Padding

```python def retrieve(queries): # Tokenize the queries inputs = tokenizer(queries, padding=True, truncation=True, return_tensors="pt") # Perform retrieval using the LLM outputs = model(**inputs…

# Load the LLM model and tokenizer model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") tokenizer = AutoTokenizer.from_pretrained("t5-base") # Define a function to generate answers def generate_answer(question): # Tokenize the ques…

- Utilize efficient libraries and frameworks that are optimized for CPU usage, such as TensorFlow or PyTorch. ### Example Implementation Here's an example of how you can fine-tune Llama 2 13B on a CPU with these strategies: #### 1. Lo…

#### 1. Data Preprocessing ```python from transformers import LlamaTokenizer import torch # Load tokenizer tokenizer = LlamaTokenizer.from_pretrained("llama-2-13b") # Tokenize dataset def tokenize_function(examples): return tokenizer…

Finally, you can combine the results from both sparse and dense retrievals. One common approach is to use a weighted sum of the scores from both methods. Here's a more complete example: ```python import numpy as np from sklearn.feature_ex…

- **Multilingual Embeddings**: Use pre-trained models like `BERT` or `mBert`. - **Cross-Lingual Indexing**: Implement indexing using embeddings. - **Query Expansion**: Use translation APIs to expand queries. - **Hybrid Ranking**: Co…

- Ensure the new model integrates seamlessly with the rest of the retrieval pipeline. ### Example Implementation #### Step 1: Data Preparation Prepare your dataset for training and validation: ```python from transformers import AutoT…

def __init__(self, queries, passages, tokenizer): self.queries = queries self.passages = passages self.tokenizer = tokenizer def __getitem__(self, idx): query = self.queries[idx] passage = se…

inputs = tokenizer(term, return_tensors='pt', padding=True, truncation=True) outputs = model(**inputs) embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling return embeddings ``` ### Step 4: Retrieve Synonyms B…

Here's an example of a hybrid approach that combines WordNet and context-aware embeddings: ```python from transformers import BertTokenizer, BertModel import torch import nltk from nltk.corpus import wordnet nltk.download('wordnet') toke…

for future in as_completed(futures): results.extend(future.result()) return results # Example usage: queries = ["What is the capital of France?", "Who is the president of the United States?", ...] reformulated_q…