title
stringlengths 12
112
| published
stringlengths 19
23
| url
stringlengths 28
28
| video_id
stringlengths 11
11
| channel_id
stringclasses 5
values | id
stringlengths 16
31
| text
stringlengths 0
596
| start
float64 0
37.8k
| end
float64 2.18
37.8k
|
|---|---|---|---|---|---|---|---|---|
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1577.04
|
So this allows us to do everything or perform this operation in batches.
| 1,577.04 | 1,584.4 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1581.6799999999998
|
And then we can also specify our batch size.
| 1,581.68 | 1,587.92 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1584.3999999999999
|
So batch size equals let's say 32.
| 1,584.4 | 1,591.6 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1587.9199999999998
|
So now when we run this where is it where is it going?
| 1,587.92 | 1,601.84 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1591.6
|
It's here. Now when we run this the map function here is going to tokenize our question and context in batches of 32.
| 1,591.6 | 1,603.76 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1601.84
|
So let's go ahead and do that.
| 1,601.84 | 1,608.08 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1605.36
|
Okay and then you can you can see that processing there.
| 1,605.36 | 1,613.04 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1608.8
|
So I mean that's that's all we really need to do with that.
| 1,608.8 | 1,617.12 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1613.04
|
So I think that's probably it for the map method.
| 1,613.04 | 1,627.36 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1617.12
|
And we'll well I'll fast forward and we'll continue with I think a few of the methods I think are quite useful as well.
| 1,617.12 | 1,631.68 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1628.3999999999999
|
Okay so that's just finishing up now.
| 1,628.4 | 1,636.16 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1631.6799999999998
|
So we can go ahead and have a look at what we've actually produced.
| 1,631.68 | 1,643.6 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1636.1599999999999
|
So come to here and say dataset train.
| 1,636.16 | 1,647.84 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1643.6
|
So what do we have now?
| 1,643.6 | 1,651.44 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1647.84
|
We have answers like we did before but now we also have attention mask.
| 1,647.84 | 1,655.76 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1652.3999999999999
|
We have input ids and we also have token type ids.
| 1,652.4 | 1,661.84 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1655.76
|
Which are the three tensors that we usually output from the tokenizer when we do that.
| 1,655.76 | 1,664.32 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1662.56
|
So we now have those in there as well.
| 1,662.56 | 1,670.32 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1664.32
|
We can also have a look another thing as well we can now rather than looping through our dataset
| 1,664.32 | 1,675.2 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1670.32
|
because we're not using a we're not using streaming which is true we're using streaming equals false.
| 1,670.32 | 1,684.88 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1675.2
|
We can now do this and we can see okay we have attention mask and it's not going to show me everything because it's quite large.
| 1,675.2 | 1,688.56 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1684.8799999999999
|
So I'll just delete that but you can see that we have the attention mask in there.
| 1,684.88 | 1,699.36 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1690.8
|
So what I want to do is say I want to be quite pedantic and I don't like the fact that
| 1,690.8 | 1,706.8 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1699.36
|
there is the fact that we have one feature called title.
| 1,699.36 | 1,713.76 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1707.52
|
Maybe I want to say okay it should be topic because it's a topic of the context and the question.
| 1,707.52 | 1,721.04 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1713.76
|
If I want to be really pedantic and modify that I could say dataset train rename column.
| 1,713.76 | 1,727.28 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1722.1599999999999
|
And to be honest you can use it for this of course but you're probably not going to you're probably
| 1,722.16 | 1,733.44 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1727.28
|
going to use it more for when you need to rename a column to make sure it aligns to whatever the
| 1,727.28 | 1,738.08 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1734.72
|
expected inputs are for a transformer model for example.
| 1,734.72 | 1,743.2 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1738.8
|
So that's where you would use it but I'm just using this example.
| 1,738.8 | 1,753.92 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1743.2
|
So I'm going to rename the column title to topic and let's print out and dataset train again.
| 1,743.2 | 1,759.12 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1753.92
|
So down here we have title and the moment we're going to have topic.
| 1,753.92 | 1,762.32 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1760.96
|
Okay so now we have topic.
| 1,760.96 | 1,771.2 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1763.3600000000001
|
So just rename column like I said come useful not in this case but generally this is usually useful.
| 1,763.36 | 1,780.24 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1772.24
|
Now what I may want to do as well is remove certain records from this dataset.
| 1,772.24 | 1,786.8 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1780.24
|
So so far we've been printing out the here we have this which is now topic.
| 1,780.24 | 1,789.28 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1786.8
|
We have University of Notre Dame.
| 1,786.8 | 1,796 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1789.84
|
Maybe for whatever reason we don't want to include those topics so we can say
| 1,789.84 | 1,805.04 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1798.32
|
very similar to before we write dataset train equals dataset train again.
| 1,798.32 | 1,809.76 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1805.04
|
This time I'm going to filter so we're going to filter out records that we don't want.
| 1,805.04 | 1,816.4 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1809.76
|
And again it's very similar to the syntax we use for the map function which is the lambda.
| 1,809.76 | 1,822.8 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1817.2
|
And in here we just need to specify the condition for the samples that we do want to include
| 1,817.2 | 1,824.08 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1822.8
|
or we do want to keep.
| 1,822.8 | 1,832.4 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1824.8799999999999
|
And in this case we want to say okay wherever the topic is not equal to University
| 1,824.88 | 1,834.08 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1832.4
|
of Notre Dame.
| 1,832.4 | 1,844.24 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1839.2800000000002
|
Okay so we'll run this and we'll have a look at what we produce.
| 1,839.28 | 1,852.56 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1844.24
|
So dataset train so somehow like we have number of rows here which is just over 88,000.
| 1,844.24 | 1,856 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1853.52
|
And we should get a lower number now.
| 1,853.52 | 1,859.76 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1856.0
|
Now this will also go through so this remember we have shuffle.
| 1,856 | 1,863.28 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1859.76
|
Set to shuffle what I keep calling it shuffle.
| 1,859.76 | 1,868.56 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1863.28
|
We have streaming set to false this time.
| 1,863.28 | 1,873.36 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1869.2
|
So it's going to run through the whole dataset and perform this filtering operation.
| 1,869.2 | 1,876.56 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1874.96
|
Now whilst we're waiting for that.
| 1,874.96 | 1,882.72 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1877.92
|
Now I'll just fast forward again to when this finishes in a moment.
| 1,877.92 | 1,888.32 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1883.52
|
Okay so now we have this finished and we can now run this.
| 1,883.52 | 1,895.52 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1888.32
|
It's finished and we have before we had 88,000 rows now we have 87.3.
| 1,888.32 | 1,902.48 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1896.48
|
And we should see so let me take the dataset train
| 1,896.48 | 1,908.08 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1904.24
|
topic and I want to see let's say the first five of those.
| 1,904.24 | 1,916.32 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1909.84
|
Okay now they're all Beyonce rather than before where it was the University of Notre Dame.
| 1,909.84 | 1,927.84 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1916.32
|
So we have those and what we may want to do now is say for example we're performing inference
| 1,916.32 | 1,930.4 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1927.84
|
with Q&A with a transformer model.
| 1,927.84 | 1,934.96 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1931.52
|
We don't really need all of the features that we have here.
| 1,931.52 | 1,943.2 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1935.6799999999998
|
So we would only need the attention mask, the input ids and also the token type ids.
| 1,935.68 | 1,948.96 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1943.2
|
So what we can do now is we can remove some of those columns.
| 1,943.2 | 1,955.44 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1948.96
|
So we'll do dataset train as always dataset train again.
| 1,948.96 | 1,964.88 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1961.44
|
And we want to remove those columns so remove columns.
| 1,961.44 | 1,972.56 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1967.52
|
And we'll just remove so all of them other than the ones that we want.
| 1,967.52 | 1,983.84 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1972.56
|
So do answers context id question and topic.
