="every_save",
+)
+trainer = Trainer(model=model, args=args, train_dataset=tokenized)
+trainer.train()