Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

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"Are we done now? Is this good enough?"Sorry, but no, not yet. Our solution still has one problem, and it boils down tocomputing distances between two encoded positions. Let’s take position numberthree and its two neighbors, positions number two and four. Obviously, thedistance between position three and each of its closest neighbors is one. Now, let’ssee what happens if we compute distance using the positional encoding.Figure 9.42 - Inconsistent distancesThe distance between positions three and two (given by the norm of the differencevector) is not equal to the distance between positions three and four. That mayseem a bit too abstract, but using an encoding with inconsistent distances wouldmake it much harder for the model to make sense out of the encoded positions.This inconsistency arises from the fact that our encoding is resetting to zero everytime the module kicks in. The distance between positions three and four got muchlarger because, at position four, the first vector goes back to zero. We need someother function that has a smoother cycle."What if we actually use a cycle, I mean, a circle?"Perfect! First, we take our encodings and multiply them by 360.Figure 9.43 - From "normalized" module to degreesNow, each value corresponds to a number of degrees that we can use to movealong a circle. The figure below shows a red arrow rotated by the correspondingnumber of degrees for each position and base in the table above.Positional Encoding (PE) | 767
Figure 9.44 - Representing degrees on a circleMoreover, the circles above show the sine and cosine values corresponding to thecoordinates of the tip of each red arrow (assuming a circle with a radius of one).The sine and cosine values, that is, the coordinates of the redarrow, are the actual positional encoding of a given position.We can simply read the sine and cosine values, from top to bottom, to build theencodings for each position.Figure 9.45 - Representing degrees using sine and cosineThere were three vectors, thus generating six coordinates or dimensions (threesines and three cosines).Next, let’s use these encodings to calculate the distances, which should beconsistent now!768 | Chapter 9 — Part II: Sequence-to-Sequence
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Deep Learning with PyTorchStep-by-S
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"What I cannot create, I do not und
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Train-Validation-Test Split. . . .
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Random Split. . . . . . . . . . . .
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The Precision Quirk . . . . . . . .
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A REAL Filter . . . . . . . . . . .
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Jupyter Notebook . . . . . . . . .
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Stacked RNN . . . . . . . . . . . .
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Attention Mechanism . . . . . . . .
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4. Positional Encoding. . . . . . .
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Model Configuration & Training . .
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AcknowledgementsFirst and foremost,
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Frequently Asked Questions (FAQ)Why
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There is yet another advantage of f
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• Classes and methods are written
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What’s Next?It’s time to set up
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After choosing a repository, it wil
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1. AnacondaIf you don’t have Anac
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3. PyTorchPyTorch is the coolest de
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(pytorchbook) C:\> conda install py
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(pytorchbook)$ pip install torchviz
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7. JupyterAfter cloning the reposit
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Part IFundamentals| 21
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notebook. If not, just click on Cha
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Also, let’s say that, on average,
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There is one exception to the "alwa
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Random Initialization1 # Step 0 - I
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Batch, Mini-batch, and Stochastic G
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Outputarray([[-2. , -1.94, -1.88, .
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one matrix for each data point, eac
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Sure, different values of b produce
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Output-3.044811379650508 -1.8337537
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each parameter using the chain rule
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What’s the impact of one update o
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gradients, we know we need to take
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Very High Learning RateWait, it may
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true_b = 1true_w = 2N = 100# Data G
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Let’s look at the cross-sections
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Zero Mean and Unit Standard Deviati
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Sure, in the real world, you’ll n
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computing the loss, as shown in the
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• visualizing the effects of usin
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If you’re using Jupyter’s defau
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Notebook Cell 1.1 - Splitting synth
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Step 2# Step 2 - Computing the loss
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Output[0.49671415] [-0.1382643][0.8
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Notebook Cell 1.2 - Implementing gr
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# Sanity Check: do we get the same
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Outputtensor(3.1416)tensor([1, 2, 3
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Outputtensor([[1., 2., 1.],[1., 1.,
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dummy_array = np.array([1, 2, 3])du
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n_cudas = torch.cuda.device_count()
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back_to_numpy = x_train_tensor.nump
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I am assuming you’d like to use y
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Outputtensor([0.1940], device='cuda
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print(error.requires_grad, yhat.req
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Output(tensor([0.], device='cuda:0'
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56 # need to tell it to let it go..
