Added an Example for BLEU.

[Neeshamraghav012]

I would like to inform you that I have made some updates to the content. Firstly, I have added an example for BLEU which was not present before. This new example serves as an illustration of the concept and should be helpful for those who are new to this topic.

Furthermore, I have also fixed the URLs by wrapping them in markdown. This should make it easier for readers to access the resources mentioned in the content.

[Neeshamraghav012]

@abheesht17 I have deleted the previous pull request because I messed up with it. Please review this one.

[abheesht17]

Left a few comments. Most of them our minor, but there is one major comment regarding rank of inputs!

[abheesht17]

Please remove extra spaces.

[abheesht17]

Since we mention that this example is with Python string inputs, this should ideally be

bleu([ref_sentence], pred_sentence)

[abheesht17]

Instead of assigning the score to a variable, let's just have

>>> bleu(ref_sentence, pred_sentence)
<tf.Tensor(0.7420885, shape=(), dtype=float32)>

Please do the same in other places as well.

[abheesht17]

Although the above example works, the ideal input formula is:

rank(ref_sentence) = rank(pred_sentence) + 1

I think we should probably stick to this formula in our examples. Otherwise, it becomes confusing for the reader. Could you please make this change everywhere?

[Neeshamraghav012]

Although the above example works, the ideal input formula is:

rank(ref_sentence) = rank(pred_sentence) + 1

I think we should probably stick to this formula in our examples. Otherwise, it becomes confusing for the reader. Could you please make this change everywhere?

Correct me if I am wrong, for this, do I need to change blue(ref_sentence, pred_sentence) to bleu([ref_sentence], pred_sentence)?

[abheesht17]

Since we mention that we are using ragged tensors here, it would be great if we could actually have a tensor with variable dimension along an axis. Also, I think your example will throw an error; pred_sentence should be a dense tensor here, not a ragged tensor.

So, something like:

>>> bleu = keras_nlp.metrics.Bleu(max_order=4)
>>> ref_sentence = tf.ragged.constant([
...         ["the quick brown fox jumps over the lazy dog"],
...         ["the quick brown fox jumps over the lazy dog", "the quick brown fox jumps over the lazy frog"]
...     ])
>>> pred_sentence = tf.constant([
...         ["the quick brown fox jumps over the box"],
...         ["the quick brown fox jumps over the box"]
...     ])
>>> bleu(ref_sentence, pred_sentence)
<tf.Tensor: shape=(), dtype=float32, numpy=0.7420885>

[abheesht17]

We should change these to

rank 1 prediction inputs, rank 2 reference inputs

or something like that?

[Neeshamraghav012]

I checked the Rouge L metric documentation, where this same convention is used. Is it okay to make these changes?

[abheesht17]

@mattdangerw - for context, this is the previous PR: #799

[Neeshamraghav012]

Hi @abheesht17 and @mattdangerw, I made the minor changes as you suggested. Thank you for your feedback!

However, I am not sure about the part where you mentioned changing the rank of inputs. Could you please provide more details or clarify what you meant by that?