# XLNetForSqeuenceClassification warnings

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**Category:** 🤗Transformers\
**Created:** [September 12, 2020, 11:43am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fxlnetforsqeuenceclassification-warnings%2F1096 "2020-09-12T11:43:01Z")\
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**Showing post:** 13

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**Author:** ![BramVanroy](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/bramvanroy/32/47360_2.png) [@BramVanroy](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FBramVanroy)%5C
**Post date:** [September 23, 2020, 1:22pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fxlnetforsqeuenceclassification-warnings%2F1096%2F13 "2020-09-23T13:22:56Z")

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No, you do not need to override those methods. They should work without needing to change anything. However, now that I take a better look at your code, why do you need a separate model? Why don’t you just use the BertForSequenceClassification model itself? You do not add any layers, right? So I think you can use this

```python
num_labels = 1
config = AutoConfig.from_pretrained("bert-base-uncased",
                                    num_labels=num_labels,
                                    output_attentions=False,
                                    output_hidden_states=False)
bert = BertForSequenceClassification.from_pretrained("bert-base-uncased", config=config)

# train model here...

# Saving/loading using built-in functionality
bert.save_pretrained(save_dir)
# Load the correct weights directly
bert = BertForSequenceClassification.from_pretrained(save_dir,
                                                     num_labels=num_labels,
                                                     output_attentions=False,
                                                     output_hidden_states=False)

# ...or using your own save/load method
checkpoint = {"epochs": epochs, "state_dict": model_save.state_dict()}
torch.save(checkpoint, save_path)

checkpoint = torch.load(save_path)
# NO from_pretrained so we don't unnecessarily load weights twice"
bert = BertForSequenceClassification(config)
bert.load_state_dict(checkpoint["state_dict"])

```

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