Token indices sequence length is longer than the specified maximum sequence length error occurs when “you are encoding a sequence that is larger than the max sequence the model can handle (which is 512 tokens” This is a common problem when working with large text inputs.
How to fix the error?
Here are five solutions to fix the error:
- Truncating the input size.
- Splitting the input text into a smaller size.
- Use a model with a larger maximum sequence length.
- Use a Sliding Window approach.
- Choose a Different Model.
Solution 1: Truncate the input size
To fix the error, truncate the input text size to fit within the model’s maximum sequence length.
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("model_name")
max_length = tokenizer.model_max_length
# Truncate the input text to fit within the maximum sequence length
truncated_text = input_text[:max_length]
tokens = tokenizer(truncated_text, truncation=True,
max_length=max_length, padding='max_length',
return_tensors='pt')
You can see that we passed a truncation = True that reduces the token length.
Remember that removing important parts of the text might lead to a loss of information.
Solution 2: Splitting the input text into smaller chunks
You can split the input text into smaller parts and process each separately. This method preserves the information in the text but might lead to less coherent results depending on how the text is split.
def split_text(text, chunk_size):
return [text[i:i+chunk_size]
for i in range(0, len(text), chunk_size)]
tokenizer = AutoTokenizer.from_pretrained("model_name")
max_length = tokenizer.model_max_length
# Split the input text into smaller chunks
chunks = split_text(input_text, max_length)
# Tokenize each chunk
tokens_list = [tokenizer(chunk, truncation=True,
max_length=max_length, padding='max_length',
return_tensors='pt') for chunk in chunks]
Solution 3: Use a model with a larger maximum sequence length
Some models have different versions with larger maximum sequence lengths, such as GPT-3’s gpt-3.5-turbo.
You can switch to a model with a larger sequence length to accommodate longer inputs. However, this might increase the computation time and memory usage.
Solution 4: Use a Sliding Window approach
A sliding window involves breaking the text into overlapping chunks of the maximum size and running the model on each chunk.
Solution 5: Choose a Different Model
If truncating the input or using a sliding window is not suitable for your task, you might consider using a different model that can handle longer sequences.