truncation

# Decode the answer answer = tokenizer.decode(outputs[0], skip_special_tokens=True) return answer # Test the function question = "What is the capital of France?" answer = generate_answer(question) print("Answer:", answer) ```…

[2025-05-09 07:28] lisamegawatts: nothing, it is just using center truncation to save credits but no one told it that, so it can't help but cut the middle and doesn't know why as it intends to do what it says and write a whole fille, but th…

# Load pre-trained model and tokenizer model_name = "bert-base-uncased" model = AutoModel.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Move the model to GPU if available device = torch.device("cuda" …

By following these steps and strategies, you can effectively manage the expanded scope of your hybrid retrieval prototype project. Regular communication, prioritization, and iterative development will help ensure that the project stays on t…

Optimize the model for faster inference. This can include quantization, pruning, and using more efficient hardware (e.g., GPUs). ### Step 4: Efficient Caching Ensure that frequently accessed embeddings are cached to reduce redundant compu…

- **Combine Truncation and Filtering**: Apply both truncation and filtering techniques to ensure the expanded query remains concise and relevant. ### Example Implementation Here's an example implementation that incorporates these strat…

[Turn 6905] Assistant: Handling cases where the expanded query becomes too long is important to ensure that the query remains manageable and does not overwhelm the search system. Here are some strategies to manage long expanded queries: ##…

expanded_query = query for lang in languages: if lang != 'en': # Use translation API or model to expand query # For simplicity, we assume a translation function `translate` translated_quer…

self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.max_tokens = max_tokens self.cache = OrderedDict() # Using OrderedDict to maintain LRU behavior self.logger = logging.getLogger(__name__) …

handler.setFormatter(formatter) self.logger.addHandler(handler) def segment(self, input_text): # Tokenize input text inputs = self.tokenizer(input_text, return_tensors='pt', truncation=True, max_length=s…

# Run the main function asyncio.run(main()) ``` ### Explanation 1. **Tokenization and Segmentation**: - Use `truncation=True` and `max_length=self.max_tokens` to ensure that the input sequence is truncated if it exceeds the maximum len…

2. **Expected Outcomes**: - For each query, define the expected resized query or the expected outcome based on the resizing algorithm. 3. **Coverage**: - Ensure that your test data covers a wide range of complexities and scenarios to…

- **Padding and Truncation**: Ensure that padding and truncation are performed consistently across all sequences. - **Error Logging**: Implement proper logging to capture and analyze mismatches for further debugging. By following these ste…

For models that require fixed-length input, you can pad shorter sequences and truncate longer sequences to a fixed length. ### 3. **Dynamic Sparse Tuning** Apply sparse tuning practices dynamically based on the length and content of the qu…

closest_synonyms.extend([synonyms[i] for i in np.argsort(similarities)[-2:]]) # Take top 2 closest synonyms return closest_synonyms # Test the synonym expansion terms = ["happy", "sad", "angry"] for term in terms: synonym…

Your current implementation uses `np.argmax(outputs.logits)` which suggests you are treating the reformulation as a classification problem. However, query reformulation is often better handled as a sequence-to-sequence task. Instead of clas…

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') def get_context_aware_synonyms(word, context_sentence): inputs = tokenizer(context_sentence, return_tensors='pt', pad…

model = T5ForConditionalGeneration.from_pretrained('./fine_tuned_model') def reformulate_query(query): inputs = tokenizer(f"reformulate: {query}", return_tensors="pt", max_length=512, truncation=True) outputs = model.generate(input…

import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from concurrent.futures import ThreadPoolExecutor, as_completed import redis class ReformulationModel: def __init__(self): self.model = AutoModelForSeq2…

model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(df['label'].unique())) tokenizer = AutoTokenizer.from_pretrained(model_name) # Tokenize the data train_encodings = tokenizer(train_df['query'].tolist(), …

tokenizer = AutoTokenizer.from_pretrained(model_name) class LLMBasedReformulator(TransformerMixin): def fit(self, X, y=None): return self def transform(self, X): # Implement LLM-based reformulation logic here …