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computation.If you chose "Local Ins
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Figure 1.6 - Now parameter "b" does
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There are many optimizers: SGD is t
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41 optimizer.zero_grad() 34243 prin
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Notebook Cell 1.8 - PyTorch’s los
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Outputarray(0.00804466, dtype=float
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Let’s build a proper (yet simple)
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"What do we need this for?"It turns
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1 Instantiating a model2 What IS th
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In the __init__() method, we create
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LayersA Linear model can be seen as
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There are MANY different layers tha
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We use magic, just like that:%run -
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• Step 1: compute model’s predi
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RecapFirst of all, congratulations
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Chapter 2Rethinking the Training Lo
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Let’s take a look at the code onc
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Higher-Order FunctionsAlthough this
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def exponentiation_builder(exponent
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Apart from returning the loss value
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Our code should look like this; see
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There is no need to load the whole
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but if we want to get serious about
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How does this change our code so fa
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Run - Model Training V2%run -i mode
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piece of code that’s going to be
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for it. We could do the same for th
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EvaluationHow can we evaluate the m
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And then, we update our model confi
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Run - Model Training V4%run -i mode
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Loading Extension# Load the TensorB
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browser, you’ll likely see someth
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model’s graph (not quite the same
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Figure 2.5 - Scalars on TensorBoard
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Define - Model Training V51 %%write
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If, by any chance, you ended up wit
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The procedure is exactly the same,
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soon, so please bear with me for no
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After recovering our model’s stat
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Run - Model Configuration V31 # %lo
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This is the general structure you
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Chapter 2.1Going ClassySpoilersIn t
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# A completely empty (and useless)
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# These attributes are defined here
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# Creates the train_step function f
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# Builds function that performs a s
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setattrThe setattr function sets th
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See? We effectively modified the un
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the random seed as arguments.This s
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The current state of development of
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Lossesdef plot_losses(self):fig = p
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Run - Data Preparation V21 # %load
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Model TrainingWe start by instantia
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Making PredictionsLet’s make up s
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OutputOrderedDict([('0.weight', ten
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Run - Data Preparation V21 # %load
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• defining our StepByStep class
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import numpy as npimport torchimpor
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Next, we’ll standardize the featu
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Equation 3.1 - A linear regression
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The odds ratio is given by the rati
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As expected, probabilities that add
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Sigmoid Functiondef sigmoid(z):retu
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A picture is worth a thousand words
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OutputOrderedDict([('linear.weight'
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The first summation adds up the err
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IMPORTANT: Make sure to pass the pr
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To make it clear: In this chapter,
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argument of nn.BCEWithLogitsLoss().
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It is not that hard, to be honest.
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Figure 3.6 - Training and validatio
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Outputarray([[0.5504593 ],[0.949995
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decision boundary.Look at the expre
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Are my data points separable?That
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model = nn.Sequential()model.add_mo
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It looks like this:Figure 3.10 - Sp
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True and False Positives and Negati
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tpr_fpr(cm_thresh50)Output(0.909090
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The trade-off between precision and
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Figure 3.13 - Using a low threshold
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Figure 3.16 - Trade-offs for two di
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thresholds do not necessarily inclu
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actual data, it is as bad as it can
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If you want to learn more about bot
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Model Training1 n_epochs = 10023 sb
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step in your journey! What’s next
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Chapter 4Classifying ImagesSpoilers
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Data GenerationOur images are quite
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Images and ChannelsIn case you’re
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image_rgb = np.stack([image_r, imag
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That’s fairly straightforward; we
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• Transformations based on Tensor
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position of an object in a picture
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Outputtensor([[[0., 0., 0., 1., 0.]
