Add GreBerta files

README.md

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+---
+language: grc
+license: apache-2.0
+inference: false
+datasets:
+- bowphs/internet_archive_filtered
+---
+# GrεBerta
+
+The paper [Exploring Language Models for Classical Philology](https://todo.com) is the first effort to systematically provide state-of-the-art language models for Classical Philology. GrεBerta is a RoBerta-base sized, monolingual, encoder-only variant. Further information can be found in our paper or in our [GitHub repository](https://github.com/Heidelberg-NLP/ancient-language-models).
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained('bowphs/GreBerta')
+model = AutoModelForMaskedLM.from_pretrained('bowphs/GreBerta')
+```
+Please check out the awesome Hugging Face tutorials on how to fine-tune our models.
+
+## Evaluation Results
+When fine-tuned on data from [Universal Dependencies 2.10](https://universaldependencies.org/), GrεBerta achieves the following results on the Ancient Greek Perseus dataset:
+
+| Task | XPoS | UPoS  | UAS | LAS |
+|:--:|:--:|:--:|:--:|:--:|
+|      |95.83|91.09|88.20|83.98|
+
+## Contact
+If you have any questions or problems, feel free to [reach out](mailto:riemenschneider@cl.uni-heidelberg.de).
+
+## Citation
+```bibtex
+@incollection{riemenschneiderfrank:2023,
+    address = "Toronto, Canada",
+    author = "Riemenschneider, Frederick and Frank, Anette",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL’23)",
+    note = "to appear",
+    pubType = "incollection",
+    publisher = "Association for Computational Linguistics",
+    title = "Exploring Large Language Models for Classical Philology",
+    url = "https://arxiv.org/abs/2305.13698",
+    year = "2023",
+    key = "riemenschneiderfrank:2023"
+}
+```

config.json

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+{
+  "_name_or_path": "bowphs/GreBerta",
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.19.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 52000
+}

pytorch_model.bin

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special_tokens_map.json

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+}

tf_model.h5

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tokenizer_config.json

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