Answers.Body: <p>Given the lengthy nature of financial reports, you can consider using techniques like document embedding or topic modeling. Document embeddings, such as Doc2Vec or Universal Sentence Encoder, can represent the semantic meaning of a document in a fixed-length vector representation. Topic modeling algorithms like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) can help identify key topics or themes within the documents.</p>
  <p>Achieving a homogeneous training set without reading all the texts can be approached through a combination of techniques. One way is to use supervised or semi-supervised learning, where you label a subset of the data and use it to train the initial model. You can then iteratively refine the model's performance by actively selecting representative texts for annotation based on the model's uncertainty or other criteria.</p>
  <p>Once you have the final model, you can query it by inputting specific text or queries to obtain the desired information. You can predefine patterns or utilize natural language processing techniques to enable more flexible inquiries. The model will interpret the query based on its learned representations and patterns, providing relevant information in response.</p>
  <p>Given the limited information available about your project's details, it's important to emphasize that the effectiveness of these techniques may rely on other factors, such as the quality and structure of the data, available computational resources, and the complexity of the information you seek to extract. Further research and experimentation will be necessary to fine-tune the process to suit your specific project requirements.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-12T01:11:12.920Z
Answers.Text: What model would be suitable for this type of problem? I mean, when I say training, should I be training a neural network for example?
Answers.UserDisplayName: Hlkwtz
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-12T12:38:20.507Z
Answers.Text: @Hlkwtz This user probably user ChatGPT or something like that to generate this answer. I'd not trust this answer or user.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-12T21:16:00.377Z
Answers.Text: @nbro True, I should've looked at his profile first, thanks for notifying.
Answers.UserDisplayName: Hlkwtz
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-11T09:28:52.947Z
Answers.LastActivityDate: 2023-12-11T09:28:52.947Z
Answers.UserDisplayName: han
Body: <p>I want to train a model to be able to interpret financial reports (from a company). Basically, <strong>I want to be able to extract relevant information without needing to read through hundreds of pages of text</strong>. I know I will need to use a database that can store vectors for the embeddings of the texts. However, I have some questions:</p>
  <ul>
  <li>Assuming the texts are hundreds of pages long and, therefore, they go over a lot of stuff, what vectorization technique can be used?</li>
  <li>I assume I will have to obtain a homogeneous training set. How do I do this without reading all the texts if I still don't have the final model (classification??)?</li>
  <li>Let's say I have the final model. How do I &quot;query&quot; the things I want to know? In other words, would I have to input some text and then the model has to be able to interpret that as a query?</li>
  </ul>
  <p>Note: I apologize in advance if im not being too descriptive, I currently don't have so much information about the details of the data and other things, I only have a general sketch about the idea of the project.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-28T00:55:44.107Z
Comments.Text: I think you should split this post into multiple ones because you're asking several questions.
Comments.UserDisplayName: nbro
Tags: <natural-language-processing><large-language-models><embeddings>
Title: Approaching construction of model that interprets financial reports
UserDisplayName: Hlkwtz
ThreadText: # Approaching construction of model that interprets financial reports
  
  (Asked by: username_0 on 2023-12-07)
  
  I want to train a model to be able to interpret financial reports (from a company). Basically, **I want to be able to extract relevant information without needing to read through hundreds of pages of text**. I know I will need to use a database that can store vectors for the embeddings of the texts. However, I have some questions:
  
  * Assuming the texts are hundreds of pages long and, therefore, they go over a lot of stuff, what vectorization technique can be used?
  * I assume I will have to obtain a homogeneous training set. How do I do this without reading all the texts if I still don't have the final model (classification??)?
  * Let's say I have the final model. How do I "query" the things I want to know? In other words, would I have to input some text and then the model has to be able to interpret that as a query?
  
  Note: I apologize in advance if im not being too descriptive, I currently don't have so much information about the details of the data and other things, I only have a general sketch about the idea of the project.
  
  **Tags:** natural-language-processing, large-language-models, embeddings
  > **Comments:**
  > **username_1**: I think you should split this post into multiple ones because you're asking several questions.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-12-11. Score: 0
  
  Given the lengthy nature of financial reports, you can consider using techniques like document embedding or topic modeling. Document embeddings, such as Doc2Vec or Universal Sentence Encoder, can represent the semantic meaning of a document in a fixed-length vector representation. Topic modeling algorithms like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) can help identify key topics or themes within the documents.
  
  Achieving a homogeneous training set without reading all the texts can be approached through a combination of techniques. One way is to use supervised or semi-supervised learning, where you label a subset of the data and use it to train the initial model. You can then iteratively refine the model's performance by actively selecting representative texts for annotation based on the model's uncertainty or other criteria.
  
  Once you have the final model, you can query it by inputting specific text or queries to obtain the desired information. You can predefine patterns or utilize natural language processing techniques to enable more flexible inquiries. The model will interpret the query based on its learned representations and patterns, providing relevant information in response.
  
  Given the limited information available about your project's details, it's important to emphasize that the effectiveness of these techniques may rely on other factors, such as the quality and structure of the data, available computational resources, and the complexity of the information you seek to extract. Further research and experimentation will be necessary to fine-tune the process to suit your specific project requirements.
  
  > **Comments:**
  > **username_0**: What model would be suitable for this type of problem? I mean, when I say training, should I be training a neural network for example?  
  > **username_1**: @Hlkwtz This user probably user ChatGPT or something like that to generate this answer. I'd not trust this answer or user.  
  > **username_0**: @nbro True, I should've looked at his profile first, thanks for notifying.  
  
  ---
  
  