[Bert] Feature: Custom Model Outputs

[bkonkle]

Closes #22

• Adds an optional Pooler layer for text classification using models like plain BERT instead of RoBERTa.
• Outputs both the last hidden states and the optional Pooler output if enabled.
• Adds the pooler layer to the loader, for using pretrained models.
• Makes the pad_token_idx public for things like the batcher to use.
• Removes .clone() in a few places where it isn't needed.
• Adds .envrc to gitignore for direnv users.
• Adds .vscode to gitignore for VS Code users.

[bkonkle]

Thanks for the review! I tested it, but then made the last-minute change to add with_pooling_layer to the config, instead of passing it in as an argument. :sweat_smile: I failed to test it afterwards, and since this config property isn't found in the original Bert model config I need to default it to false. I'll fix shortly.

I might be able to work up some Github Actions code to run those examples automatically as part of PR checks. I'll open a separate PR for that if I do.

Update: Defaulting it to false with #[config(default = false)] doesn't actually prevent the error, since it looks for the field when it attempts to load the config from the base model file. The fact that it's not present in the base config file is why I originally structured the flag as an argument, but when Nathan suggested moving it into the config I didn't think it would be an issue. :sweat_smile: I'm working towards a solution now.

[bkonkle]

I went with pub with_pooling_layer: Option<bool> to avoid the problems with loading the base model config, coupled with .unwrap_or(false) to resolve the wrapped value.

Examples are working again for me:

Model variant: roberta-base
Input: Shape { dims: [3, 63] } // (Batch Size, Seq_len)
Roberta Sentence embedding Shape { dims: [3, 768] } // (Batch Size, Embedding_dim)

Model variant: bert-base-uncased
Input: Shape { dims: [3, 64] } // (Batch Size, Seq_len)
Roberta Sentence embedding Shape { dims: [3, 768] } // (Batch Size, Embedding_dim)

Model variant: roberta-large
Input: Shape { dims: [3, 63] } // (Batch Size, Seq_len)
Roberta Sentence embedding Shape { dims: [3, 1024] } // (Batch Size, Embedding_dim)

I'm using this in the burn-transformers library like this: https://github.com/bkonkle/burn-transformers/blob/0.2.0/src/models/bert/sequence_classification/text_classification.rs#L130-L131