i bought old domain and when i put it in search console i go

i bought old domain and when i put it in search console i got this message , ho
i bought old domain and when i put it in search console i got this message , how to solve this problem ?
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Say goodbye to the domain and take ur money back if the one who u bought from is still reachable
You can request Google to remove penalty and in most cases they do.

You'll first need to find all the spam backlinks and then disavow them.

After that you need to humbly request Google to remove spam penalty and let them know that it was a honest mistake and you didn't know that domain was spammed.

I have done it (https://blogcharge.com/remove-google-spam-penalty/) so I can speak from my experience that it's possible.

Disclaimer: I own BlogCharge and wrote that article. I didn't want to spam or shamelessly promote my blog here but I feel the complete guide will help OP sort out his issue. If mods feel it is unnecessary, they can remove the link.
when i put it in search console i got this message , ho
Oh I thought google is calling you a HO
Your domain's toast buddy. Time to go with a fresh one. New domain >> Old domain (in most cases) but that's just me.

Every keyword you’re actively targeting gets added as an exact match negative. This will ensure your broad match keyword can focus on new query ideas/one off searches, while your core campaigns can deliver leads/sales via proven keyword concepts.
If an applicable in-market audience exists, layer it on the broad ad group/campaign so you can prequalify the data acquisition.
Audit your queries regularly, and be open to swapping keyword concepts you’re actively targeting for ideas your broad match keyword secures (provided there’s enough volume/the business case is there).
Campaigns should only have one broad match keyword (sequestered away in its own ad group). Any more than that, and the data acquisition will turn into waste.
Tactic 2: Lead with Display, Remarket with Search
If you’ve read my deep learning articles, you should not only have a practical understanding of how BERT works but also how to use it for SEO purposes – specifically, for automating intent classification.

Let’s expand this and cover another use case: state of the art text summarization.

We can use automated text summarization to generate meta descriptions that we can populate on pages that don’t have one.

In order to illustrate this powerful technique, I’m going to automatically download and summarize my last article and as usual, I’ll share Python code snippets that you can follow along and adapt to your own site or your clients’.

Here is our plan of action:

Discuss automated text summarization.
Learn how to find state-of-the-art (SOTA) code that we can use for summarization.
Download the text summarization code and prepare the environment.
Download my last article and scrape just the main content on the page.
Use abstractive text summarization to generate the text summary.
Go over the concepts behind PreSumm.
Discuss some of the limitations.
Finally, I’ll share resources to learn more and community projects.
Text Summarization to Produce Meta Descriptions

When we have pages rich in content, we can leverage automated text summarization to produce meta descriptions at scale.

There are two primary approaches for text summarization according to the output:

Extractive: We split the text into sentences and rank them based on how effective they will be as a summary for the whole article. The summary will always contain sentences found in the text.
Abstractive: We generate potentially novel sentences that capture the essence of the text.
In practice, it is generally a good idea to try both approaches and pick the one that gets the best results for your site.

How to Find State of the Art (SOTA) Code for Text Summarization

My favorite place to find cutting edge code and papers is Papers with Code.

If you browse the State-of-the-Art section, you can find the best performing research for many categories.

If we narrow down our search to Text Summarization, we can find this paper: Text Summarization with Pretrained Encoders, which leverages BERT.

From there, we can conveniently find links to the research paper, and most importantly the code that implements the research.

How to Use BERT to Generate Meta Descriptions at Scale

It is also a good idea to check the global rankings often in case a superior paper comes up.

Download PreSum & Set up the Environment

Create a notebook in Google Colab to follow the next steps.

The original code found in the researcher’s repository doesn’t make it easy to use the code to generate summaries.

You can feel the pain just by reading this Github issue. ??

We are going to use a forked version of the repo and some simplified steps that I adapted from this notebook.

Let’s first clone the repository.

!git clone https://github.com/mingchen62/PreSumm.git
Then install the dependencies.

!pip install torch==1.1.0 pytorch_transformers tensorboardX multiprocess pyrouge
Next, we need to download the pre-trained models.

Then, we need to uncompress and move them to organized directories.

After this step, we should have the summarization software ready.

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