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Outputtensor([[[-1., -1., -1., 1.,
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We can convert the former into the
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composer = Compose([RandomHorizonta
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Output<torch.utils.data.dataset.Sub
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train_composer = Compose([RandomHor
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The minority class should have the
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train_loader = DataLoader(dataset=t
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implemented in Chapter 2.1? Let’s
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Let’s take one mini-batch of imag
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What does our model look like? Visu
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Model TrainingLet’s train our mod
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preceding hidden layer to compute i
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fig = sbs_nn.plot_losses()Figure 4.
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Equation 4.2 - Equivalence of deep
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w_nn_equiv = w_nn_output.mm(w_nn_hi
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Weights as PixelsDuring data prepar
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is only 0.25 (for z = 0) and that i
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nn.Tanh()(dummy_z)Outputtensor([-0.
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dummy_z = torch.tensor([-3., 0., 3.
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As you can see, in PyTorch the coef
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Figure 4.16 - Deep model (for real)
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Figure 4.18 - Losses (before and af
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Equation 4.3 - Activation functions
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Helper Function #41 def index_split
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Model Configuration1 # Sets learnin
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Bonus ChapterFeature SpaceThis chap
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Affine TransformationsAn affine tra
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Figure B.3 - Annotated model diagra
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Figure B.5 - In the beginning…But
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OK, now we can clearly see a differ
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In the model above, the sigmoid fun
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the more dimensions, the more separ
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import randomimport numpy as npfrom
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identity = np.array([[[[0, 0, 0],[0
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Figure 5.4 - Striding the image, on
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Output-----------------------------
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Outputtensor([[[[9., 5., 0., 7.],[0
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OutputParameter containing:tensor([
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Moreover, notice that if we were to
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In code, as usual, PyTorch gives us
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Outputtensor([[[[5., 5., 0., 8., 7.
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edge = np.array([[[[0, 1, 0],[1, -4
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A pooling kernel of two-by-two resu
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Outputtensor([[22., 23., 11., 24.,
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Figure 5.15 - LeNet-5 architectureS
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• second block: produces 16-chann
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Transformed Dataset1 class Transfor
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LossNew problem, new loss. Since we
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Outputtensor([4.0000, 1.0000, 0.500
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The loss only considers the predict
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Outputtensor([[-1.5229, -0.3146, -2
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IMPORTANT: I can’t stress this en
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figures at the beginning of this ch
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The three units in the output layer
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StepByStep Method@staticmethoddef _
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The meow() method is totally indepe
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StepByStep Methoddef visualize_filt
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dummy_model = nn.Linear(1, 1)dummy_
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dummy_listOutput[(Linear(in_feature
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Output{Conv2d(1, 1, kernel_size=(3,
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will be the externally defined vari
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Removing Hookssbs_cnn1.remove_hooks
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return figsetattr(StepByStep, 'visu
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Figure 5.22 - Feature maps (classif
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classification: The predicted class
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convolutional layers to our model a
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Capturing Outputsfeaturizer_layers
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the filters learned by the model pr
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given chapter are imported at its v
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Data PreparationThe data preparatio
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model anyway. We’ll use it to com
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StepByStep Method@staticmethoddef m
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"What’s wrong with the colors?"Th
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three_channel_filter = np.array([[[
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Fancier Model (Constructor)class CN
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Fancier Model (Classifier)def class
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torch.manual_seed(44)dropping_model
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Outputtensor([0.1000, 0.2000, 0.300
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Figure 6.8 - Output distribution fo
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Adaptive moment estimation (Adam) u
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torch.manual_seed(13)# Model Config
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Outputtorch.Size([5, 3, 3, 3])Its s
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Choosing a learning rate that works
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Higher-Order Learning Rate Function
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Perfect! Now let’s build the actu
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ax.set_xlabel('Learning Rate')ax.se
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LRFinderThe function we’ve implem
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value in our moving average has an
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Figure 6.15 - Distribution of weigh
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In code, the implementation of the
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As expected, the EWMA without corre
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optimizer = optim.Adam(model.parame
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IMPORTANT: The logging function mus
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Output{'state': {140601337662512: {
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different optimizer, set them to ca
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• dampening: dampening factor for
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Figure 6.20 - Paths taken by SGD (w
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Equation 6.16 - Looking aheadOnce N
