Token indices sequence length is longer than the specified maximum sequence length for this model (651 > 512) with Hugging face sentiment classifier - deep-learning

Question

I'm trying to get the sentiments for comments with the help of hugging face sentiment analysis pretrained model. It's returning error like Token indices sequence length is longer than the specified maximum sequence length for this model (651 > 512) with Hugging face sentiment classifier.
Below I'm attaching the code please look at it
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import transformers
import pandas as pd
model = AutoModelForSequenceClassification.from_pretrained('/content/drive/MyDrive/Huggingface-Sentiment-Pipeline')
token = AutoTokenizer.from_pretrained('/content/drive/MyDrive/Huggingface-Sentiment-Pipeline')
classifier = pipeline(task='sentiment-analysis', model=model, tokenizer=token)
data = pd.read_csv('/content/drive/MyDrive/DisneylandReviews.csv', encoding='latin-1')
data.head()
Output is
Review
0 If you've ever been to Disneyland anywhere you...
1 Its been a while since d last time we visit HK...
2 Thanks God it wasn t too hot or too humid wh...
3 HK Disneyland is a great compact park. Unfortu...
4 the location is not in the city, took around 1...
Followed by
classifier("My name is mark")
Output is
[{'label': 'POSITIVE', 'score': 0.9953688383102417}]
Followed by code
basic_sentiment = [i['label'] for i in value if 'label' in i]
basic_sentiment
Output is
['POSITIVE']
Appending the total rows to empty list
text = []
for index, row in data.iterrows():
text.append(row['Review'])
I'm trying to get the sentiment for all the rows
sent = []
for i in range(len(data)):
sentiment = classifier(data.iloc[i,0])
sent.append(sentiment)
The error is :
Token indices sequence length is longer than the specified maximum sequence length for this model (651 > 512). Running this sequence through the model will result in indexing errors
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-19-4bb136563e7c> in <module>()
2
3 for i in range(len(data)):
----> 4 sentiment = classifier(data.iloc[i,0])
5 sent.append(sentiment)
11 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1914 # remove once script supports set_grad_enabled
1915 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1916 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
1917
1918
IndexError: index out of range in self

Answer 1

some of the sentences in your Review column of the data frame are too long. when these sentences are converted to tokens and sent inside the model they are exceeding the 512 seq_length limit of the model, the embedding of the model used in the sentiment-analysis task was trained on 512 tokens embedding.
to fix this issue you can filter out the long sentences and keep only smaller ones (with token length < 512 )
or you can truncate the sentences with truncating = True
sentiment = classifier(data.iloc[i,0], truncation=True)

Answer 2

If you're tokenizing separately from your classification step, this warning can be output during tokenization itself (as opposed to classification).
In my case, I am using a BERT model, so I have MAX_TOKENS=510 (leaving room for the sequence-start and sequence-end tokens).
token = AutoTokenizer.from_pretrained("your model")
tokens = token.tokenize(
text, max_length=MAX_TOKENS, truncation=True
)
Now, when you run your classifier, the tokens are guaranteed not to exceed the maximum length.