The token indices sequence length is longer than the specified maximum sequence length error occurs when the token indices sequence length exceeds the specified maximum sequence length. This is a common problem when working with large text inputs.
To fix the token indices sequence length is longer than the specified maximum sequence length 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.
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]
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.
That’s it.
