tahamajs
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bert-base-swag-lora

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 ---
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 ---
 library_name: transformers
+tags:
+- text-classification
+- multiple-choice
+- swag
+- bert
+- lora
 ---
+# Model Card for bert-base-swag-lora
 ## Model Details
 ### Model Description
+This model is a `bert-base-uncased` model that has been fine-tuned for the multiple-choice question-answering task using the **SWAG (Situations with Adversarial Generations)** dataset. The fine-tuning was performed using a parameter-efficient technique called **LoRA (Low-Rank Adaptation)**, which significantly reduces the number of trainable parameters while achieving strong performance.
+The model is trained to predict the most plausible continuation of a sentence from four possible choices, testing its commonsense and contextual reasoning abilities.
+- **Developed by:** Taha Majlesi
+- **Model type:** BERT (Bidirectional Encoder Representations from Transformers)
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model:** `google-bert/bert-base-uncased`
 ### Model Sources [optional]
+- **Repository:** `https://huggingface.co/[Your Hugging Face Username]/bert-base-swag-lora`
+- **Paper [optional]:** [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) and [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)
 ## Uses
 ### Direct Use
+This model is intended to be used for multiple-choice question answering, specifically on tasks that require commonsense inference similar to the SWAG dataset. It takes a context and four possible endings as input and outputs the index of the most likely correct ending.
+```python
+from transformers import AutoModelForMultipleChoice, AutoTokenizer
+from peft import PeftModel
+import torch
+# Define your repository name
+repo_name = "[Your Hugging Face Username]/bert-base-swag-lora"
+base_model_name = "google-bert/bert-base-uncased"
+# Load the fine-tuned model from the Hub
+tokenizer = AutoTokenizer.from_pretrained(repo_name)
+base_model = AutoModelForMultipleChoice.from_pretrained(base_model_name)
+model = PeftModel.from_pretrained(base_model, repo_name)
+model.eval()
+# Example from SWAG
+context = "A man is skiing down a mountain."
+choices = [
+    "he falls down and gets back up.",
+    "he makes a snowball and throws it.",
+    "he takes a picture of the scenery.",
+    "he stops to drink some water."
+]
+# Prepare the input
+prompt = [context] * 4
+next_sentences = [f"{choices[i]}" for i in range(4)]
+inputs = tokenizer(prompt, next_sentences, return_tensors="pt", padding=True)
+# Reshape for the model
+inputs = {k: v.unsqueeze(0) for k, v in inputs.items()}
+# Get prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    predicted_index = torch.argmax(outputs.logits).item()
+print(f"The most likely ending is: '{choices[predicted_index]}'")
+# Expected output: 'he falls down and gets back up.'