# Homework and bake-off: word-level entailment with neural networks

```python
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2020"
```

## Contents

1. [Overview](#Overview)
1. [Set-up](#Set-up)
1. [Data](#Data)
  1. [Edge disjoint](#Edge-disjoint)
  1. [Word disjoint](#Word-disjoint)
1. [Baseline](#Baseline)
  1. [Representing words: vector_func](#Representing-words:-vector_func)
  1. [Combining words into inputs: vector_combo_func](#Combining-words-into-inputs:-vector_combo_func)
  1. [Classifier model](#Classifier-model)
  1. [Baseline results](#Baseline-results)
1. [Homework questions](#Homework-questions)
  1. [Hypothesis-only baseline [2 points]](#Hypothesis-only-baseline-[2-points])
  1. [Alternatives to concatenation [2 points]](#Alternatives-to-concatenation-[2-points])
  1. [A deeper network [2 points]](#A-deeper-network-[2-points])
  1. [Your original system [3 points]](#Your-original-system-[3-points])
1. [Bake-off [1 point]](#Bake-off-[1-point])

## Overview

The general problem is word-level natural language inference.

Training examples are pairs of words $(w_{L}, w_{R}), y$ with $y = 1$ if $w_{L}$ entails $w_{R}$, otherwise $0$.

The homework questions below ask you to define baseline models for this and develop your own system for entry in the bake-off, which will take place on a held-out test-set distributed at the start of the bake-off. (Thus, all the data you have available for development is available for training your final system before the bake-off begins.)

## Set-up

See [the first notebook in this unit](nli_01_task_and_data.ipynb) for set-up instructions.

```python
from collections import defaultdict
import json
import numpy as np
import os
import pandas as pd
from torch_shallow_neural_classifier import TorchShallowNeuralClassifier
import nli
import utils
```

```python
DATA_HOME = 'data'

NLIDATA_HOME = os.path.join(DATA_HOME, 'nlidata')

wordentail_filename = os.path.join(
    NLIDATA_HOME, 'nli_wordentail_bakeoff_data.json')

GLOVE_HOME = os.path.join(DATA_HOME, 'glove.6B')
```

## Data

I've processed the data into two different train/test splits, in an effort to put some pressure on our models to actually learn these semantic relations, as opposed to exploiting regularities in the sample.

* `edge_disjoint`: The `train` and `dev` __edge__ sets are disjoint, but many __words__ appear in both `train` and `dev`.
* `word_disjoint`: The `train` and `dev` __vocabularies are disjoint__, and thus the edges are disjoint as well.

These are very different problems. For `word_disjoint`, there is real pressure on the model to learn abstract relationships, as opposed to memorizing properties of individual words.

```python
with open(wordentail_filename) as f:
    wordentail_data = json.load(f)
```

The outer keys are the  splits plus a list giving the vocabulary for the entire dataset:

```python
wordentail_data.keys()
```

    dict_keys(['edge_disjoint', 'vocab', 'word_disjoint'])

### Edge disjoint

```python
wordentail_data['edge_disjoint'].keys()
```

    dict_keys(['dev', 'train'])

This is what the split looks like; all three have this same format:

```python
wordentail_data['edge_disjoint']['dev'][: 5]
```

    [[['sweater', 'stroke'], 0],
     [['constipation', 'hypovolemia'], 0],
     [['disease', 'inflammation'], 0],
     [['herring', 'animal'], 1],
     [['cauliflower', 'outlook'], 0]]

Let's test to make sure no edges are shared between `train` and `dev`:

```python
nli.get_edge_overlap_size(wordentail_data, 'edge_disjoint')
```

    0

As we expect, a *lot* of vocabulary items are shared between `train` and `dev`:

```python
nli.get_vocab_overlap_size(wordentail_data, 'edge_disjoint')
```

    2916

This is a large percentage of the entire vocab:

```python
len(wordentail_data['vocab'])
```

    8470

Here's the distribution of labels in the `train` set. It's highly imbalanced, which will pose a challenge for learning. (I'll go ahead and reveal that the `dev` set is similarly distributed.)

```python
def label_distribution(split):
    return pd.DataFrame(wordentail_data[split]['train'])[1].value_counts()
```

```python
label_distribution('edge_disjoint')
```

    0    14650
    1     2745
    Name: 1, dtype: int64

### Word disjoint

```python
wordentail_data['word_disjoint'].keys()
```

    dict_keys(['dev', 'train'])

In the `word_disjoint` split, no __words__ are shared between `train` and `dev`:

```python
nli.get_vocab_overlap_size(wordentail_data, 'word_disjoint')
```

    0

Because no words are shared between `train` and `dev`, no edges are either:

```python
nli.get_edge_overlap_size(wordentail_data, 'word_disjoint')
```

    0

The label distribution is similar to that of `edge_disjoint`, though the overall number of examples is a bit smaller:

```python
label_distribution('word_disjoint')
```

    0    7199
    1    1349
    Name: 1, dtype: int64

## Baseline

Even in deep learning, __feature representation is vital and requires care!__ For our task, feature representation has two parts: representing the individual words and combining those representations into a single network input.

### Representing words: vector_func

Let's consider two baseline word representations methods:

1. Random vectors (as returned by `utils.randvec`).
1. 50-dimensional GloVe representations.

```python
def randvec(w, n=50, lower=-1.0, upper=1.0):
    """Returns a random vector of length `n`. `w` is ignored."""
    return utils.randvec(n=n, lower=lower, upper=upper)
```

```python
# Any of the files in glove.6B will work here:

glove_dim = 50

glove_src = os.path.join(GLOVE_HOME, 'glove.6B.{}d.txt'.format(glove_dim))

# Creates a dict mapping strings (words) to GloVe vectors:
GLOVE = utils.glove2dict(glove_src)

def glove_vec(w):
    """Return `w`'s GloVe representation if available, else return
    a random vector."""
    return GLOVE.get(w, randvec(w, n=glove_dim))
```

### Combining words into inputs: vector_combo_func

Here we decide how to combine the two word vectors into a single representation. In more detail, where `u` is a vector representation of the left word and `v` is a vector representation of the right word, we need a function `vector_combo_func` such that `vector_combo_func(u, v)` returns a new input vector `z` of dimension `m`. A simple example is concatenation:

```python
def vec_concatenate(u, v):
    """Concatenate np.array instances `u` and `v` into a new np.array"""
    return np.concatenate((u, v))
```

`vector_combo_func` could instead be vector average, vector difference, etc. (even combinations of those) – there's lots of space for experimentation here; [homework question 2](#Alternatives-to-concatenation-[1-point]) below pushes you to do some exploration.

