Change task category to text-ranking

README.md

@@ -1,138 +1,158 @@
 ---
-dataset_info:
-  features:
-  - name: query_id
-    dtype: string
-  - name: query
-    dtype: string
-  - name: positive_passages
-    list:
-    - name: docid
-      dtype: string
-    - name: text
-      dtype: string
-    - name: title
-      dtype: string
-  - name: negative_passages
-    list:
-    - name: docid
-      dtype: string
-    - name: text
-      dtype: string
-    - name: title
-      dtype: string
-  - name: subset
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 10528804827
-    num_examples: 648766
-  download_size: 6214353445
-  dataset_size: 10528804827
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-license: cc-by-sa-4.0
-task_categories:
-- question-answering
-language:
-- en
-pretty_name: HN Remove 680K
-size_categories:
-- 100K<n<1M
+library_name: transformers
+tags:
+- text-ranking
 ---
 
-# Dataset Card for HN-Remove 680K
-
-## Dataset Description
-[Repository](https://github.com/castorini/rlhn) |
-[Paper](https://huggingface.co/papers/2505.16967) |
-[ArXiv](https://arxiv.org/abs/2505.16967)
-
-RLHN is a cascading LLM framework designed to accurately relabel hard negatives in existing IR/RAG training datasets, such as MS MARCO and HotpotQA.
-
-This Tevatron dataset (680K training pairs) contains the queries, positives, hard negatives (with dropped false negatives) for 7 datasets in the BGE training collection. 
-
-This repository contains the training pairs that can be used to fine-tune embedding, ColBERT or multi-vector, and reranker models. 
-
-The original dataset (bad quality; containing false negatives) can be found at [rlhn/default-680K](https://huggingface.co/datasets/rlhn/default-680K/).
-
-> Note: RLHN datasets are not **new** training datasets, but rather existing BGE collection training datasets with hard negatives cleaned!
-
-## Dataset Structure
-
-To access the data using HuggingFace `datasets`:
-```python
-rlhn = datasets.load_dataset('rlhn/hn-remove-680K')
-
-# training set:
-for data in freshstack['train']:
-    query_id = data["query_id"]                            # md5 hash of the query_id
-    query = data["query"]                                  # query text
-    subset = data["subset"]                                # training dataset, e.g., fiqa or msmarco_passage
-
-    # positive passages
-    for positive_passage in data["positive_passages"]:
-        doc_id = positive_passage["docid"]
-        title = positive_passage["title"]                  # title is usually empty, added in text
-        text = positive_passage["text"]                    # contains both the title & text
-
-    # hard negative passages
-    for negative_passage in data["negative_passages"]:
-        doc_id = negative_passage["docid"]
-        title = negative_passage["title"]                  # title is usually empty, added in text
-        text = negative_passage["text"]                    # contains both the title & text
-```
-
-
-## Original Dataset Statistics 
-The following table contains the number of training pairs for each training dataset included in RLHN. These numbers are for the default setting.  
-
-| Dataset           | 100K splits | 250K splits | 400K splits | 680K splits  |
-|-------------------|-------------|-------------|-------------|------------- |
-| arguana           | 4,065       | 4,065       | 4,065       | 4,065        |
-| fever             | 28,755      | 28,755      | 28,755      | 28,755       |
-| fiqa              | 5,500       | 5,500       | 5,500       | 5,500        |
-| hotpotqa          | 10,250      | 30,000      | 84,516      | 84,516       |
-| msmarco_passage   | 49,571      | 145,000     | 210,000     | 485,823      |
-| nq                | 6,110       | 30,000      | 58,568      | 58,568       |
-| scidocsrr         | 12,654      | 12,654      | 12,654      | 12,654       |
-| **total**         | **96,167**  | **255,974** | **404,058** | **679,881**  |
-
-
-## License
-The RLHN dataset is made available with the CC-BY-SA 4.0 license.
-
-## Hashing & IDs
-
-We generate the md5 hash as the unique identifier (ID) for both the query \& documents, using the code below:
-
-```python
-import hashlib
-
-def get_md5_hash(text):
-  """Calculates the MD5 hash of a given string.
-  Args:
-    text: The string to hash.
-  Returns:
-    The MD5 hash of the string as a hexadecimal string.
-  """
-  text_bytes = text.encode('utf-8')  # Encode the string to bytes
-  md5_hash = hashlib.md5(text_bytes).hexdigest()
-  return md5_hash
-```
-
-## Citation
-```
-@misc{thakur2025relabel,
-      title={Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval}, 
-      author={Nandan Thakur and Crystina Zhang and Xueguang Ma and Jimmy Lin},
-      year={2025},
-      eprint={2505.16967},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR},
-      url={https://arxiv.org/abs/2505.16967}, 
-}
-```
+# Model Card for Model ID
+
+Model presented in the paper [Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval](https://huggingface.co/papers/2505.16967).
+
+## Model Details
+
+### Model Description
+
+This model is a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. It is fine-tuned using a novel approach of cascading LLM prompts to identify and relabel hard negatives for robust information retrieval.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** Text-ranking
+- **Language(s) (NLP):** en
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+- **Repository:** [More Information Needed]
+- **Paper:** [Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval](https://huggingface.co/papers/2505.16967)
+- **Code:** https://github.com/luo-junyu/rlhn
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+### Direct Use
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+[More Information Needed]
+
+### Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+[More Information Needed]
+
+### Training Procedure
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed]
+
+#### Speeds, Sizes, Times [optional]
+
+[More Information Needed]
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+[More Information Needed]
+
+#### Factors
+
+[More Information Needed]
+
+#### Metrics
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+## Model Examination [optional]
+
+[More Information Needed]
+
+## Environmental Impact
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]