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Figure 6.22 - Path taken by each SG
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for epoch in range(4):# training lo
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course) up to a given number of epo
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Next, we create a protected method
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Mini-Batch SchedulersThese schedule
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Schedulers in StepByStep — Part I
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Scheduler PathsBefore trying out a
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After applying each scheduler to SG
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Data Preparation1 # Loads temporary
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Figure 6.31 - LossesEvaluationprint
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[96] http://www.samkass.com/theorie
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ImportsFor the sake of organization
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ILSVRC-2012The 2012 edition [111] o
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remained unchanged.ResNet (MSRA Tea
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Transfer Learning in PracticeIn Cha
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dropout. You’re already familiar
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OutputDownloading: "https://downloa
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Replacing the "Top" of the Model1 a
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Model Size Classifier Layer(s) Repl
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Model TrainingWe have everything se
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"Removing" the Top Layer1 alex.clas
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torch.save(train_preproc.tensors, '
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Outputtensor([[109, 124],[124, 124]
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Model Configuration1 optimizer_mode
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Figure 7.4 - 1x1 convolutionThe inp
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The weights used by PIL are 0.299 f
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• reduce the number of output cha
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The constructor method defines the
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Does it sound familiar? That’s wh
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and w to represent these parameters
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A mini-batch of size 64 is small en
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normed1 = batch_normalizer(batch1[0
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OutputOrderedDict([('running_mean',
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OutputOrderedDict([('running_mean',
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batch_normalizer = nn.BatchNorm2d(n
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torch.manual_seed(23)dummy_points =
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np.concatenate([dummy_points[:5].nu
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Another advantage of these shortcut
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It should be pretty clear, except f
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Data Preparation1 # ImageNet statis
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Data Preparation — Preprocessing1
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• freezing the layers of the mode
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Extra ChapterVanishing and Explodin
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discussing it, let me illustrate it
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Model Configuration (2)1 loss_fn =
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weights. If done properly, the init
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just did), or, if you are training
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Figure E.3 - The effect of batch no
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Model Configuration1 torch.manual_s
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torch.manual_seed(42)parm = nn.Para
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(and only if) the norm exceeds the
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if callable(self.clipping): 1self.c
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Moreover, let’s use a ten times h
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Clipping with HooksFirst, we reset
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• visualizing the difference betw
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Chapter 8SequencesSpoilersIn this c
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Before shuffling, the pixels were o
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And then let’s visualize the firs
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sequence so far, and a data point f
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Considering this, the not "unrolled
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linear_input = nn.Linear(n_features
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Outputtensor([[0.3924, 0.8146]], gr
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Now we’re talking! The last hidde
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Let’s take a look at the RNN’s
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◦ The initial hidden state, which
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batch_first argument to True so we
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OutputOrderedDict([('weight_ih_l0',
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out, hidden = rnn_stacked(x)out, hi
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_l0_reverse).Once again, let’s cr
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For bidirectional RNNs, the last el
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Model Configuration1 class SquareMo
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StepByStep.loader_apply(test_loader
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Figure 8.14 - Final hidden states f
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Figure 8.16 - Transforming the hidd
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Since the RNN cell has both of them
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Every gate worthy of its name will
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• For r=0 and z=0, the cell becom
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In code, we can use split() to get
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Let’s pause for a moment here. Fi
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Square Model II — The QuickeningT
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Outputtensor([[53, 53],[75, 75]])Th
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Figure 8.22 - Transforming the hidd
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Equation 8.9 - LSTM—candidate hid
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Now, let’s visualize the internal
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OutputOrderedDict([('weight_ih', te
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def forget_gate(h, x):thf = f_hidde
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Outputtensor([[-5.4936e-02, -8.3816
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1 First change: from RNN to LSTM2 S
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Like the GRU, the LSTM presents fou
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Output-----------------------------
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Before moving on to packed sequence
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column-wise fashion, from top to bo
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does match the last output.• No,
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So, to actually get the last output
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Data Preparation1 class CustomDatas
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OutputPackedSequence(data=tensor([[
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Model Configuration & TrainingWe ca
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size = 5weight = torch.ones(size) *
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torch.manual_seed(17)conv_seq = nn.