### Classifier model

For a baseline model, I chose `TorchShallowNeuralClassifier`:

```python
net = TorchShallowNeuralClassifier(hidden_dim=50, max_iter=100)
```

### Baseline results

The following puts the above pieces together, using `vector_func=glove_vec`, since `vector_func=randvec` seems so hopelessly misguided for `word_disjoint`!

```python
word_disjoint_experiment = nli.wordentail_experiment(
    train_data=wordentail_data['word_disjoint']['train'],
    assess_data=wordentail_data['word_disjoint']['dev'],
    model=net,
    vector_func=glove_vec,
    vector_combo_func=vec_concatenate)
```

    Finished epoch 100 of 100; error is 0.02542850561439991

                  precision    recall  f1-score   support

               0      0.924     0.938     0.931      1910
               1      0.435     0.381     0.406       239

        accuracy                          0.876      2149
       macro avg      0.680     0.659     0.669      2149
    weighted avg      0.869     0.876     0.873      2149

## Homework questions

Please embed your homework responses in this notebook, and do not delete any cells from the notebook. (You are free to add as many cells as you like as part of your responses.)

### Hypothesis-only baseline [2 points]

During our discussion of SNLI and MultiNLI, we noted that a number of research teams have shown that hypothesis-only baselines for NLI tasks can be remarkably robust. This question asks you to explore briefly how this baseline effects the 'edge_disjoint' and 'word_disjoint' versions of our task.

For this problem, submit two functions:

1. A `vector_combo_func` function called `hypothesis_only` that simply throws away the premise, using the unmodified hypothesis (second) vector as its representation of the example.

1. A function called `run_hypothesis_only_evaluation` that does the following:
    1. Loops over the two conditions 'word_disjoint' and 'edge_disjoint' and the two `vector_combo_func` values `vec_concatenate` and `hypothesis_only`, calling `nli.wordentail_experiment` to train on the conditions 'train' portion and assess on its 'dev' portion, with `glove_vec` as the `vector_func`. So that the results are consistent, use an `sklearn.linear_model.LogisticRegression` with default parameters as the model.
    1. Returns a `dict` mapping `(condition_name, function_name)` pairs to the 'macro-F1' score for that pair, as returned by the call to `nli.wordentail_experiment`. (Tip: you can get the `str` name of your function `hypothesis_only` with `hypothesis_only.__name__`.)

The test functions `test_hypothesis_only` and `test_run_hypothesis_only_evaluation` will help ensure that your functions have the desired logic.

```python
##### YOUR CODE HERE
def hypothesis_only(u, v):
    return v

    ##### YOUR CODE HERE

def run_hypothesis_only_evaluation():
    ##### YOUR CODE HERE
    from sklearn.linear_model import LogisticRegression
    ret = {}
    for key, condition in wordentail_data.items():
        if key == 'vocab':
            continue
        for v_combo_func in [vec_concatenate, hypothesis_only]:
            ret[(key,v_combo_func.__name__)] = nli.wordentail_experiment(
                                                    train_data=condition['train'],
                                                    assess_data=condition['dev'],
                                                    model=LogisticRegression(),
                                                    vector_func=glove_vec,
                                                    vector_combo_func=v_combo_func
                                                )['macro-F1']
    return ret

```

```python
def test_hypothesis_only(hypothesis_only):
    v = hypothesis_only(1, 2)
    assert v == 2
```

```python
test_hypothesis_only(hypothesis_only)
```

```python
def test_run_hypothesis_only_evaluation(run_hypothesis_only_evaluation):
    results = run_hypothesis_only_evaluation()
    assert ('word_disjoint', 'vec_concatenate') in results, \
        "The return value of `run_hypothesis_only_evaluation` does not have the intended kind of keys"
    assert isinstance(results[('word_disjoint', 'vec_concatenate')], float), \
        "The values of the `run_hypothesis_only_evaluation` result should be floats"
```

```python
test_run_hypothesis_only_evaluation(run_hypothesis_only_evaluation)
```

    /Users/christopherleung/anaconda2/envs/nlu/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
      FutureWarning)

                  precision    recall  f1-score   support

               0      0.875     0.969     0.920      7376
               1      0.572     0.229     0.327      1321

        accuracy                          0.857      8697
       macro avg      0.724     0.599     0.623      8697
    weighted avg      0.829     0.857     0.830      8697

    /Users/christopherleung/anaconda2/envs/nlu/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
      FutureWarning)

                  precision    recall  f1-score   support

               0      0.871     0.976     0.921      7376
               1      0.590     0.196     0.294      1321

        accuracy                          0.857      8697
       macro avg      0.731     0.586     0.607      8697
    weighted avg      0.829     0.857     0.825      8697

    /Users/christopherleung/anaconda2/envs/nlu/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
      FutureWarning)

                  precision    recall  f1-score   support

               0      0.901     0.981     0.939      1910
               1      0.471     0.138     0.214       239

        accuracy                          0.887      2149
       macro avg      0.686     0.559     0.576      2149
    weighted avg      0.853     0.887     0.858      2149

                  precision    recall  f1-score   support

               0      0.892     0.988     0.938      1910
               1      0.312     0.042     0.074       239

        accuracy                          0.883      2149
       macro avg      0.602     0.515     0.506      2149
    weighted avg      0.827     0.883     0.842      2149

    /Users/christopherleung/anaconda2/envs/nlu/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
      FutureWarning)

### Alternatives to concatenation [2 points]

We've so far just used vector concatenation to represent the premise and hypothesis words. This question asks you to explore two simple alternative:

1. Write a function `vec_diff` that, for a given pair of vector inputs `u` and `v`, returns the element-wise difference between `u` and `v`.

1. Write a function `vec_max` that, for a given pair of vector inputs `u` and `v`, returns the element-wise max values between `u` and `v`.

