Correct task category to text-ranking

This PR corrects the `task_categories` metadata from `question-answering` to `text-ranking` for better discoverability of the dataset. The dataset is designed for improving retrieval and reranking models.

README.md

@@ -1,138 +1,137 @@
 ---
-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: 1238537194
-    num_examples: 61026
-  download_size: 713689370
-  dataset_size: 1238537194
-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: Remove 100K
-size_categories:
-- 10K<n<100K
+library_name: transformers
+tags: []
+license: cc-by-4.0
+pipeline_tag: feature-extraction
 ---
 
-# Dataset Card for Remove 100K
+# Model Card for Model ID
 
-## Dataset Description
-[Repository](https://github.com/castorini/rlhn) |
-[Paper](https://huggingface.co/papers/2505.16967) |
-[ArXiv](https://arxiv.org/abs/2505.16967)
+This model identifies and relabels false negatives in IR training datasets as described in the paper [Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval](https://huggingface.co/papers/2505.16967). It is based on the e5-base model.
 
-RLHN is a cascading LLM framework designed to accurately relabel hard negatives in existing IR/RAG training datasets, such as MS MARCO and HotpotQA.
+## Model Details
 
-This Tevatron dataset (100K training pairs) contains the original queries, positives and hard negatives after dropping each training pair with a single false negative. 
+-   **Developed by:** [More Information Needed]
+-   **Model type:** BertModel
+-   **Language(s) (NLP):** en
+-   **License:** cc-by-4.0
+-   **Finetuned from model:** models/e5-base-unsupervised-bge-retrieval-7-datasets-250K-default
 
-This repository contains the training pairs that can be used to fine-tune embedding, ColBERT or multi-vector, and reranker models. 
+### Model Sources
 
-The original dataset (bad quality; containing false negatives) can be found at [rlhn/default-100K](https://huggingface.co/datasets/rlhn/default-100K/).
+-   **Repository:** Automatically Generated
+-   **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/studio-name/rlhn
 
-> Note: RLHN datasets are not **new** training datasets, but rather existing BGE collection training datasets with hard negatives cleaned!
+## Uses
 
-## Dataset Structure
+### Direct Use
 
-To access the data using HuggingFace `datasets`:
-```python
-rlhn = datasets.load_dataset('rlhn/remove-100K')
-
-# 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
-```
+This model is designed for identifying and relabeling hard negatives in information retrieval training datasets. It can be used to improve the quality of training data for retrieval and reranker models.
+
+### Downstream Use
+
+Fine-tuning retrieval and reranker models using the relabeled data can lead to significant improvements in retrieval effectiveness, especially on out-of-distribution datasets.
+
+### Out-of-Scope Use
 
+This model is not intended for use in applications that require real-time or low-latency performance, as the relabeling process involves computationally intensive LLM inference.
 
-## 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.  
+## Bias, Risks, and Limitations
 
-| 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**  |
+The effectiveness of this model depends on the quality and diversity of the LLMs used for relabeling. Biases in the LLMs may lead to biased relabeling and affect the performance of downstream models.
 
+### Recommendations
 
-## License
-The RLHN dataset is made available with the CC-BY-SA 4.0 license.
+Users should be aware of the potential biases and limitations of the LLMs used for relabeling and carefully evaluate the impact of the relabeled data on the performance of downstream models.
 
-## Hashing & IDs
+## How to Get Started with the Model
 
-We generate the md5 hash as the unique identifier (ID) for both the query \& documents, using the code below:
+Use the model with the transformers library:
 
 ```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
+from transformers import AutoModel, AutoTokenizer
+
+model_name = "models/e5-base-unsupervised-bge-retrieval-7-datasets-250K-default"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Example usage
+text = "This is an example sentence."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+embeddings = outputs.last_hidden_state
+print(embeddings.shape)
 ```
 
+## Training Details
+
+### Training Data
+
+The model used here was trained on a subset of the BGE collection and has a vocab size of 30522.
+
+### Training Procedure
+
+The model was fine-tuned using a semi-supervised approach with LLMs to relabel hard negatives.
+
+#### Training Hyperparameters
+
+-   **Training regime:** bfloat16 mixed precision
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+BEIR and AIR-Bench
+
+#### Metrics
+
+nDCG@10
+
+### Results
+
+Relabeling false negatives with true positives improves both E5 (base) and Qwen2.5-7B retrieval models by 0.7-1.4 nDCG@10 on BEIR and by 1.7-1.8 nDCG@10 on zero-shot AIR-Bench evaluation. Similar gains are observed for rerankers fine-tuned on the relabeled data, such as Qwen2.5-3B on BEIR.
+
+## 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
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
 ## 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}, 
+@misc{luo2024semievol,
+    title={Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval},
+    author={Junyu Luo and Xiao Luo and Xiusi Chen and Zhiping Xiao and Wei Ju and Ming Zhang},
+    year={2024},
+    eprint={2410.14745},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/2410.14745},
 }
 ```