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Figure 8.32 - Applying dilated filt
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Model Configuration1 torch.manual_s
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We can actually find an expression
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Data Preparation1 def pack_collate(
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and variable-length sequences.Model
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• generating variable-length sequ
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import copyimport numpy as npimport
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Figure 9.3 - Sequence datasetThe co
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coordinates of a "perfect" square a
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Let’s pretend for a moment that t
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to initialize the hidden state and
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predictions in previous steps have
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the second set of predicted coordin
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Let’s create an instance of the m
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Model Configuration & TrainingThe m
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Sure, we can!AttentionHere is a (no
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based on "the" and "zone," I’ve j
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Figure 9.12 - Matching a query to t
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Outputtensor([[[ 0.0832, -0.0356],[
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utmost importance for the correct i
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Its formula is:Equation 9.3 - Cosin
Page 742 and 743: second hidden state contributes to
Page 744 and 745: Outputtensor([[[ 0.5475, 0.0875, -1
Page 746 and 747: alphas = F.softmax(scaled_products,
Page 748 and 749: Outputtensor([[[ 0.2138, -0.3175]]]
Page 750 and 751: Attention Mechanism1 class Attentio
Page 752 and 753: "Why would I want to force it to do
Page 754 and 755: 1 Sets attention module and adjusts
Page 756 and 757: encdec = EncoderDecoder(encoder, de
Page 758 and 759: fig = sbs_seq_attn.plot_losses()Fig
Page 760 and 761: Figure 9.20 - Attention scoresSee?
Page 762 and 763: Wide vs Narrow AttentionThis mechan
Page 764 and 765: "What’s so special about it?"Even
Page 766 and 767: Once again, the affine transformati
Page 768 and 769: Next, we shift our focus to the sel
Page 770 and 771: Encoder + Self-Attention1 class Enc
Page 772 and 773: Figure 9.27 - Encoder with self- an
Page 774 and 775: The figure below depicts the self-a
Page 776 and 777: shifted_seq = torch.cat([source_seq
Page 778 and 779: Equation 9.17 - Decoder’s (masked
Page 780 and 781: At evaluation / prediction time we
Page 782 and 783: Outputtensor([[[0.4132, 0.3728],[0.