| 1,972.56 | 1,991.52 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1988.96
|
Okay and then let's have a look at what we have left.
| 1,988.96 | 1,999.52 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1994.56
|
Okay and then that's it so we have those final features and these are the ones that we would
| 1,994.56 | 2,002.56 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t1999.52
|
input into a transform model for training.
| 1,999.52 | 2,006.16 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t2003.28
|
Now I mean there's nothing else I really want to cover.
| 2,003.28 | 2,012.4 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t2006.16
|
I think that is pretty much all you need to know on Iconface datasets to get started
| 2,006.16 | 2,019.04 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t2012.4
|
and start building pretty I think good input pipelines and using some of the
| 2,012.4 | 2,021.2 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t2020.08
|
datasets that are available.
| 2,020.08 | 2,023.36 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t2021.84
|
So we'll leave it there.
| 2,021.84 | 2,029.2 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t2024.16
|
Thank you very much for watching and I will see you again in the next one.
| 2,024.16 | 2,029.76 |
Build NLP Pipelines with HuggingFace Datasets
|
2021-09-23 13:30:07 UTC
|
https://youtu.be/r-zQQ16wTCA
|
r-zQQ16wTCA
|
UCv83tO5cePwHMt1952IVVHw
|
r-zQQ16wTCA-t2029.2
| 2,029.2 | 2,029.76 |
|
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t0.0
|
Hi, welcome to this video on sentiment analysis using the Flare library.
| 0 | 14.32 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t6.32
|
So Flare is an incredibly simple, easy to use library, which contains a load of pre-built models for NLP
| 6.32 | 18.48 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t14.32
|
that we can simply import and use to make predictions.
| 14.32 | 24.08 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t19.12
|
So it actually allows us to use some of the most powerful models out there as well.
| 19.12 | 31.12 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t24.08
|
So in this tutorial, we're going to be using the Distilbert model, which is based on a BERT, but it's a lot smaller,
| 24.08 | 34.96 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t31.119999999999997
|
but almost as powerful as BERT itself.
| 31.12 | 38.56 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t35.92
|
So we're going to go ahead and begin.
| 35.92 | 43.68 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t40.16
|
First, if you haven't already, you need to pip install Flare.
| 40.16 | 49.92 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t46.0
|
And alongside Flare, you are also going to need PyTorch.
| 46 | 57.12 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t49.92
|
If you haven't got PyTorch installed already, you'll need to head over to the PyTorch website.
| 49.92 | 61.92 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t58.0
|
And they give you instructions on exactly what you need to install.
| 58 | 67.68 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t62.72
|
So we come down to here and we can see, OK, for me, I have Windows.
| 62.72 | 73.52 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t67.68
|
I want to install using Conda, using Python and then CUDA.
| 67.68 | 78.48 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t73.52000000000001
|
So this is if you have a CUDA enabled GPU on your machine.
| 73.52 | 82.72 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t78.48
|
If you don't know what that means, you probably don't.
| 78.48 | 85.6 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t83.2
|
So in that case, just click none.
| 83.2 | 89.12 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t86.16
|
But for me, I have 10.2.
| 86.16 | 94.16 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t89.2
|
So all we need to do is copy the command underneath here.
| 89.2 | 100.24 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t94.96000000000001
|
And then we would run this in our Anaconda prompt.
| 94.96 | 106.4 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t101.36
|
I already have these installed, so I'm going to go ahead and actually begin coding.
| 101.36 | 110 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t106.4
|
So we're going to need to use Pandas and also Flare.
| 106.4 | 122.48 |
How-to do Sentiment Analysis with Flair in Python
|
2020-12-04 14:00:03 UTC
|
https://youtu.be/DFtP1THE8fE
|
DFtP1THE8fE
|
UCv83tO5cePwHMt1952IVVHw
|
DFtP1THE8fE-t115.76
|
So now we have imported Flare, we can actually import a sentiment model straight away.
| 115.76 | 129.52 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.