Update README.md

README.md

@@ -1,37 +1,22 @@
 ---
 license: cc-by-4.0
-
-configs:
-- config_name: client_split
- data_files: "client_split.tar.gz"
-- config_name: detail
- data_files: "detail.tar.gz"
-- config_name: ptls
- data_files: "ptls.tar.gz"
-- config_name: targets
- data_files: "targets.tar.gz"
+viewer: false
 ---
-# Intro
-Predicting a customer's propensity to purchase a product is an important task for many companies, helping to:
-- assess the customer's needs, form their product profile;
-- improve the quality of recommendations, form package offers, form individual conditions;
-- correctly form a communication strategy with the customer
-- estimate the income that the customer can bring to the company in the future, based on the profitability of the products in which he is interested (Customer lifetime value - CLTV).
-
-To solve such problems, various data about the customer are usually used:
-- customer profile;
-- history of previous purchases and communications;
-- transactional activity;
-- geo-information about places of permanent or temporary residence;
-- etc.;
-
-Of particular importance are the data characterizing the patterns of client behavior (chains of events), as they help to understand the patterns in the client's actions, to assess the dynamics of changes in his behavior. The combined use of behavioral data from various sources helps to more fully describe the client in terms of predicting his needs, which, in turn, creates the task of the most effective combination of various modalities to improve the performance and quality of the developed model.
+# Dataset summary
+This dataset is designed to assist in predicting a customer's propensity to purchase various products within a month following the reporting date. The dataset includes anonymized historical data on transaction activity, dialog embeddings, and geo-activity for some bank clients over 12 months.
+
+Reduced dataset version is avaliable as [MBD-mini](https://huggingface.co/datasets/ai-lab/MBD-mini). The mini MBD dataset contains a reduced subset of the data, making it easier and faster to work with during the development and testing phases. It includes a smaller number of clients and a shorter time span but maintains the same structure and features as the full dataset. MBD-mini has data based on 10% of unique clients listed in MBD.
 
 # Data
-The dataset consists of anonymized historical data, which contains the following information: transaction activity (transactions), dialog embeddings (dialogs), geo-activity (geostream) for some of the Bank's clients over 12 months.
+The dataset consists of anonymized historical data, which contains the following information for some of the Bank's clients over 12 months:
+- transaction activity (transactions) Details about past transactions including amounts, types, and dates;
+- dialog embeddings (dialogs) Embeddings from customer interactions, which capture semantic information from dialogues;
+- geo-activity (geostream) Location-based data representing clients' geographic activity patterns.
 
 Objective: To predict for each user the taking/not taking of each of the four products within a month after the reporting date, historical data for them is in targets
 
+The dataset is divided into 5 folds based on client_split (which consist of an equal number of unique clients) for cross-validation purposes.
+
 ```
 client_split Desc: Splitting clients into folds
 |-- client_id: str Desc: Client id
@@ -42,6 +27,7 @@ detail
  |-- client_id: str Desc: Client id
  |-- event_time: timestamp Desc: Dialog's date
  |--embedding: array float Desc: Dialog's embeddings
+ |-- fold: int
 |-- geo Desc: Geo activity
  |-- client_id: str Desc: Client id
  |-- event_time: timestamp Desc: Event datetime
@@ -71,6 +57,7 @@ ptls Desc: Data is similar with detail but in pytorch-lifestream format https://
  |-- client_id: str Desc: Client id
  |-- event_time: Array[timestamp] Desc: Dialog's date
  |-- embedding: Array[float] Desc: Dialog's embedding
+ |-- fold: int
 |-- geo Desc: Geo activity
  |-- client_id: str Desc: Client id
  |-- event_time: Array[timestamp] Desc: Event datetime
@@ -104,6 +91,7 @@ targets
 |-- trans_count: int Desc: Number of transactions
 |-- diff_trans_date: int Desc: Time difference between transactions
 |-- client_id: str Desc: Client id
+|-- fold: int
 ```
  
 # Load dataset
@@ -113,7 +101,7 @@ Download a single file with datasets
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("ai-lab/MBD", 'client_split')
+dataset = load_dataset("ai-lab/MBD", data_files='client_split.tar.gz')
 ```
 
 Download a single file with huggingface_hub
@@ -145,17 +133,6 @@ snapshot_download(repo_id="ai-lab/MBD")
 ```
 
 # Citation
-We have a [paper](https://arxiv.org/abs/2002.08232) you can cite it:
 ```
-@inproceedings{
- Babaev_2022, series={SIGMOD/PODS ’22},
- title={CoLES: Contrastive Learning for Event Sequences with Self-Supervision},
- url={http://dx.doi.org/10.1145/3514221.3526129},
- DOI={10.1145/3514221.3526129},
- booktitle={Proceedings of the 2022 International Conference on Management of Data},
- publisher={ACM},
- author={Babaev, Dmitrii and Ovsov, Nikita and Kireev, Ivan and Ivanova, Maria and Gusev, Gleb and Nazarov, Ivan and Tuzhilin, Alexander},
- year={2022},
- month=jun, collection={SIGMOD/PODS ’22}
-}
+TBD
 ```