Page 784 and 785: Figure 9.33 - Encoder + decoder + a
Page 786 and 787: 64 return outputsThe encoder-decode
Page 788 and 789: Figure 9.34 - Losses—encoder + de
Page 790 and 791: curse. On the one hand, it makes co
Page 794 and 795: Figure 9.46 - Consistent distancesA
Page 796 and 797: Let’s recap what we’ve already
Page 798 and 799: Let’s see it in code:max_len = 10
Page 800 and 801: Let’s put it all together into a
Page 802 and 803: Outputtensor([[[-1.0000, 0.0000],[-
Page 804 and 805: Decoder with Positional Encoding1 c
Page 806 and 807: Visualizing PredictionsLet’s plot
Page 808 and 809: Next, we’re moving on to the thre
Page 810 and 811: Data Generation & Preparation1 # Tr
Page 812 and 813: 59 self.trg_masks)60 else:61 # Deco
Page 814 and 815: Model Configuration1 class EncoderS
Page 816 and 817: 1617 @property18 def alphas(self):1
Page 818 and 819: Output(0.016193246061448008, 0.0341
Page 820 and 821: sequential order of the data• fig
Page 822 and 823: following imports:import copyimport
Page 824 and 825: Figure 10.2 - Chunking: the wrong a
Page 826 and 827: chunks to compute the other half of
Page 828 and 829: 67 # N, L, n_heads, d_k68 context =
Page 830 and 831: dummy_points = torch.randn(16, 2, 4
Page 832 and 833: Stacking Encoders and DecodersLet
Page 834 and 835: "… with great depth comes great c
Page 836 and 837: Transformer EncoderWe’ll be repre
Page 838 and 839: Let’s see it in code, starting wi
Page 840 and 841: Transformer Encoder1 class EncoderT
Page 842 and 843:
of the encoder-decoder (or Transfor
Page 844 and 845:
In PyTorch, the decoder "layer" is
Page 846 and 847:
In PyTorch, the decoder is implemen
Page 848 and 849:
Equation 10.7 - Data points' means
Page 850 and 851:
layer_norm = nn.LayerNorm(d_model)n
Page 852 and 853:
Figure 10.10 - Layer norm vs batch
Page 854 and 855:
Outputtensor([[[ 1.4636, 2.3663],[
Page 856 and 857:
The TransformerLet’s start with t
Page 858 and 859:
"values") in the decoder.• decode
Page 860 and 861:
Data Preparation1 # Generating trai
Page 862 and 863:
Figure 10.15 - Losses—Transformer
Page 864 and 865:
• First, and most important, PyTo
Page 866 and 867:
decode(), with a single one, encode
Page 868 and 869:
46 for i in range(self.target_len):
Page 870 and 871:
Figure 10.18 - Losses - PyTorch’s
Page 872 and 873:
Figure 10.20 - Sample image—label
Page 874 and 875:
4041 # Builds a weighted random sam
Page 876 and 877:
Figure 10.23 - Sample image—split
Page 878 and 879:
Einops"There is more than one way t
Page 880 and 881:
Figure 10.26 - Two patch embeddings
Page 882 and 883:
Now each sequence has ten elements,
Page 884 and 885:
It takes an instance of a Transform
Page 886 and 887:
Putting It All TogetherIn this chap
Page 888 and 889:
1. Encoder-DecoderThe encoder-decod
Page 890 and 891:
This is the actual encoder-decoder
Page 892 and 893:
3. DecoderThe Transformer decoder h
Page 894 and 895:
5. Encoder "Layer"The encoder "laye
Page 896 and 897:
7. "Sub-Layer" WrapperThe "sub-laye
Page 898 and 899:
8. Multi-Headed AttentionThe multi-
Page 900 and 901:
Model Configuration & TrainingModel
Page 902 and 903:
• training the Transformer to tac
Page 904 and 905:
Part IVNatural Language Processing|
Page 906 and 907:
Additional SetupThis is a special c
Page 908 and 909:
"Down the Yellow Brick Rabbit Hole"
Page 910 and 911:
The actual texts of the books are c
Page 912 and 913:
"What is this punkt?"That’s the P
Page 914 and 915:
14 # If there is a configuration fi
Page 916 and 917:
Sentence Tokenization in spaCyBy th
