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| title: Book Recommender | |
| emoji: ⚡ | |
| colorFrom: indigo | |
| colorTo: gray | |
| sdk: gradio | |
| sdk_version: 5.6.0 | |
| app_file: app.py | |
| pinned: false | |
| short_description: A content based book recommender. | |
| # Content-Based-Book-Recommender | |
| A HyDE based approach for building recommendation engine. | |
| Try it out: https://huggingface.co/spaces/LunaticMaestro/book-recommender | |
|  | |
| ## Foreword | |
| - All images are my actual work please source powerpoint of them in `.resources` folder of this repo. | |
| - Code is documentation is as per [Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html) | |
| - ALL files Paths are at set as CONST in beginning of each script, to make it easier while using the paths while inferencing & evaluation; hence not passing as CLI arguments | |
| - prefix `z_` in filenames is just to avoid confusion (to human) of which is prebuilt module and which is custom during import. | |
| ## Table of Content | |
| > | |
| - [Running Inference Locally](#libraries-execution) | |
| - [10,000 feet Approach overview](#approach) | |
| - Pipeline walkthrough in detail | |
| *For each part of pipeline there is separate script which needs to be executed, mentioned in respective section along with output screenshots.* | |
| - [Training](#training-steps) | |
| - [Step 1: Data Clean](#step-1-data-clean) | |
| - [Step 2: Generate vectors of the books summaries](#step-2-generate-vectors-of-the-books-summaries) | |
| - [Step 3: Fine-tune GPT-2 to Hallucinate but with some bounds.](#step-3-fine-tune-gpt-2-to-hallucinate-but-with-some-bounds) | |
| - [Evaluation](#evaluation) | |
| - Inference | |
| ## Running Inference Locally | |
| ### Memory Requirements | |
| The code need <2Gb RAM to use both the following. Just CPU works fine for inferencing. | |
| - https://huggingface.co/openai-community/gpt2 ~500 mb | |
| - https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 <500 mb | |
| ### Libraries | |
| I used google colab with following libraries extra. | |
| ```SH | |
| pip install sentence-transformers datasets | |
| ``` | |
| ### Running | |
| #### Local System | |
| ```SH | |
| python app.py | |
| ``` | |
| access at http://localhost:7860/ | |
| #### Goolge Colab | |
| Modify app.py edit line 93 to `demo.launch(share=True)` then run following in cell. | |
| ``` | |
| !python app.py | |
| ``` | |
| ## Approach | |
|  | |
| References: | |
| - This is the core idea: https://arxiv.org/abs/2212.10496 | |
| - Another work based on same, https://github.com/aws-samples/content-based-item-recommender | |
| - For future, a very complex work https://github.com/HKUDS/LLMRec | |
| ## Training Steps | |
| ### Step 1: Data Clean | |
| What is taken care | |
| - unwanted column removal (the first column of index) | |
| - missing values removal (drop rows) | |
| - duplicate rows removal. | |
| What is not taken care | |
| - stopword removal, stemming/lemmatization or special character removal | |
| **because approach is to use the casual language modelling (later steps) hence makes no sense to rip apart the word meaning** | |
| ### Observations from `z_cleand_data.ipynb` | |
| - Same title corresponds to different categories | |
|  | |
| - Total 1230 unique titles. | |
|  | |
| **Action**: We are not going to remove them rows that shows same titles (& summaries) with different categories but rather create a separate file for unique titles. | |
| **RUN**: | |
| ```SH | |
| python z_clean_data.py | |
| ``` | |
|  | |
| Output: `clean_books_summary.csv`, `unique_titles_books_summary.csv` | |
| ### Step 2: Generate vectors of the books summaries. | |
| **WHAT & WHY** | |
| Here, I am going to use pretrained sentence encoder that will help get the meaning of the sentence. We perform this over `unique_titles_books_summary.csv` dataset | |
| Caching because the semantic meaning of the summaries (for books to output) are not changed during entire runtime. | |
|  | |
| **RUN**: | |
| Use command | |
| ```SH | |
| python z_embedding.py | |
| ``` | |
| Just using CPU should take <1 min | |
|  | |
| Output: `app_cache/summary_vectors.npy` | |
| ### Step 3: Fine-tune GPT-2 to Hallucinate but with some bounds. | |
| **What & Why** | |
| Hypothetical Document Extraction (HyDE) in nutshell | |
| - The **Hypothetical** part of HyDE approach is all about generating random summaries,in short hallucinating. **This is why the approach will work for new book titles** | |
