Update README.md

README.md

@@ -9,7 +9,7 @@ tags:
 
 # Funnel Transformer small (B4-4-4 with decoder) fine-tuned on IMDB for Sentiment Analysis
 
-This are the model weights for the Funnel Transformer small model fine-tuned on the IMDB dataset for performing Sentiment Analysis.
+These are the model weights for the Funnel Transformer small model fine-tuned on the IMDB dataset for performing Sentiment Analysis with `max_position_embeddings=1024`.
 
 The original model weights for English language are from [funnel-transformer/small](https://huggingface.co/funnel-transformer/small) and it uses a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English.
 
@@ -33,9 +33,14 @@ This way, the model learns an inner representation of the English language that
 Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
-from transformers import FunnelTokenizer, FunnelModel
-tokenizer = FunnelTokenizer.from_pretrained("Sreevishnu/funnel-transformer-small-imdb")
-model = FunneModel.from_pretrained("Sreevishnu/funnel-transformer-small-imdb")
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained(
+    "Sreevishnu/funnel-transformer-small-imdb",
+    use_fast=True)
+model = AutoModelForSequenceClassification.from_pretrained(
+    "Sreevishnu/funnel-transformer-small-imdb",
+    num_labels=2,
+    max_position_embeddings=1024)
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)