Improve model card with details from paper and repo

This PR updates the model card for this retrieval model by providing more relevant information:

- Added `pipeline_tag`: feature-extraction
- Added model description and link to github.
- Added license information

README.md

@@ -1,147 +1,113 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-4.0
+language:
+- en
+pipeline_tag: feature-extraction
+architectures:
+- BertModel
+tags:
+- embedding
+- retrieval
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+This model is a BERT model fine-tuned for feature extraction, specifically designed for use in information retrieval tasks. It is intended to be used as an encoder to generate embeddings for passages, which can then be used to improve the recall or re-ranking of information retrieval systems. This model was introduced 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
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Junyu Luo et al.
+- **Model type:** BERT
+- **Language(s) (NLP):** English
+- **License:** CC-BY-4.0
+- **Finetuned from model:** E5 Small Unsupervised BGE All Datasets HF
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** This repository
+- **Paper:** [Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval](https://huggingface.co/papers/2505.16967)
+- **Code:** The relabeling code can be found at https://github.com/luojunyu/rlhn
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is designed to generate embeddings for passages in information retrieval systems. It can be used directly for passage retrieval or fine-tuned for specific tasks.
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
+Generate passage embeddings for retrieval or re-ranking.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+This model can be fine-tuned for specific retrieval tasks or plugged into a larger information retrieval system to improve performance.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not intended for use in generating text or for any tasks other than feature extraction for information retrieval.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model's performance is dependent on the quality of the training data. It may exhibit biases present in the original training data or the relabeled data used in fine-tuning.
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## How to Get Started with the Model
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Please refer to the code in the original paper repository for how to compute passage embeddings.
 
-## How to Get Started with the Model
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
 
-Use the code below to get started with the model.
+model_name = 'WhereIsAI/e5-small-unsupervised-bge-all-datasets-hf'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True).to('cuda')
 
-[More Information Needed]
+text = "This is a sample passage."
+input_ids = tokenizer([text], return_tensors="pt", max_length=512, truncation=True, padding='max_length').to('cuda')
+with torch.no_grad():
+    output = model(**input_ids)
+embeddings = output.last_hidden_state[:, 0, :]  # Extract embeddings
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+The model was fine-tuned using a semi-supervised approach on a mix of labeled and unlabeled data. See the paper for more details.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+[More Information Needed]
 
-#### Preprocessing [optional]
+#### Preprocessing
 
 [More Information Needed]
 
-
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training regime:** [More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
 [More Information Needed]
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
 [More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
 [More Information Needed]
 
 ### Results
 
 [More Information Needed]
 
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
 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]
@@ -150,7 +116,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **Compute Region:** [More Information Needed]
 - **Carbon Emitted:** [More Information Needed]
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
@@ -168,32 +134,20 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 [More Information Needed]
 
-## Citation [optional]
+## Citation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+```
+@misc{luo2024semievol,
+    title={SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation},
+    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},
+}
+```
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
+Niels Drost (Hugging Face)