You needn't include your uses of `nli.wordentail_experiment` with these functions, but we assume you'll be curious to see how they do!

```python
def vec_diff(u, v):
    ##### YOUR CODE HERE
    return u-v

def vec_max(u, v):
    ##### YOUR CODE HERE
    return np.maximum(v, u)

```

```python
def test_vec_diff(vec_diff):
    u = np.array([10.2, 8.1])
    v = np.array([1.2, -7.1])
    result = vec_diff(u, v)
    expected = np.array([9.0, 15.2])
    assert np.array_equal(result, expected), \
        "Expected {}; got {}".format(expected, result)
```

```python
test_vec_diff(vec_diff)
```

```python
def test_vec_max(vec_max):
    u = np.array([1.2,  8.1])
    v = np.array([10.2, -7.1])
    result = vec_max(u, v)
    expected = np.array([10.2, 8.1])
    assert np.array_equal(result, expected), \
        "Expected {}; got {}".format(expected, result)
```

```python
test_vec_max(vec_max)
```

### A deeper network [2 points]

It is very easy to subclass `TorchShallowNeuralClassifier` if all you want to do is change the network graph: all you have to do is write a new `define_graph`. If your graph has new arguments that the user might want to set, then you should also redefine `__init__` so that these values are accepted and set as attributes.

For this question, please subclass `TorchShallowNeuralClassifier` so that it defines the following graph:

$$\begin{align}
h_{1} &= xW_{1} + b_{1} \\
r_{1} &= \textbf{Bernoulli}(1 - \textbf{dropout\_prob}, n) \\
d_{1} &= r_1 * h_{1} \\
h_{2} &= f(d_{1}) \\
h_{3} &= h_{2}W_{2} + b_{2}
\end{align}$$

Here, $r_{1}$ and $d_{1}$ define a dropout layer: $r_{1}$ is a random binary vector of dimension $n$, where the probability of a value being $1$ is given by $1 - \textbf{dropout_prob}$. $r_{1}$ is multiplied element-wise by our first hidden representation, thereby zeroing out some of the values. The result is fed to the user's activation function $f$, and the result of that is fed through another linear layer to produce $h_{3}$. (Inside `TorchShallowNeuralClassifier`, $h_{3}$ is the basis for a softmax classifier, so no activation function is applied to it.)

For your implementation, please use `nn.Sequential`, `nn.Linear`, and `nn.Dropout` to define the required layers.

For comparison, using this notation, `TorchShallowNeuralClassifier` defines the following graph:

$$\begin{align}
h_{1} &= xW_{1} + b_{1} \\
h_{2} &= f(h_{1}) \\
h_{3} &= h_{2}W_{2} + b_{2}
\end{align}$$

The following code starts this sub-class for you, so that you can concentrate on `define_graph`. Be sure to make use of `self.dropout_prob`

For this problem, submit just your completed  `TorchDeepNeuralClassifier`. You needn't evaluate it, though we assume you will be keen to do that!

You can use `test_TorchDeepNeuralClassifier` to ensure that your network has the intended structure.

```python
import torch.nn as nn

class TorchDeepNeuralClassifier(TorchShallowNeuralClassifier):
    def __init__(self, dropout_prob=0.7, **kwargs):
        self.dropout_prob = dropout_prob
        super().__init__(**kwargs)

    def define_graph(self):
        """Complete this method!

        Returns
        -------
        an `nn.Module` instance, which can be a free-standing class you
        write yourself, as in `torch_rnn_classifier`, or the outpiut of
        `nn.Sequential`, as in `torch_shallow_neural_classifier`.

        """
        ##### YOUR CODE HERE
        return nn.Sequential(
            nn.Linear(self.input_dim, self.hidden_dim),
            nn.Dropout(self.dropout_prob),
            self.hidden_activation,
            nn.Linear(self.hidden_dim, self.n_classes_))

##### YOUR CODE HERE
network_dropout_experiment = nli.wordentail_experiment(
    train_data=wordentail_data['word_disjoint']['train'],
    assess_data=wordentail_data['word_disjoint']['dev'],
    model=TorchDeepNeuralClassifier(),
    vector_func=glove_vec,
    vector_combo_func=vec_concatenate)

```

    Finished epoch 100 of 100; error is 2.4482920467853546

                  precision    recall  f1-score   support

               0      0.902     0.992     0.945      1910
               1      0.688     0.138     0.230       239

        accuracy                          0.897      2149
       macro avg      0.795     0.565     0.587      2149
    weighted avg      0.878     0.897     0.865      2149

```python
def test_TorchDeepNeuralClassifier(TorchDeepNeuralClassifier):
    dropout_prob = 0.55
    assert hasattr(TorchDeepNeuralClassifier(), "dropout_prob"), \
        "TorchDeepNeuralClassifier must have an attribute `dropout_prob`."
    try:
        inst = TorchDeepNeuralClassifier(dropout_prob=dropout_prob)
    except TypeError:
        raise TypeError("TorchDeepNeuralClassifier must allow the user "
                        "to set `dropout_prob` on initialization")
    inst.input_dim = 10
    inst.n_classes_ = 5
    graph = inst.define_graph()
    assert len(graph) == 4, \
        "The graph should have 4 layers; yours has {}".format(len(graph))
    expected = {
        0: 'Linear',
        1: 'Dropout',
        2: 'Tanh',
        3: 'Linear'}
    for i, label in expected.items():
        name = graph[i].__class__.__name__
        assert label in name, \
            "The {} layer of the graph should be a {} layer; yours is {}".format(i, label, name)
    assert graph[1].p == dropout_prob, \
        "The user's value for `dropout_prob` should be the value of `p` for the Dropout layer."
```

```python
test_TorchDeepNeuralClassifier(TorchDeepNeuralClassifier)
```

### Your original system [3 points]

This is a simple dataset, but our focus on the 'word_disjoint' condition ensures that it's a challenging one, and there are lots of modeling strategies one might adopt.

You are free to do whatever you like. We require only that your system differ in some way from those defined in the preceding questions. They don't have to be completely different, though. For example, you might want to stick with the model but represent examples differently, or the reverse.

