fix quotes (huggingface#15)

how-to-generate.md

@@ -113,15 +113,15 @@ its next word: \\(w_t = argmax_{w}P(w | w_{1:t-1})\\) at each timestep
 
 <img src="/blog/assets/02_how-to-generate/greedy_search.png" alt="greedy search" style="margin: auto; display: block;">
 
-Starting from the word \\(\text{"The"}\\), the algorithm greedily chooses
-the next word of highest probability \\(\text{"nice"}\\) and so on, so
+Starting from the word \\(\text{``The''}\\), the algorithm greedily chooses
+the next word of highest probability \\(\text{``nice''}\\) and so on, so
 that the final generated word sequence is
-\\(\text{"The", "nice", "woman"}\\) having an overall probability of
+\\(\text{``The'', ``nice'', ``woman''}\\) having an overall probability of
 \\(0.5 \times 0.4 = 0.2\\).
 
 In the following we will generate word sequences using GPT2 on the
 context
-\\((\text{"I", "enjoy", "walking", "with", "my", "cute", "dog"})\\). Let's
+\\((\text{``I'', ``enjoy'', ``walking'', ``with'', ``my'', ``cute'', ``dog''})\\). Let's
 see how greedy search can be used in `transformers`:
 
 </div>
@@ -165,10 +165,10 @@ The major drawback of greedy search though is that it misses high
 probability words hidden behind a low probability word as can be seen in
 our sketch above:
 
-The word \\(\text{"has"}\\) with its high conditional probability of
-\\(0.9\\) is hidden behind the word \\(\text{"dog"}\\), which has only the
+The word \\(\text{``has''}\\) with its high conditional probability of
+\\(0.9\\) is hidden behind the word \\(\text{``dog''}\\), which has only the
 second-highest conditional probability, so that greedy search misses the
-word sequence \\(\text{"The"}, \text{"dog"}, \text{"has"}\\).
+word sequence \\(\text{``The''}, \text{``dog''}, \text{``has''}\\).
 
 Thankfully, we have beam search to alleviate this problem\!
 
@@ -186,10 +186,10 @@ highest probability. Let's illustrate with `num_beams=2`:
 <img src="/blog/assets/02_how-to-generate/beam_search.png" alt="beam search" style="margin: auto; display: block;">
 
 At time step \\(1\\), besides the most likely hypothesis
-\\(\text{"The", "woman"}\\), beam search also keeps track of the second
-most likely one \\(\text{"The", "dog"}\\). At time step \\(2\\), beam search
-finds that the word sequence \\(\text{"The", "dog", "has"}\\) has with
-\\(0.36\\) a higher probability than \\(\text{"The", "nice", "woman"}\\),
+\\(\text{``The'', ``woman''}\\), beam search also keeps track of the second
+most likely one \\(\text{``The'', ``dog''}\\). At time step \\(2\\), beam search
+finds that the word sequence \\(\text{``The'', ``dog'', ``has''}\\) has with
+\\(0.36\\) a higher probability than \\(\text{``The'', ``nice'', ``woman''}\\),
 which has \\(0.2\\). Great, it has found the most likely word sequence in
 our toy example\!
 
@@ -389,10 +389,10 @@ generation when sampling.
 <img src="/blog/assets/02_how-to-generate/sampling_search.png" alt="sampling search" style="margin: auto; display: block;">
 
 It becomes obvious that language generation using sampling is not
-*deterministic* anymore. The word \\(\text{"car"}\\) is sampled from the
-conditioned probability distribution \\(P(w | \text{"The"})\\), followed
-by sampling \\(\text{"drives"}\\) from
-\\(P(w | \text{"The"}, \text{"car"})\\).
+*deterministic* anymore. The word \\(\text{``car''}\\) is sampled from the
+conditioned probability distribution \\(P(w | \text{``The''})\\), followed
+by sampling \\(\text{``drives''}\\) from
+\\(P(w | \text{``The'', ``car''})\\).
 
 In `transformers`, we set `do_sample=True` and deactivate *Top-K*
 sampling (more on this later) via `top_k=0`. In the following, we will
@@ -454,7 +454,7 @@ look as follows.
 <img src="/blog/assets/02_how-to-generate/sampling_search_with_temp.png" alt="sampling temp search" style="margin: auto; display: block;">
 
 The conditional next word distribution of step \\(t=1\\) becomes much
-sharper leaving almost no chance for word \\(\text{"car"}\\) to be
+sharper leaving almost no chance for word \\(\text{``car''}\\) to be
 selected.
 
 Let's see how we can cool down the distribution in the library by
@@ -523,7 +523,7 @@ to 6 words. While the 6 most likely words, defined as
 probability mass in the first step, it includes almost all of the
 probability mass in the second step. Nevertheless, we see that it
 successfully eliminates the rather weird candidates
-\\(\text{"not", "the", "small", "told"}\\) in the second sampling step.
+\\(\text{``not'', ``the'', ``small'', ``told''}\\) in the second sampling step.
 
 Let's see how *Top-K* can be used in the library by setting `top_k=50`:
 
@@ -573,9 +573,9 @@ others from a much more flat distribution (distribution on the left in
 the graph above).
 
 In step \\(t=1\\), *Top-K* eliminates the possibility to sample
-\\(\text{"people", "big", "house", "cat"}\\), which seem like reasonable
+\\(\text{``people'', ``big'', ``house'', ``cat''}\\), which seem like reasonable
 candidates. On the other hand, in step \\(t=2\\) the method includes the
-arguably ill-fitted words \\(\text{"down", "a"}\\) in the sample pool of
+arguably ill-fitted words \\(\text{``down'', ``a''}\\) in the sample pool of
 words. Thus, limiting the sample pool to a fixed size *K* could endanger
 the model to produce gibberish for sharp distributions and limit the
 model's creativity for flat distribution. This intuition led [Ari
@@ -604,9 +604,9 @@ words to exceed together \\(p=92\%\\) of the probability mass, defined as
 likely words, whereas it only has to pick the top 3 words in the second
 example to exceed 92%. Quite simple actually\! It can be seen that it
 keeps a wide range of words where the next word is arguably less
-predictable, *e.g.* \\(P(w | \text{"The"})\\), and only a few words when
+predictable, *e.g.* \\(P(w | \text{``The''})\\), and only a few words when
 the next word seems more predictable, *e.g.*
-\\(P(w | \text{"The", "car"})\\).
+\\(P(w | \text{``The'', ``car''})\\).
 
 Alright, time to check it out in `transformers`\! We activate *Top-p*
 sampling by setting `0 < top_p < 1`: