upload custom README

README.md

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 library_name: transformers
-tags: []
+base_model:
+- pranaydeeps/Ancient-Greek-BERT
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+This model is part of a series of models trained for the ML4AL paper “Gotta catch ‘em all!”: Retrieving people in Ancient Greek texts combining transformer models and domain knowledge", written in the context of the KU Leuven ID-N project NIKAW (Networks of Ideas and Knowledge in the Ancient World)
 
 ## 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:** [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 [optional]
-
-<!-- Provide the basic links for the model. -->
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-- **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. -->
-
-### 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]
-
-<!-- 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
+- **Developed by:** Marijke Beersmans & Alek Keersmaekers
+- **Model type:** BERTForTokenClassification, finetuned for NER (PERS, LOC, GRP).
+- **Language(s) (NLP):** Ancient Greek (NFC normalization)
+- **Finetuned from model:** pranaydeeps/Ancient-Greek-BERT
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Model Sources
 
-[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]
+- **Repository:** [NERAncientGreekML4AL GitHub](https://github.com/NER-AncientLanguages/NERAncientGreekML4AL.git) (for data and training scripts)
+- **Paper:** [ML4AL paper](https://aclanthology.org/2024.ml4al-1.16/)
 
 ## 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]
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-### 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]
+Repository: [NERAncientGreekML4AL GitHub](https://github.com/NER-AncientLanguages/NERAncientGreekML4AL.git)
 
-[More Information Needed]
+We thank the following projects for providing the training data:
 
+- [Digital Periegesis](https://www.periegesis.org/en)  
+- [Josh Kemp, annotated Odyssey](https://medium.com/pelagios/beyond-translation-building-better-greek-scholars-561ab331a1bc)  
+- [The Stepbible project](https://github.com/STEPBible/STEPBible-Data)  
+- [Perseus Digital Library, *Deipnosophistae*](https://data.perseus.org/citations/urn:cts:greekLit:tlg0008.tlg001.perseus-grc4)
 
-#### Training Hyperparameters
+### 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 -->
+We use Weights & Biases for hyperparameter optimization with a random search strategy (10 folds), aiming to maximize the evaluation F1 score (eval_f1). 
 
-#### Speeds, Sizes, Times [optional]
+The search space includes:
+ - Learning Rate: Sampled uniformly between 1e-6 and 1e-4
+ - Weight Decay: One of [0.1, 0.01, 0.001] 
+ - Number of Training Epochs: One of [3, 4, 5, 6]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+For the final training of this model, the hyperparameters were: 
+ - Learning Rate: 6.040686648207059e-05
+ - Weight Decay: 0.01
+ - Number of Training Epochs: 3
 
-[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. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## 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]
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-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## 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. -->
-
-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
+This models was evaluated on precision, recall and macro-f1 for its entity classes. See the paper for more information.
+| Label        |   precision |   recall |   f1-score |   support |
+|:-------------|------------:|---------:|-----------:|----------:|
+| GRP          |      0.7785 |   0.8483 |     0.8119 |      1384 |
+| LOC          |      0.7088 |   0.7557 |     0.7315 |      1105 |
+| PERS         |      0.8459 |   0.888  |     0.8664 |      3090 |
+| micro avg    |      0.8015 |   0.8519 |     0.826  |      5579 |
+| macro avg    |      0.7777 |   0.8306 |     0.8033 |      5579 |
+| weighted avg |      0.802  |   0.8519 |     0.8262 |      5579 |
+
+
+If you use this work, please cite the following paper:
+
+### **APA:**
+Beersmans, M., Keersmaekers, A., de Graaf, E., Van de Cruys, T., Depauw, M., & Fantoli, M. (2024). “Gotta catch `em all!”: Retrieving people in Ancient Greek texts combining transformer models and domain knowledge. In J. Pavlopoulos, T. Sommerschield, Y. Assael, S. Gordin, K. Cho, M. Passarotti, R. Sprugnoli, Y. Liu, B. Li, & A. Anderson (Eds.), Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024) (pp. 152–164). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.ml4al-1.16
+
+### **BibTeX**
+```bibtex
+@inproceedings{Beersmans_Keersmaekers_de Graaf_Van de Cruys_Depauw_Fantoli_2024,
+  address = {Hybrid in Bangkok, Thailand and online},
+  title = {“Gotta catch `em all!”: Retrieving people in Ancient Greek texts combining transformer models and domain knowledge},
+  url = {https://aclanthology.org/2024.ml4al-1.16},
+  DOI = {10.18653/v1/2024.ml4al-1.16},
+  abstractNote = {In this paper, we present a study of transformer-based Named Entity Recognition (NER) as applied to Ancient Greek texts, with an emphasis on retrieving personal names. Recent research shows that, while the task remains difficult, the use of transformer models results in significant improvements. We, therefore, compare the performance of four transformer models on the task of NER for the categories of people, locations and groups, and add an out-of-domain test set to the existing datasets. Results on this set highlight the shortcomings of the models when confronted with a random sample of sentences. To be able to more straightforwardly integrate domain and linguistic knowledge to improve performance, we narrow down our approach to the category of people. The task is simplified to a binary PERS/MISC classification on the token level, starting from capitalised words. Next, we test the use of domain and linguistic knowledge to improve the results. We find that including simple gazetteer information as a binary mask has a marginally positive effect on newly annotated data and that treebanks can be used to help identify multi-word individuals if they are scarcely or inconsistently annotated in the available training data. The qualitative error analysis identifies the potential for improvement in both manual annotation and the inclusion of domain and linguistic knowledge in the transformer models.},
+  booktitle = {Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)},
+  publisher = {Association for Computational Linguistics},
+  author = {Beersmans, Marijke and Keersmaekers, Alek and de Graaf, Evelien and Van de Cruys, Tim and Depauw, Mark and Fantoli, Margherita},
+  editor = {Pavlopoulos, John and Sommerschield, Thea and Assael, Yannis and Gordin, Shai and Cho, Kyunghyun and Passarotti, Marco and Sprugnoli, Rachele and Liu, Yudong and Li, Bin and Anderson, Adam},
+  year = {2024},
+  month = aug,
+  pages = {152--164}
+}