<p>In this problem, you can also use NER models in order to tag those numbers as Width, Height,... . You can also fine-tune a DistilBert model for your task.</p>
<p>if you want to train NER model by tok2vec you can use:
<a href="https://spacy.io/usage/training" rel="nofollow noreferrer">Spacy Library</a></p>
<p>for fine-tuning DistilBERT you can use this.
<a href="https://huggingface.co/transformers/model_doc/distilbert.html" rel="nofollow noreferrer">Hugging Face</a></p>

CC BY-SA 4.0
2021-12-04T15:59:50.033Z
Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).
Community
CC BY-SA 4.0
2021-12-04T15:30:58.560Z
2021-12-04T18:17:48.493Z
2021-12-04T18:17:48.493Z
Seyed Moein Ayyoubzadeh
<p>I'm trying to process product data for an e-commerce platform. The goal is to understand products' size.</p>

<p>Just to show you some examples on how messy product dimension description is:</p>

<pre><code>Overall Dimensions: 66 in W x 41 in D x 36 in H
Overall: 59 in W x 28.75 in D x 30.75 in H
92w 37d 32h",
86.6 in W x 33.9 in D x 24 in H
W: 95.75\" D: 36.5\" H: 28.75\"",
W: 96\" D: 39.25\" H: 32\"",
"118\"W x 35\"D x 33\"T.",
"28 L x 95 W x 41 H"
"95\" W x 26.5\" H x 34.75\" D"
"98\"W x 39\"D x 29\"H"
"28\" High x 80\" Wide x 32\" Deep"

</code></pre>

<p>Now assume that the product dimension description is short &lt; 60 characters, I trained a two layer bidirectional LSTM, which can handle this task perfectly.</p>

<p>But the problem is, the above dimension is usually embedded in a long context (as a part of the product description). How can I extract the useful information from the long context and understand it? My LSTM can only accept context size of 60.</p>

<p>What language model is more suitable for this?</p>

CC BY-SA 4.0
2020-05-11T17:17:04.930Z
Why can you only train an LSTM with a maximum context of 60?
Mathias Müller
CC BY-SA 4.0
2021-12-05T20:53:05.867Z
I would also use (in addition of LSTM) some symbolic AI approach, expert-system like (e.g. [RefPerSys](http://refpersys.org/)....)
Basile Starynkevitch
<natural-language-processing><long-short-term-memory>
Which NLP model to use to handle long context?
Bill Yan
# Which NLP model to use to handle long context?

(Asked by: username_0 on 2020-05-07)

I'm trying to process product data for an e-commerce platform. The goal is to understand products' size.
Just to show you some examples on how messy product dimension description is:
Overall Dimensions: 66 in W x 41 in D x 36 in H
Overall: 59 in W x 28.75 in D x 30.75 in H
92w 37d 32h",
86.6 in W x 33.9 in D x 24 in H
W: 95.75\" D: 36.5\" H: 28.75\"",
W: 96\" D: 39.25\" H: 32\"",
"118\"W x 35\"D x 33\"T.",
"28 L x 95 W x 41 H"
"95\" W x 26.5\" H x 34.75\" D"
"98\"W x 39\"D x 29\"H"
"28\" High x 80\" Wide x 32\" Deep"


Now assume that the product dimension description is short < 60 characters, I trained a two layer bidirectional LSTM, which can handle this task perfectly.
But the problem is, the above dimension is usually embedded in a long context (as a part of the product description). How can I extract the useful information from the long context and understand it? My LSTM can only accept context size of 60.
What language model is more suitable for this?

**Tags:** natural-language-processing, long-short-term-memory

> **Comments:**
> **username_1**: Why can you only train an LSTM with a maximum context of 60?  
> **username_2**: I would also use (in addition of LSTM) some symbolic AI approach, expert-system like (e.g. [RefPerSys](http://refpersys.org/)....)  

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## 1 answers

### Answer by username_3 on 2021-12-04. Score: 1

In this problem, you can also use NER models in order to tag those numbers as Width, Height,... . You can also fine-tune a DistilBert model for your task.

if you want to train NER model by tok2vec you can use:
[Spacy Library](https://spacy.io/usage/training)

for fine-tuning DistilBERT you can use this.
[Hugging Face](https://huggingface.co/transformers/model_doc/distilbert.html)

> **Comments:**
> **username_4**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  

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