Download training data from S3

Removed unnecessary import

Removed train_base_models task. Not needed with RoBERTa models

Added gradio to requirements.txt

Configured gradio app for training

Pin allennlp and torch to solve package conflics (#2)

Added HuggingFace Space configuration YAML to README.md

Added HuggingFace Space configuration YAML to README.md

Created HuggingFace Space

Removed diagnostic

Ensure consitency value is 32 bit

Removed diagnostics

Correct dimension of consistency cosine

Wrap CrossEntropyLoss in callable that makes it appplicable to sequences

Using torch.nn.CosineSimilarity to simplify code

Use builtin sum rather than torch.sum for a generator

Removed unnecessary parameters

Attention mask in decoder

Set use_cache argument

Fix Einstein summation notation

Use keepdim option when normalising vectors

Make EPSILON a tensor

torch.maximum, not torch.max

Unsqueeze attention mask

Unpack BatchEncoding

Create BatchEncoding in pad

Use BatchEncoding for training, not batch_encoding

Use BatchEncoding for training

Removed unnecessary on_epoch_end

Learning rate scheduler

input_embeddings not needed

Removed unnecessary parameter

get_input_embeddings() directly from base model

Missing 'from_pretrained'

config didn't need to be a property

There's a simpler way of doing this, I hope

Might be simpler to inherit from RobertaModel rather than PreTrainedModel

Removed a base model that was causing a loop in model initialisation

Problems with config

Fixed typo

Removed line that would have failed

Fixed import

Typo

Further changes for compatibility with HuggingFace Pytorch implementation

Minor error in setting up tokenizer

PyTorch implementation of HugggingFace PreTrainedModel class does not allow direct setting of base_model. Rejig constructors accordingly

Removed superfluous ()

Removed superfluous ()

Corrected inheritance

Updated requitements.txt