thesaurus

thesaurus.add_synonym("sad", "unhappy") thesaurus.add_synonym("sad", "depressed") # Test the lookup start_time = time.time() synonyms = thesaurus.get_synonyms("happy") end_time = time.time() print(f"Lookup took {end_time - start_time} seco…

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…

term_embedding = get_contextual_embeddings(term) closest_synonyms = [] for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_context…

### 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…

for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…

[Turn 10159] Assistant: To determine which subtasks will likely take the most time, let's analyze each subtask in the context of implementing an advanced NLP model for synonym expansion and integrating it with an existing thesaurus and cach…

- **Time-Consuming Aspects**: - Model selection and configuration. - Integration with existing systems. - Performance tuning and optimization. 2. **Integrate with Existing Thesaurus** - **Steps**: - Map the output…