.mean(dim=1)

Here's an example implementation: ```python import pandas as pd import numpy as np # Generate sample data for 50 tasks np.random.seed(0) # For reproducibility task_ids = [f'Task {i+1}' for i in range(50)] sprint_durations = np.random.cho…

streaming_latency = self.streaming_uploads['latency'].mean() return batch_latency, streaming_latency def compare_throughput(self): # Calculate average throughput for batch and streaming uploads batch_thr…

with torch.no_grad(): doc_outputs = model(**doc_inputs) query_outputs = model(**query_inputs) doc_embeddings = doc_outputs.last_hidden_state.mean(dim=1) query_embedding = query_outputs.last_hidden_state.mean(dim…

# Average the embeddings of the term tokens if term_start is not None and term_end is not None: term_embedding = last_hidden_state[:, term_start:term_end, :].mean(dim=1) else: term_embedding = torch.zeros((1…

- **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…

def add_token(self, token): self.tokens.append(token) self.token_count += 1 def get_context(self): if self.token_count in self.cache: return self.cache[self.token_count] context = list(s…

- Compress large data structures using libraries like `zlib`, `gzip`, `brotli`, or `lz4`. - Store compressed data and decompress it on-the-fly when needed. 5. **Caching**: - Use in-memory caching solutions like Redis or Memcached …

- Ensure that you are using appropriate data types and avoiding unnecessary memory usage. For example, use `pd.to_numeric` to convert columns to numeric types if applicable. 4. **Profiling and Optimization**: - Use profiling tools li…

### Step 3: Initialize Redis for Caching Initialize Redis to cache the contextual embeddings and synonyms: ```python import redis redis_client = redis.Redis(host='localhost', port=6379, db=0) ``` ### Step 4: Generate Contextual Embeddin…