Keep in mind that, for the bake-off evaluation, the 'edge_disjoint' portions of the data are off limits. You can, though, train on the combination of the 'word_disjoint' 'train' and 'dev' portions. You are free to use different pretrained word vectors and the like. Please do not introduce additional entailment datasets into your training data, though.

Please embed your code in this notebook so that we can rerun it.

In the cell below, please provide a brief technical description of your original system, so that the teaching team can gain an understanding of what it does. This will help us to understand your code and analyze all the submissions to identify patterns and strategies.

```python
# Enter your system description in this cell.
# Please do not remove this comment.
'''
For input, the system concatenates the GLoVe vectors together along with vector difference, vector means,
and cosine similarity between the two vectors.
For the model, I used a deep NN with layer sizes = [150,130,100,70,30], 2500 max_iterations, 0.001 learning rate,
activation function as tanh, and softmax output.
'''
def vec_cos_sim(u, v):
    from scipy.spatial.distance import cosine
    return np.array([cosine(u,v)])

def vec_mean(u,v):
    return np.array([np.mean(u), np.mean(v)])

def vec_cat_all(u, v):
    vec = np.concatenate((u,v))
    for v_combo_func in [vec_concatenate, vec_diff, vec_cos_sim, vec_mean]:
        vec = np.concatenate((vec, v_combo_func(u,v)))
    return vec

class TorchDeeperNeuralClassifier(TorchShallowNeuralClassifier):
    def __init__(self, dropout_prob=0.7, hidden_dims=[150,130,100,70,30], **kwargs):
        self.dropout_prob = dropout_prob
        self.hidden_dims = hidden_dims
        super().__init__(**kwargs)

    def define_graph(self):
        """Complete this method!

        Returns
        -------
        an `nn.Module` instance, which can be a free-standing class you
        write yourself, as in `torch_rnn_classifier`, or the outpiut of
        `nn.Sequential`, as in `torch_shallow_neural_classifier`.

        """
        ##### YOUR CODE HERE
        from collections import OrderedDict
        module_list = []
        module_list.append(('input',nn.Linear(self.input_dim, self.hidden_dims[0])))
        module_list.append(('dropout1',nn.Dropout(self.dropout_prob)))
        module_list.append(('hidden1',self.hidden_activation))

        for idx, hidden_dim in enumerate(self.hidden_dims):
            if idx == len(self.hidden_dims)-1:
                continue
            module_list.append(('linear'+str(idx+2),nn.Linear(self.hidden_dims[idx], self.hidden_dims[idx+1])))
            module_list.append(('dropout'+str(idx+2),nn.Dropout(self.dropout_prob)))
            module_list.append(('hidden'+str(idx+2),self.hidden_activation))
        module_list.append(('output', nn.Linear(self.hidden_dims[-1], self.n_classes_)))

        return nn.Sequential(OrderedDict(module_list))

def test_model(model, v_combo_func):
    return nli.wordentail_experiment(
        train_data=wordentail_data['word_disjoint']['train'] + wordentail_data['word_disjoint']['dev'],
        assess_data=wordentail_data['edge_disjoint']['dev'],
        model=model,
        vector_func=glove_vec,
        vector_combo_func=v_combo_func)

model = TorchDeeperNeuralClassifier(dropout_prob=0.5, hidden_dims=[150,130,100,70,30], max_iter=2500, eta=0.001)
v_combo_func = vec_cat_all

test_ret = test_model(model, v_combo_func)
print(test_ret)
```

    Finished epoch 2500 of 2500; error is 0.47911667637526993

                  precision    recall  f1-score   support

               0      0.938     0.970     0.954      7376
               1      0.791     0.642     0.709      1321

        accuracy                          0.920      8697
       macro avg      0.865     0.806     0.831      8697
    weighted avg      0.916     0.920     0.916      8697

