Improve model card: Add pipeline tag, library name, and expand usage details

README.md

@@ -1,13 +1,16 @@
 ---
-MachineLearningML: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
-license: apache-2.0
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
 # MachineLearningLM
 
-## model summary
+This repository contains the model presented in the paper [MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining](https://huggingface.co/papers/2509.06806).
+
+## Model Summary
 
 Can LLMs learn from 1,000 in-context examples?
 
@@ -25,10 +28,10 @@ Introducing **MachineLearningLM** 🧪📊 — a model continuously pretrained o
 
    GitHub: https://github.com/HaoAreYuDong/MachineLearningLM
 
-## evaluation and validation
+## Evaluation and Validation
 
 We have developed an automated evaluation framework — simply configure the parameters to easily perform validation and evaluation. 
-**The code is now open-sourced at our GitHub.**
+**The code is now open-sourced at our [GitHub repository](https://github.com/HaoAreYuDong/MachineLearningLM).**
 
 **Quick Start**
 
@@ -39,7 +42,7 @@ python ./src/evaluation/model_pred/dl_model_pred.py \
   --output_dir ./demo_output.jsonl \
   --model_name MachineLearningLM/MachineLearningLM-7B-v1
 ```
-**pipeline**
+**Pipeline**
 ```bash
 # modify the evaluate_parameters.sh file
 source evaluate_parameters.sh
@@ -64,3 +67,118 @@ source evaluate_parameters.sh
 
 For more usage details, please visit our GitHub.
 
+## Tabicl Evaluation
+
+**This part of the code needs to run in an environment with the tabicl and openpyxl libraries installed.**
+
+The evaluation code for tabicl is placed separately in the `./src/evaluation/tabicl_evaluate.py` file. Use `./scripts/tabicl_evaluate.sh` to obtain the evaluation results for tabicl.
+
+Use --datasets to specify the datasets to be evaluated, and --sample_sizes to indicate the number of shots. 
+
+If multiple datasets need to be evaluated, separate them with spaces. To evaluate all CSV files in the input folder, use **all**.
+
+## Prior_data
+
+MachineLearningLM uses the code from tabicl to generate prior data.
+
+Use `./scripts/generate_data.sh` to generate the prior data. It generates the corresponding .pt and .csv files, and normalizes the feature values in the CSV files to the range of 0–999, as we did in the paper.
+
+### Parameter Introduction（refer to the comments in the file `tabicl\src\tabicl\prior\dataset.py`）
+
+**Data Scale & Structure**
+
+| Parameter      | Type | Description                                             |
+| :------------- | :--- | :------------------------------------------------------ |
+| `min_features` | int  | Minimum number of features per dataset                  |
+| `max_features` | int  | Maximum number of features per dataset                  |
+| `max_classes`  | int  | Maximum number of target classes                        |
+| `min_seq_len`  | int  | Minimum samples per dataset. Uses `max_seq_len` if None |
+| `max_seq_len`  | int  | Maximum samples per dataset （Not Include）             |
+
+**Batch Configuration**
+
+| Parameter              | Type | Description                                                  |
+| :--------------------- | :--- | :----------------------------------------------------------- |
+| `batch_size`           | int  | Total number of datasets to generate per batch               |
+| `batch_size_per_gp`    | int  | Number of datasets per group (shared characteristics)        |
+| `batch_size_per_subgp` | int  | Number of datasets per subgroup (similar causal structures). Defaults to `batch_size_per_gp` if None |
+
+**Sequence Length Control**
+
+| Parameter        | Type | Description                                                  |
+| :--------------- | :--- | :----------------------------------------------------------- |
+| `log_seq_len`    | bool | Sample sequence length from log-uniform distribution if True |
+| `seq_len_per_gp` | bool | Sample sequence length per group (enables variable-sized datasets) |
+| `replay_small`   | bool | Occasionally sample smaller sequences for model robustness   |
+
+**Train-Test Split**
+
+| Parameter        | Type      | Description                                                  |
+| :--------------- | :-------- | :----------------------------------------------------------- |
+| `min_train_size` | int/float | Start position/ratio for train split (int: absolute, float: fractional) |
+| `max_train_size` | int/float | End position/ratio for train split (int: absolute, float: fractional) |
+
+**Generation Method**
+
+| Parameter    | Type | Description                                                  |
+| :----------- | :--- | :----------------------------------------------------------- |
+| `prior_type` | str  | Prior type: 'mlp_scm', 'tree_scm', or 'mix_scm' (random selection) |
+| `fixed_hp`   | dict | Fixed structural configuration parameters                    |
+| `sampled_hp` | dict | Parameters sampled during generation                         |
+
+**Computation Settings**
+
+| Parameter                  | Type | Description                                       |
+| :------------------------- | :--- | :------------------------------------------------ |
+| `n_jobs`                   | int  | Number of parallel jobs (-1 = use all processors) |
+| `num_threads_per_generate` | int  | Number of threads per generation job              |
+| `device`                   | str  | Computation device ('cpu' or 'cuda')              |
+
+## Train
+
+MachineLearningLM uses the LLaMA-Factory framework for training.
+
+#### Training Environment Configuration
+
+```bash
+cd ./third_party/LLaMA-Factory
+pip install -e ".[torch,metrics]" --no-build-isolation
+pip install wandb
+```
+
+Use `./scripts/train.sh` for training.
+
+## Project Structure
+
+```
+MachineLearningLM/
+├──src/
+|   ├──evaluation/
+│   │   ├── data_prep/          # Data preprocessing and chunking utilities
+│   │   ├── prompt_gen/         # Prompt generation for deep learning models
+│   │   ├── model_pred/         # Model inference (ML and DL prediction engines)
+│   │   ├── result_proc/        # 5-layer evaluation architecture and metrics processing
+│   │   ├── zero_summary/       # Result summarization and report generation
+│   │   └── tabicl_evaluate.py
+│   └──prior_data
+│       └── pt_to_csv.py     
+├── scripts/
+│   ├── single_process/         # Sequential execution shell scripts
+│   ├── multi_process/          # Parallel execution shell scripts (with _mp suffix)
+│   ├── evaluate_parameters.sh  # Global parameter configuration
+|   ├── evaluate_pipeline.sh    # automated pipeline
+|   ├── generate_data.sh
+|   ├── tabicl_evaluate.sh
+|   └── train.sh
+├── datahub_inputs/
+│   ├── data_demo/          # Demo datasets for testing
+│   └── data_raw/           # Raw input datasets
+├── third_party/
+│   ├── tabicl/          
+│   └── LLaMA-Factory/   
+├── requirements.txt        # Python dependencies for Evaluation Framework
+├── README.md
+├── README_zh.md
+├── THIRD_PARTY_NOTICES.md
+└── LICENSE
+```