Page 918 and 919:
AttributesThe Dataset has many attr
Page 920 and 921:
Output{'labels': 1,'sentence': 'The
Page 922 and 923:
elements from the text. But preproc
Page 924 and 925:
Data AugmentationLet’s briefly ad
Page 926 and 927:
The corpora’s dictionary is not a
Page 928 and 929:
Finally, if we want to convert a li
Page 930 and 931:
Once we’re happy with the size an
Page 932 and 933:
from transformers import BertTokeni
Page 934 and 935:
"What about the separation token?"T
Page 936 and 937:
The last output, attention_mask, wo
Page 938 and 939:
Outputtensor([[ 3, 27, 1, ..., 0, 0
Page 940 and 941:
vector, right? And our vocabulary i
Page 942 and 943:
Maybe you filled this blank in with
Page 944 and 945:
Continuous Bag-of-Words (CBoW)In th
Page 946 and 947:
That’s a fairly simple model, rig
Page 948 and 949:
Figure 11.13 - Continuous bag-of-wo
Page 950 and 951:
Figure 11.15 - Reviewing restaurant
Page 952 and 953:
You got that right—arithmetic—r
Page 954 and 955:
There we go, 50 dimensions! It’s
Page 956 and 957:
Equation 11.1 - Embedding arithmeti
Page 958 and 959:
Only 82 out of 50,802 words in the
Page 960 and 961:
Now we can use its encode() method
Page 962 and 963:
Model I — GloVE + ClassifierData
Page 964 and 965:
Pre-trained PyTorch EmbeddingsThe e
Page 966 and 967:
Model Configuration & TrainingLet
Page 968 and 969:
6 self.encoder = encoder7 self.mlp
Page 970 and 971:
Figure 11.20 - Losses—Transformer
Page 972 and 973:
Outputtensor([[[2.6334e-01, 6.9912e
Page 974 and 975:
I want to introduce you to…ELMoBo
Page 976 and 977:
OutputToken: 32 watchThe get_token(
Page 978 and 979:
Helper Function to Retrieve Embeddi
Page 980 and 981:
Output(tensor(-0.5047, device='cuda
Page 982 and 983:
torch.all(new_flair_sentences[0].to
Page 984 and 985:
Outputtensor(0.3504, device='cuda:0
Page 986 and 987:
We can leverage this fact to slight
Page 988 and 989:
We can easily get the embeddings fo
Page 990 and 991:
Figure 11.24 - Losses—simple clas
Page 992 and 993:
We can inspect the pre-trained mode
Page 994 and 995:
Every word piece is prefixed with #
Page 996 and 997:
far, our models used these embeddin
Page 998 and 999:
position_ids = torch.arange(512).ex
Page 1000 and 1001:
Pre-training TasksMasked Language M
Page 1002 and 1003:
Then, let’s create an instance of
Page 1004 and 1005:
If these two sentences were the inp
Page 1006 and 1007:
The BERT model may take many other
Page 1008 and 1009:
The contextual word embeddings are
Page 1010 and 1011:
Model Configuration1 class BERTClas
Page 1012 and 1013:
"Which BERT is that? DistilBERT?!"D
Page 1014 and 1015:
Well, you probably don’t want to
Page 1016 and 1017:
set num_labels=1 as argument.If you
Page 1018 and 1019:
Output{'attention_mask': [1, 1, 1,
Page 1020 and 1021:
OutputTrainingArguments(output_dir=
Page 1022 and 1023:
Method for Computing Accuracy1 def
Page 1024 and 1025:
loaded_model = (AutoModelForSequenc
Page 1026 and 1027:
logits.logits.argmax(dim=1)Outputte
Page 1028 and 1029:
For a complete list of available ta
Page 1030 and 1031:
[215]. For a demo of GPT-2’s capa
Page 1032 and 1033:
in Chapter 9, and I reproduce it be
Page 1034 and 1035:
Data Preparation1 auto_tokenizer =
Page 1036 and 1037:
Data Preparation1 lm_train_dataset
Page 1038 and 1039:
The training arguments are roughly
Page 1040 and 1041:
device_index = (model.device.indexi
Page 1042 and 1043:
• learning that a language model
Page 1044 and 1045:
[167] https://huggingface.co/docs/d
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