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26 changes: 26 additions & 0 deletions README.md
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# CS 224N Default Final Project - Multitask BERT

This is the starting code for the default final project for the Stanford CS 224N class.

In this project, you will implement some important components of the BERT model to better understanding its architecture.
You will then use the embeddings produced by your BERT model on three downstream tasks: sentiment classification, paraphrase detection and semantic similarity.

After finishing the BERT implementation, you will have a simple model that simultaneously performs the three tasks.
You will then implement extensions to improve on top of this baseline.

## Setup instructions

* Follow `setup.sh` to properly setup a conda environment and install dependencies.
* There is a detailed description of the code structure in [STRUCTURE.md](./STRUCTURE.md), including a description of which parts you will need to implement.
* You are only allowed to use libraries that are installed by `setup.sh`, external libraries that give you other pre-trained models or embeddings are not allowed (e.g., `transformers`).

## Handout

Please refer to the handout for a through description of the project and its parts.

### Acknowledgement

The BERT implementation part of the project was adapted from the "minbert" assignment developed at Carnegie Mellon University's [CS11-711 Advanced NLP](http://phontron.com/class/anlp2021/index.html),
created by Shuyan Zhou, Zhengbao Jiang, Ritam Dutt, Brendon Boldt, Aditya Veerubhotla, and Graham Neubig.

Parts of the code are from the [`transformers`](https://github.com/huggingface/transformers) library ([Apache License 2.0](./LICENSE)).
88 changes: 88 additions & 0 deletions STRUCTURE.md
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# Structure of the code base

## bert.py
This file contains the BERT Model whose backbone is the [transformer](https://arxiv.org/pdf/1706.03762.pdf). We recommend walking through Section 3 of the paper to understand each component of the transformer. This is where you'll start your implementation: your first goal is to complete this file in order to pass the tests in `sanity_check.py`.

### BertSelfAttention
This class should implement the multi-head attention layer of the transformer. This layer maps a query and a set of key-value pairs to an output. The output is calculated as the weighted sum of the values, where the weight of each value is computed by a function that takes the query and the corresponding key. To implement this layer, you should:

1. Linearly project the queries, keys, and values with their corresponding linear layers
2. Split the vectors for multi-head attention
3. Follow the equation to compute the attended output of each head
4. Concatenate multi-head attention outputs to recover the original shape

$$Attention(Q,K,V)=softmax(\frac{QK^T}{\sqrt{d_k}})V$$

### BertLayer
This corresponds to one transformer layer which has
1. A multi-head attention layer
2. Add-norm layer
3. A feed-forward layer
4. Another add-norm layer

### BertModel
This is the BertModel that takes the input ids and returns the contextualized representation for each word. The structure of the ```BertModel``` is:
1. an embedding layer that consists of word embedding ```word_embedding``` and positional embedding```pos_embedding```.
2. bert encoder layer which is a stack of ```config.num_hidden_layers``` ```BertLayer```
3. a projection layer for [CLS] token which is often used for classification tasks

The desired outputs are
1. ```last_hidden_state```: the contextualized embedding for each word of the sentence, taken from the last BertLayer (i.e. the output of the bert encoder)
2. ```pooler_output```: the [CLS] token embedding

### To be implemented
Components that require your implementations are marked with ```TODO```. More detailed instructions can be found in their corresponding code blocks, in the following functions:

* ```bert.BertSelfAttention.attention```
* ```bert.BertLayer.add_norm```
* ```bert.BertLayer.forward```
* ```bert.BertModel.embed```

*ATTENTION:* you are free to re-organize the functions inside each class, but please don't change the variable names that correspond to BERT parameters. The change to these variable names will fail to load the pre-trained weights.

### Sanity check
We provide a sanity check function at sanity_check.py to test your implementation. It will reload two embeddings we computed with our reference implementation and check whether your implementation outputs match ours.

## optimizer.py (to be implemented)
This is where `AdamW` is defined.
You will need to update the `step()` function based on [Decoupled Weight Decay Regularization](https://arxiv.org/abs/1711.05101) and [Adam: A Method for Stochastic Optimization](https://arxiv.org/abs/1412.6980).
There are a few slight variations on AdamW, pleae note the following:
- The reference uses the "efficient" method of computing the bias correction mentioned at the end of section 2 "Algorithm" in Kigma & Ba (2014) in place of the intermediate m hat and v hat method.
- The learning rate is incorporated into the weight decay update.
- There is no learning rate schedule: we'll use the same alpha throughout.

You can check your optimizer implementation using `optimizer_test.py`.

## main.py
This file contains the pipeline to
* Call the BERT model to encode the sentences for their contextualized representations
* Fine-tune the BERT model on the downstream tasks


### SentenceEmbedding (to be implemented)
This class is used to encode the sentences using BERT to obtain the pooled output representation of the sentence.
You can start with a trivial implementation that just returns the plain BERT embeddings, and potentially
play with it later.

### train_multitask (to be implemented)
This is where you'll implement fine-tuning. This should improve on top of your vanilla BERT
implementation once that one is working.

## base_bert.py

This is the base class for the BertModel, and it is already fully implemented for you. It contains functions to
1. Initialize the weights ``init_weights``, ``_init_weights``
2. Restore pre-trained weights ``from_pretrained``. Since we are using the weights from HuggingFace, we are doing a few mappings to match the parameter names
You won't need to modify this file in your project.

## tokenizer.py
This is where `BertTokenizer` is defined. You won't need to modify this file in your project.

## config.py
This is where the configuration class is defined. You won't need to modify this file in your project.

## utils.py
This file contains utility functions for various purpose. You won't need to modify this file in your project.

## Reference
[Vaswani el at. + 2017] Attention is all you need https://arxiv.org/pdf/1706.03762.pdf
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