    {'model': TorchDeeperNeuralClassifier(
    	hidden_dim=50,
    	hidden_activation=Tanh(),
    	batch_size=1028,
    	max_iter=2500,
    	eta=0.001,
    	optimizer=<class 'torch.optim.adam.Adam'>,
    	l2_strength=0), 'train_data': [[['abode', 'house'], 1], [['abortion', 'aneurysm'], 0], [['abortion', 'atrophy'], 0], [['abortion', 'blindness'], 0], [['abortion', 'chancre'], 0], [['abortion', 'chorioretinitis'], 0], [['abortion', 'deafness'], 0], [['abortion', 'inflammation'], 0], [['abortion', 'lesion'], 0], [['abortion', 'neuritis'], 0], [['abortion', 'neurosyphilis'], 0], [['abortion', 'palsy'], 0], [['abortion', 'paresis'], 0], [['abortion', 'sterility'], 0], [['abortion', 'tumor'], 0], [['abortion', 'ulcer'], 0], [['abscess', 'alcohol'], 0], [['abscess', 'anesthesia'], 0], [['abscess', 'aneurysm'], 0], [['abscess', 'angina'], 0], [['abscess', 'anxiety'], 0], [['abscess', 'arteritis'], 0], [['abscess', 'bacteremia'], 0], [['abscess', 'bite'], 0], [['abscess', 'bronchitis'], 0], [['abscess', 'cervicitis'], 0], [['abscess', 'chill'], 0], [['abscess', 'coffee'], 0], [['abscess', 'constipation'], 0], [['abscess', 'contraction'], 0], [['abscess', 'diphtheria'], 0], [['abscess', 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'phenylbutazone'], 0], [['fever', 'irritation'], 0], [['deer', 'gorilla'], 0], [['sheep', 'complaint'], 0], [['spoon', 'axe'], 0], [['spoon', 'hammer'], 0], [['tanker', 'quartet'], 0], [['bomber', 'rudder'], 0], [['fighter', 'scooter'], 0], [['ruling', 'summons'], 0], [['flute', 'artefact'], 1], [['drug', 'anaesthetic'], 0], [['sweat', 'heat'], 0], [['hat', 'keyhole'], 0], [['tranquil', 'impatient'], 0], [['truck', 'employment'], 0], [['glycogenolysis', 'constriction'], 0], [['vertigo', 'scotoma'], 0], [['saw', 'sufferer'], 0], [['gorilla', 'anchor'], 0], [['diarrhea', 'ulcer'], 0], [['cannon', 'pageant'], 0], [['tumour', 'abscess'], 0], [['crow', 'pheasant'], 0], [['jet', 'car'], 0], [['jitteriness', 'insomnia'], 0], [['asthma', 'hypoprothrombinemia'], 0], [['blouse', 'ballot'], 0], [['leftist', 'ruling'], 0], [['bronchospasm', 'hypoprothrombinemia'], 0], [['tumour', 'illness'], 1], [['arthritis', 'parotitis'], 0], [['scarf', 'park'], 0], [['owl', 'talon'], 0], [['vagina', 'archegonium'], 0], [['nun', 'chastity'], 1], [['woman', 'hypogonadism'], 0], [['surplus', 'decline'], 0], [['depression', 'photophobia'], 0], [['garlic', 'leek'], 0], [['intoxication', 'fever'], 0], [['diet', 'banana'], 0], [['coat', 'satellite'], 0], [['food', 'allergy'], 0], [['ambassador', 'chairman'], 0], [['bus', 'car'], 0], [['preamble', 'section'], 1], [['gangrene', 'death'], 0], [['ferry', 'steel'], 0], [['dilatation', 'ingestion'], 0], [['colt', 'male'], 1], [['iridocyclitis', 'antigen'], 1], [['penicillin', 'streptomycin'], 0], [['tension', 'headache'], 0], [['chair', 'expectation'], 0], [['soprano', 'singer'], 1], [['dagger', 'shore'], 0], [['dress', 'fabric'], 0], [['mailbox', 'flag'], 1], [['arrhythmia', 'jitteriness'], 0], [['television', 'shovel'], 0], [['hyperglycaemia', 'diarrhoea'], 0], [['sweater', 'coordination'], 0], [['squirrel', 'rugby'], 0], [['empyema', 'prostatitis'], 0], [['cholangitis', 'dacryocystitis'], 0], [['rat', 'elephant'], 0], [['villa', 'fireplace'], 0], [['nausea', 'exercise'], 0], [['coconut', 'grape'], 0], [['tuna', 'catcher'], 0], [['furunculosis', 'disease'], 1], [['corkscrew', 'high-intensity'], 0], [['immunization', 'antigen'], 0], [['stereo', 'datum'], 0], [['coconut', 'cannibalism'], 0], [['storm', 'sandwich'], 0], [['table', 'shoe'], 0], [['uterus', 'vagina'], 0], [['carp', 'whale'], 0], [['cephalosporin', 'aminoglycosides'], 0], [['lime', 'pear'], 0], [['relaxing', 'working'], 0], [['impetigo', 'cold'], 0], [['bear', 'plasticine'], 0], [['scotoma', 'aphasia'], 0], [['period', 'coughing'], 0], [['weakness', 'depression'], 0], [['fly', 'airplane'], 0], [['goat', 'bull'], 0], [['lesion', 'std'], 0], [['cathedral', 'dividend'], 0], [['myocardium', 'cortex'], 0], [['halloween', 'candy'], 0], [['indigestion', 'abscess'], 0], [['pig', 'geyser'], 0], [['narcotic', 'dehydration'], 0], [['economy', 'environment'], 0], [['fever', 'smallpox'], 0], [['furunculosis', 'pneumonia'], 0], [['owl', 'axiom'], 0], [['penicillin', 'amoxicillin'], 0], [['restaurant', 'brick'], 0], [['codeine', 'warfarin'], 0], [['radio', 'good'], 1], [['bacterium', 'toxin'], 0], [['pneumonia', 'rubella'], 0], [['frigate', 'motorboat'], 0], [['knife', 'plier'], 0], [['penis', 'gametophyte'], 0], [['title', 'text'], 1], [['inflammation', 'convulsion'], 0], [['wrench', 'Baptist'], 0], [['nausea', 'stroke'], 0], [['urticaria', 'pruritus'], 0], [['intoxication', 'prodrome'], 0], [['sweater', 'discipline'], 0], [['saw', 'blade'], 0], [['aneurysm', 'migraine'], 0], [['halved', 'whole'], 0], [['hemiplegia', 'sore'], 0], [['mammal', 'gorilla'], 0], [['ruling', 'motion'], 1], [['caffeine', 'migraine'], 0], [['hammer', 'glycoproteins'], 0], [['hyperglycemia', 'relaxation'], 0], [['kill', 'sentence'], 0], [['relief', 'period'], 0], [['thief', 'housing'], 0], [['ulcer', 'chorioretinitis'], 0], [['bag', 'outside'], 0], [['excuse', 'explanation'], 1], [['encephalopathy', 'hypertension'], 0], [['androstenedione', 'pregnenolone'], 0], [['gun', 'adventure'], 0], [['hand', 'joint'], 1], [['cat', 'nonzero'], 0], [['shirt', 'good'], 1], [['insipidus', 'heat'], 0], [['cathedral', 'rescue'], 0], [['shirt', 'clause'], 0], [['axe', 'accession'], 0], [['cold', 'bacteremia'], 0], [['irreparable', 'fixed'], 0], [['pub', 'deposit'], 0], [['wardrobe', 'shelf'], 0], [['bag', 'siren'], 0], [['shirt', 'cuff'], 0], [['chloramphenicol', 'neomycin'], 0], [['ambulance', 'sauna'], 0], [['exhaustion', 'hypernatremia'], 0], [['penguin', 'gauntlet'], 0], [['science', 'concept'], 0], [['rabbit', 'superhero'], 0], [['searing', 'roasting'], 0], [['cellulitis', 'endocarditis'], 0], [['frigate', 'troupe'], 0], [['tumor', 'alcohol'], 0], [['axe', 'spear'], 0], [['fox', 'sub-committee'], 0], [['body', 'wrist'], 0], [['teachers', 'college'], 0], [['vomiting', 'chloride'], 0], [['boat', 'dinghy'], 0], [['photophobia', 'contraction'], 0], [['insanity', 'papillitis'], 0], [['cider', 'fluid'], 1], [['illness', 'dermatitis'], 0], [['hornet', 'opposite'], 0], [['celery', 'golfer'], 0], [['trumpet', 