Update README.md

README.md

@@ -4,41 +4,26 @@ language:
 - he
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
 
 ## 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 is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
+- **Model type:** CrossEncoder
+- **Language(s) (NLP):** Hebrew
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+- **Finetuned from model [optional]:** [DictaBERT](https://huggingface.co/dicta-il/dictabert)
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## 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. -->
+Model was trained for ranking task as a part of a Hebrew semantic search engine.
 
-### Direct Use
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
 
 ```python
 from sentence_transformers import CrossEncoder
@@ -64,7 +49,7 @@ doc2 = """
 עם עלייה חדה בתקריות טרור מצד ארגונים אסלאמיים קיצוניים בסיני.
 """
 
-model = CrossEncoder('haguy77/sdictabert-heq')
+model = CrossEncoder("haguy77/dictabert-ce")
 
 scores = model.predict([[query, doc1], [query, doc2]])  # Note: query should ALWAYS be the first of each pair
 # array([0.02000629, 0.00031683], dtype=float32)
@@ -73,143 +58,37 @@ results = model.rank(query, [doc2, doc1])
 # [{'corpus_id': 1, 'score': 0.020006292}, {'corpus_id': 0, 'score': 0.00031683326}]
 ```
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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
 
-<!-- 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]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[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]
+[Hebrew Question Answering Dataset (HeQ)](https://github.com/NNLP-IL/Hebrew-Question-Answering-Dataset)
 
-## 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]
-- **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]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@misc{shmidman2023dictabert,
+      title={DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew}, 
+      author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel},
+      year={2023},
+      eprint={2308.16687},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+```bibtex
+@inproceedings{cohen2023heq,
+  title={Heq: a large and diverse hebrew reading comprehension benchmark},
+  author={Cohen, Amir and Merhav-Fine, Hilla and Goldberg, Yoav and Tsarfaty, Reut},
+  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
+  pages={13693--13705},
+  year={2023}
+}
+```
 
 **APA:**
+```apa
+Shmidman, S., Shmidman, A., & Koppel, M. (2023). DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew. arXiv preprint arXiv:2308.16687.
 
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Cohen, A., Merhav-Fine, H., Goldberg, Y., & Tsarfaty, R. (2023, December). Heq: a large and diverse hebrew reading comprehension benchmark. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 13693-13705).
+```