| - The **Document Extraction** (part of HyDE) is about using these hallucinated summaries to do semantic search on database. | |
| **Why to fine-tune GPT-2** | |
| 1. We want it to hallucinate but withing boundaries i.e. speak words/ language that we have in books_summaries.csv NOT VERY DIFFERENT OUT OF WORLD LOGIC. | |
| 2. Prompt Tune such that we can get consistent results. (Screenshot from https://huggingface.co/openai-community/gpt2); The screenshot show the model is mildly consistent. | |
|  | |
| Reference: | |
| - HyDE Approach, Precise Zero-Shot Dense Retrieval without Relevance Labels https://arxiv.org/pdf/2212.10496 | |
| - Prompt design and book summary idea I borrowed from https://github.com/pranavpsv/Genre-Based-Story-Generator | |
| - I didnt not use his model | |
| - its lacks most of the categories; (our dataset is different) | |
| - His code base is too much, can edit it but not worth the effort. | |
| - Fine-tuning code instructions are from https://huggingface.co/docs/transformers/en/tasks/language_modeling | |
| **RUN** | |
| If you want to | |
| - push to HF. You must supply your token from huggingface, required to push model to HF | |
| ```SH | |
| huggingface-cli login | |
| ``` | |
| - Not Push to HF, then in `z_finetune_gpt.py`: | |
| - set line 59 ` push_to_hub` to `False` | |
| - comment line 77 `trainer.push_to_hub()` | |
| We are going to use dataset `clean_books_summary.csv` while triggering this training. | |
| ```SH | |
| python z_finetune_gpt.py | |
| ``` | |
| Image below just shows for 2 epochs, but the one push to my HF https://huggingface.co/LunaticMaestro/gpt2-book-summary-generator is trained for 10 epochs that lasts ~30 mins for 10 epochs with T4 GPU **reduing loss to 0.87 ~ (perplexity = 2.38)** | |
|  | |
| The loss you see is cross-entryopy loss; as ref in the [fine-tuning instructions](https://huggingface.co/docs/transformers/en/tasks/language_modeling) : `Transformers models all have a default task-relevant loss function, so you don’t need to specify one ` | |
| So all we care is lower the value better is the model trained :) | |
| We are NOT going to test this unit model on some test dataset as the model is already proven (its GPT-2 duh!!). | |
| But **we are going to evaluate our HyDE approach end-2-end next to ensure sanity of the approach** that will inherently prove the goodness of this model. | |
| ## Evaluation | |
| Before discussing evaluation metric let me walk you through two important pieces recommendation generation and similarity matching; | |
| ### Recommendation Generation | |
| The generation is handled by functions in script `z_hypothetical_summary.py`. Under the hood following happens | |
|  | |
| Code Preview. I did the minimal post processing to chop of the `prompt` from the generated summaries before returning the result. | |
|  | |
| ### Similarity Matching | |
|  | |
|  | |
| Because there are 1230 unique titles so we get the averaged similarity vector of same size. | |
|  | |
| ### Evaluation Metric | |
| So for given input title we can get rank (by desc order cosine similarity) of the store title. To evaluate we the entire approach we are going to use a modified version **Mean Reciprocal Rank (MRR)**. | |
|  | |
| We are going to do this for random 30 samples and compute the mean of their reciprocal ranks. Ideally all the title should be ranked 1 and their MRR should be equal to 1. Closer to 1 is good. | |
|  | |
| The values of TOP_P and TOP_K (i.e. token sampling for our generator model) are sent as `CONST` in the `z_evaluate.py`; The current set of values of this are borrowed from the work: https://www.kaggle.com/code/tuckerarrants/text-generation-with-huggingface-gpt2#Top-K-and-Top-P-Sampling | |
| MRR = 0.311 implies that there's a good change that the target book will be in rank (1/.311) ~ 3 (third rank) **i.e. within top 5 recommendations** | |
| ## Inference | |
| `app.py` is written so that it can best work with gradio interface in the HuggingFace, althought you can try it out locally as well :) | |
| ```SH | |
| python app.py | |
| ``` | |
| 1. I rewrote the snippets from `z_evaluate.py` to `app.py` with minor changes to expriment with view. | |
| 2. DONT set `debug=True` for gradio in HF space, else it doesn't start. | |
| 3. HF space work differently for retaining models across module scipts; local running (tried in colab space) works faster. You will see lot of my commits in HF Space to work around this problem. | |