'metal'], 0], [['fever', 'tumour'], 0], [['nephrosis', 'pneumonia'], 0], [['psoriasis', 'scabies'], 0], [['mackerel', 'example'], 0], [['placenta', 'vessel'], 0], [['tissue', 'animal'], 0], [['shirt', 'pullover'], 0], [['information', 'corporation'], 0], [['alcohol', 'inflammation'], 0], [['blindness', 'ulcer'], 0], [['warplane', 'gunmen'], 0], [['coaching', 'chart'], 0], [['wasp', 'lance'], 0], [['cedar', 'grandmaster'], 0], [['cat', 'pig'], 0], [['radio', 'amplifier'], 0], [['stroke', 'anxiety'], 0], [['death', 'depression'], 0], [['fever', 'strain'], 0], [['streptococcus', 'infection'], 1], [['enolase', 'anhydrase'], 0], [['humanist', 'scholar'], 1], [['drown', 'swim'], 0], [['gametophyte', 'division'], 0], [['hatchet', 'spear'], 0], [['acne', 'ulcer'], 0], [['dresser', 'wardrobe'], 0], [['legality', 'element'], 0], [['ferry', 'kind'], 0], [['robe', 'dress'], 0], [['bookcase', 'garrison'], 0], [['glove', 'birch'], 0], [['lizard', 'chordate'], 1], [['sweating', 'odor'], 1], [['trumpet', 'cyst'], 0], [['abscess', 'tumor'], 0], [['phone', 'goody'], 0], [['government', 'minister'], 1], [['banana', 'hemorrhoid'], 0], [['vulture', 'pigeon'], 0], [['argument', 'willingness'], 0], [['lion', 'bull'], 0], [['bill', 'law'], 1], [['eagle', 'feather'], 0], [['sinusitis', 'aneurysm'], 0], [['cramp', 'fatigue'], 0], [['hatchet', 'cutlery'], 1], [['pesticide', 'cabbage'], 0], [['athlete', 'filly'], 0], [['war', 'fighting'], 1], [['giraffe', 'skin'], 0], [['codeine', 'alcohol'], 0], [['donkey', 'horse'], 0], [['truck', 'delinquency'], 0], [['vulture', 'feather'], 0], [['parched', 'thirstiness'], 1], [['motility', 'gene'], 0], [['injury', 'catarrh'], 0], [['stomach', 'ulcer'], 0], [['masculinization', 'hyperplasia'], 0], [['hotel', 'accommodation'], 1], [['snake', 'tail'], 0], [['convict', 'behave'], 0], [['sheep', 'weekday'], 0], [['pain', 'decongestant'], 0], [['nausea', 'polyuria'], 0], [['coconut', 'plum'], 0], [['cellulitis', 'peritonitis'], 0], [['ambulance', 'theology'], 0], [['scarf', 'ppb'], 0], [['gun', 'excellence'], 0], [['estradiol', 'androstenedione'], 0], [['staphylococci', 'virus'], 0], [['truck', 'deficit'], 0], [['robin', 'addiction'], 0], [['eagle', 'pigeon'], 0], [['injury', 'chill'], 0], [['train', 'rail'], 0], [['trim', 'shorten'], 0], [['hunger', 'catarrh'], 0], [['spear', 'instrument'], 1], [['flavonoid', 'body'], 0], [['stereo', 'artifact'], 1], [['bowl', 'bottom'], 0], [['privatisation', 'cull'], 0], [['vintage', 'beverage'], 1], [['biochemistry', 'science'], 1], [['fever', 'death'], 0], [['hematoma', 'intoxication'], 0], [['beaver', 'rat'], 0], [['inhalation', 'infection'], 0], [['reserve', 'liquidity'], 1], [['symptom', 'rhinorrhea'], 0], [['hat', 'bra'], 0], [['radiation', 'phenomenon'], 1], [['processing', 'complex'], 0], [['epilepsy', 'nausea'], 0], [['missile', 'weekend'], 0], [['spoon', 'rake'], 0], [['boy', 'male'], 1], [['willow', 'flower'], 0], [['pistol', 'priest'], 0], [['fencing', 'obstruction'], 1], [['hemoconcentration', 'constipation'], 0], [['parapet', 'wall'], 1], [['son', 'offspring'], 1], [['headache', 'dysmenorrhea'], 0], [['anxiety', 'hyperthyroidism'], 0], [['signal', 'walnut'], 0], [['woodpecker', 'vertebrate'], 1], [['inflammation', 'ankylosis'], 0], [['shareholder', 'broker'], 0], [['verapamil', 'bowel'], 0], [['penguin', 'flat'], 0], [['germ', 'streptococci'], 0], [['loan', 'mortgage'], 0], [['wrench', 'watermelon'], 0], [['pig', 'creature'], 1], [['coyote', 'dolphin'], 0], [['tiger', 'pusher'], 0], [['theatre', 'brook'], 0], [['grapefruit', 'section'], 0], [['car', 'front'], 0], [['polyuria', 'diuresis'], 1], [['pirate', 'criminal'], 1], [['toxicity', 'dryness'], 0], [['irritation', 'inflammation'], 0], [['fridge', 'elector'], 0], [['snake', 'plastic'], 0], [['sparrow', 'fatherhood'], 0], [['goose', 'isle'], 0], [['guitar', 'arrangement'], 0], [['horse', 'flesh'], 0], [['wrench', 'pliers'], 0], [['jar', 'plate'], 0], [['potato', 'carbohydrate'], 0], [['protection', 'prevention'], 1], [['polyuria', 'cathartic'], 0], [['gonorrhea', 'dermatitis'], 0], [['rake', 'steel'], 0], [['criticize', 'castigate'], 0], [['fighter', 'captain'], 0], [['delirium', 'symptom'], 0], [['violin', 'aggregate'], 0], [['bus', 'seatbelt'], 0], [['bowl', 'exterior'], 0], [['inflammation', 'sepsis'], 0], [['amoxicillin', 'carbenicillin'], 0], [['owl', 'plume'], 0], [['opium', 'hypothyroidism'], 0], [['paragraph', 'word'], 1], [['dryness', 'delirium'], 0], [['sweater', 'gene'], 0], [['pine', 'elocution'], 0], [['hotel', 'structure'], 1], [['weapon', 'rifle'], 0], [['vulture', 'owl'], 0], [['van', 'harassment'], 0], [['peach', 'pineapple'], 0], [['alligator', 'mouth'], 0], [['scarf', 'tower'], 0], [['analgesic', 'diet'], 0], [['pneumonia', 'cataract'], 0], [['animal', 'chick'], 0], [['yacht', 'truck'], 0], [['lime', 'fruit'], 1], [['fluid', 'diuretic'], 0], [['hyperglycemia', 'heart'], 0], [['ulcer', 'pain'], 0], [['herring', 'transfusion'], 0], [['glove', 'species'], 0], [['helicopter', 'motorcycle'], 0], [['estradiol', 'dihydrotestosterone'], 0], [['doctor', 'degree'], 1], [['bat', 'raccoon'], 0], [['pistol', 'sight'], 0], [['gastroenteritis', 'syphilis'], 0], [['frigate', 'raft'], 0], [['train', 'reference'], 0], [['erosion', 'tenderness'], 0], [['scarf', 'cloak'], 0], [['scooter', 'necessity'], 0], [['headache', 'sore'], 0], [['gorilla', 'pub'], 0], [['disease', 'conjunctivitis'], 0], [['cramp', 'death'], 0], [['infection', 'photophobia'], 0], [['neurasthenia', 'depression'], 0], [['cancer', 'acne'], 0], [['salmon', 'carp'], 0], [['goat', 'ear'], 0], [['glider', 'agent'], 0], [['villa', 'edifice'], 1], [['chairman', 'secretary'], 0], [['abscess', 'contraction'], 0], [['cauliflower', 'beet'], 0], [['initiative', 'programme'], 0], [['obstruction', 'verapamil'], 0], [['sleeplessness', 'fatigue'], 0], [['clotting', 'atherogenesis'], 0], [['brain', 'monocyte'], 0], [['psoriasis', 'infection'], 0], [['dog', 'cattle'], 0], [['sedation', 'hypotension'], 0], [['crow', 'swan'], 0], [['goat', 'preceptor'], 0], [['symptom', 'photophobia'], 0], [['match', 'round'], 1], [['salicylate', 'depression'], 0], [['campsite', 'discipline'], 0], [['acne', 'gastroenteritis'], 0], [['audible', 'listen'], 0], [['table', 'marble'], 0], [['vessel', 'placenta'], 0], [['compass', 'direction'], 1], [['lesion', 'arthritis'], 0], [['chancre', 'tumor'], 0], [['prodrome', 'sleep'], 0], [['fence', 'barrier'], 1], [['pseudonym', 'golf'], 0], [['paresis', 'tumor'], 0], [['bed', 'enrolment'], 0], [['surplus', 'flow'], 0], [['contraction', 'relaxation'], 0], [['radio', 'stove'], 0], [['exercise', 'hyperglycemia'], 0], [['streptococci', 'cellulitis'], 0], [['bull', 'ear'], 0], [['lingerie', 'remorse'], 0], [['sentence', 'extension'], 0], [['vest', 'object'], 1], [['magnitude', 'radius'], 0], [['broccoli', 'vegetable'], 1], [['dehydration', 'pain'], 0], [['aspirin', 'opiate'], 0], [['molecule', 'lipid'], 0], [['lysis', 'death'], 0], [['oven', 'fridge'], 0], [['irritability', 'hunger'], 0], [['contest', 'final'], 0], [['mountain', 'town'], 0], [['masturbation', 'steroid'], 0], [['ambulance', 'bus'], 0], [['icon', 'disease'], 0], [['gap', 'rivalry'], 0], [['lemon', 'gramme'], 0], [['surplus', 'remittance'], 0], [['spasm', 'symptom'], 1], [['dolphin', 'park'], 0], [['depression', 'salicylate'], 0], [['train', 'warplane'], 0], [['vertebrate', 'chariot'], 0], [['beetle', 'Latin'], 0], [['indigestion', 'symptom'], 1], [['verapamil', 'laxative'], 0], [['frog', 'headwaters'], 0], [['dysphagia', 'hyperglycaemia'], 0], [['match', 'fixture'], 0], [['body', 'clothes'], 0], [['glove', 'understanding'], 0], [['gorilla', 'creature'], 1], [['grapefruit', 'vitamin'], 0], [['penguin', 'aviator'], 0], [['bus', 'brake'], 0], [['train', 'headache'], 0], [['hemorrhage', 'tumor'], 0], [['parasite', 'thalassemia'], 0], [['argument', 'presence'], 0], [['immunization', 'antibody'], 0], [['constipation', 'arteritis'], 0], [['dish', 'container'], 1], [['acacia', 'crown'], 0], [['syphilis', 'noise'], 0], [['article', 'ruling'], 0], [['haematemesis', 'urticaria'], 0], [['pear', 'kind'], 0], [['lion', 'cat'], 0], [['flute', 'nickel'], 0], [['ferry', 'warming'], 0], [['whale', 'tic'], 0], [['infection', 'constipation'], 0], [['jar', 'bottle'], 0], [['hypovolemia', 'thirst'], 0], [['male', 'men'], 1], [['cottage', 'home'], 1], [['complex', 'office'], 0], [['stereo', 'plastic'], 0], [['miner', 'workman'], 1], [['library', 'restaurant'], 0], [['haemorrhage', 'congestion'], 0], [['helicopter', 'lettuce'], 0], [['screwdriver', 'jacket'], 0], [['curious', 'investigation'], 1], [['hypertension', 'hematoma'], 0], [['frigate', 'rudder'], 0], [['birch', 'bole'], 0], [['ciprofloxacin', 'gentamicin'], 0], [['thirty', 'integer'], 1], [['bowl', 'cycle'], 0], [['nematode', 'invertebrate'], 1], [['iron', 'achlorhydria'], 0], [['restlessness', 'pain'], 0], [['bleeding', 'leukemia'], 0], [['villa', 'house'], 1], [['poplar', 'masterclasses'], 0], [['dishwasher', 'resident'], 0], [['chancre', 'rash'], 0], [['constriction', 'vasodilatation'], 0], [['fork', 'field'], 0], [['ovum', 'archegonium'], 0], [['vomiting', 'hyperglycemia'], 0], [['chloramphenicol', 'probenecid'], 0], [['giraffe', 'dolphin'], 0], [['fungi', 'streptococci'], 0], [['ruling', 'parliamentary'], 0], [['washer', 'object'], 1], [['theology', 'phenomenon'], 0], [['discomfort', 'pain'], 0], [['cannon', 'wheel'], 0], [['goods', 'coal'], 1], [['saw', 'algae'], 0], [['meningitis', 'encephalopathy'], 0], [['cabbage', 'corn'], 0], [['ferry', 'cabin'], 0], [['bull', 'cave'], 0], [['raccoon', 'dog'], 0], [['castle', 'inside'], 0], [['shovel', 'twenty-two'], 0], [['hemorrhage', 'anxiety'], 0], [['vasoconstriction', 'dilation'], 0], [['bulldog', 'dog'], 1], [['ingestion', 'haemorrhage'], 0], [['earnings', 'surplus'], 0], [['alcohol', 'hypertension'], 0], [['bomb', 'kalashnikov'], 0], [['injunction', 'ruling'], 1], [['ruling', 'referendum'], 0], [['impetigo', 'peritonitis'], 0], [['strain', 'pain'], 0], [['mackerel', 'photography'], 0], [['hospital', 'warehouse'], 0], [['ampicillin', 'gentamicin'], 0], [['lesion', 'neuritis'], 0], [['strain', 'coffee'], 0], [['yacht', 'decade'], 0], [['prostaglandin', 'clotting'], 0], [['vomiting', 'insipidus'], 0], [['excitement', 'dilatation'], 0], [['cello', 'artefact'], 1], [['project', 'complex'], 1], [['oak', 'branch'], 0], [['beaver', 'tiger'], 0], [['donkey', 'wickedness'], 0], [['migraine', 'staple'], 0], [['ant', 'director'], 0], [['gangrene', 'tuberculosis'], 0], [['urethra', 'vagina'], 0], [['funding', 'management'], 1], [['std', 'cold'], 0], [['hypotension', 'cramp'], 0], [['winger', 'athlete'], 1], [['opiate', 'alkaloid'], 0], [['gain', 'performance'], 1], [['migraine', 'encephalopathy'], 0], [['strawberry', 'ingenuity'], 0], [['achlorhydria', 'tapeworm'], 0], [['illness', 'fever'], 0], [['station', 'complex'], 1], [['dog', 'pet'], 1], [['sun', 'tan'], 1], [['pain', 'diet'], 0], [['monosaccharide', 'galactose'], 0], [['knife', 'holiday'], 0], [['lime', 'justice'], 0], [['cough', 'distress'], 0], [['chocolate', 'fruit'], 1], [['pheasant', 'triangulation'], 0], [['ampicillin', 'rifampin'], 0], [['apricot', 'produce'], 1], [['squirrel', 'prediction'], 0], [['bottle', 'impact'], 0], [['depression', 'asthma'], 0], [['starving', 'hunger'], 1], [['cow', 'coyote'], 0], [['performance', 'recovery'], 0], [['deer', 'tail'], 0], [['gastroenteritis', 'disease'], 1], [['apple', 'vitamin'], 0], [['squirrel', 'accuracy'], 0], [['bacteriuria', 'dacryocystitis'], 0], [['corkscrew', 'object'], 1], [['blouse', 'good'], 1], [['nephrosis', 'syphilis'], 0], [['goat', 'coyote'], 0], [['study', 'learning'], 1], [['iron', 'tapeworm'], 0], [['dolphin', 'constituent'], 0], [['toxicity', 'tinnitus'], 0], [['paresis', 'paralysis'], 1], [['attraction', 'phenomenon'], 1], [['crab', 'invertebrate'], 1], [['fighter', 'shrub'], 0], [['distress', 'pain'], 1], [['blouse', 'silk'], 0], [['federation', 'bureau'], 1], [['veil', 'garment'], 1], [['division', 'antheridium'], 0], [['asthma', 'motel'], 0], [['impetigo', 'syphilis'], 0], [['rake', 'utensil'], 1], [['dress', 'fleece'], 0], [['pine', 'gaiety'], 0], [['alopecia', 'arthritis'], 0], [['raccoon', 'skunk'], 0], [['meningitis', 'nausea'], 0], [['palsy', 'ulcer'], 0], [['dismutase', 'glutathione'], 0], [['effusion', 'abscess'], 0], [['tanker', 'indoctrination'], 0], [['dryness', 'headache'], 0]], 'macro-F1': 0.8311351189207528, 'vector_func': <function glove_vec at 0x1298ef320>, 'vector_combo_func': <function vec_cat_all at 0x12eddfef0>}

## Bake-off [1 point]

The goal of the bake-off is to achieve the highest macro-average F1 score on __word_disjoint__, on a test set that we will make available at the start of the bake-off. The announcement will go out on the discussion forum. To enter, you'll be asked to run `nli.bake_off_evaluation` on the output of your chosen `nli.wordentail_experiment` run.

The cells below this one constitute your bake-off entry.

The rules described in the [Your original system](#Your-original-system-[3-points]) homework question are also in effect for the bake-off.

Systems that enter will receive the additional homework point, and systems that achieve the top score will receive an additional 0.5 points. We will test the top-performing systems ourselves, and only systems for which we can reproduce the reported results will win the extra 0.5 points.

Late entries will be accepted, but they cannot earn the extra 0.5 points. Similarly, you cannot win the bake-off unless your homework is submitted on time.

The announcement will include the details on where to submit your entry.

```python
# Enter your bake-off assessment code into this cell.
# Please do not remove this comment.
##### YOUR CODE HERE
test_data_filename = os.path.join(
    NLIDATA_HOME,
    "bakeoff-wordentail-data",
    "nli_wordentail_bakeoff_data-test.json")

nli.bake_off_evaluation(
    test_ret,
    test_data_filename)

```

                  precision    recall  f1-score   support

               0      0.881     0.922     0.901      1767
               1      0.621     0.507     0.558       446

        accuracy                          0.838      2213
       macro avg      0.751     0.714     0.730      2213
    weighted avg      0.829     0.838     0.832      2213

```python
# On an otherwise blank line in this cell, please enter
# your macro-avg f1 value as reported by the code above.
# Please enter only a number between 0 and 1 inclusive.
# Please do not remove this comment.

##### YOUR CODE HERE
macro_avg_f1=0.730

```