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README.md

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+---
+language: cs
+license: cc-by-nc-sa-4.0
+tags:
+- Czech
+- GEC
+- GECCC dataset
+---
+
+# Model Card for transformer-base-geccc-mate
+
+The `transformer-base-geccc-mate` model is a sequence-to-sequence model performing
+grammar error correction in Czech described in the paper
+[Refining Czech GEC: Insights from a Multi-Experiment Approach](https://arxiv.org/abs/2506.22402).
+It is a base-sized Transformer trained from scratch using
+the MATE method and the [GECCC dataset](https://hdl.handle.net/11234/1-4861).
+
+## Model Description
+
+- **Developed by:** [Seznam.cz](https://seznam.cz) and [Charles University, MFF, ÚFAL](https://ufal.mff.cuni.cz/)
+- **Language(s) (NLP):** Czech
+- **Model type:** subword-based encoder-decoder Transformer model
+- **Finetuned on:**
+  - first synthetic errors generated by the MATE method (see [the paper](https://arxiv.org/abs/2506.22402))
+  - then the [GECCC dataset](https://hdl.handle.net/11234/1-4861)
+- **License:** CC BY-NC-SA 4.0
+
+## Model Sources
+
+- **Repository:** https://github.com/ufal/tsd2025-gec
+- **Paper:** [Refining Czech GEC: Insights from a Multi-Experiment Approach](https://arxiv.org/abs/2506.22402)
+- **Dataset:** [GECCC dataset](https://hdl.handle.net/11234/1-4861)
+
+## Evaluation
+
+<div align="center">
+  <img src="https://github.com/ufal/tsd2025-gec/blob/main/figures/bubble_chart.svg?raw=true" width="100%" alt="Performance bubblechart" />
+</div>
+
+| Model | Parameters | GECCC F-0.5 score |
+|:------|-----------:|:-----------------:|
+| [byt5-small-geccc-mate](https://hf.co/ufal/byt5-small-geccc-mate) | 300M | 72.56 |
+| [byt5-base-geccc-mate](https://hf.co/ufal/byt5-base-geccc-mate) | 582M | 75.15 |
+| [byt5-large-geccc-mate](https://hf.co/ufal/byt5-large-geccc-mate) | 1275M | 77.01 |
+| [**transformer-base-geccc-mate**](https://hf.co/ufal/transformer-base-geccc-mate) | **65M** | **73.73** |
+
+## Uses
+
+The model can be directly used to process space-tokenized input Czech text and produce grammar-corrected Czech text.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model. Note that the input must be **space-tokenized**, i.e., every token (using the [UDPipe 1](https://ufal.mff.cuni.cz/udpipe/1) tokenizer [czech-pdt-ud-2.5-191206.udpipe](https://hdl.handle.net/11234/1-3131)) must be space-separated.
+
+```python
+tokenizer = transformers.AutoTokenizer.from_pretrained("ufal/transformer-base-geccc-mate")
+model = transformers.AutoModelForSeq2SeqLM.from_pretrained("ufal/transformer-base-geccc-mate")
+
+batch = tokenizer(["Sveřepý šakali zavile vyly na býlí mesýc .", return_tensors="pt")
+outputs = model.generate(batch.input_ids, max_length=256, num_beams=4)
+
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+```
+
+## BibTeX Citation
+
+```
+@InProceedings{10.1007/978-3-032-02551-7_7,
+  author="Pechman, Petr and Straka, Milan and Strakov{\'a}, Jana and N{\'a}plava, Jakub",
+  editor="Ek{\v{s}}tein, Kamil and Konop{\'i}k, Miloslav and Pra{\v{z}}{\'a}k, Ond{\v{r}}ej and P{\'a}rtl, Franti{\v{s}}ek",
+  title="Refining Czech GEC: Insights from a Multi-experiment Approach",
+  booktitle="Text, Speech, and Dialogue",
+  year="2026",
+  publisher="Springer Nature Switzerland",
+  address="Cham",
+  pages="64--76",
+  isbn="978-3-032-02551-7",
+  doi="10.1007/978-3-032-02551-7_7"
+}
+```

config.json

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+{
+  "activation_dropout": 0.0,
+  "activation_function": "relu",
+  "architectures": [
+    "BartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "classifier_dropout": 0.0,
+  "d_model": 512,
+  "decoder_attention_heads": 8,
+  "decoder_ffn_dim": 2048,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 6,
+  "decoder_start_token_id": 1,
+  "dropout": 0.1,
+  "encoder_attention_heads": 8,
+  "encoder_ffn_dim": 2048,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 6,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_position_embeddings": 256,
+  "model_type": "bart",
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "scale_embedding": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "decoder_start_token_id": 1,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.33.3"
+}

model.safetensors

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+oid sha256:327150a2f8aef62fe74170a91b09f3c096d8ad984297b46ec6af288866f90ba6
+size 243312640

pytorch_model.bin

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+size 243368627

special_tokens_map.json

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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tf_model.h5

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+oid sha256:08fd484ce8f0665ef2d3c18cb49229f217d65d53f14af5443cbe35e71531ea43
+size 243574672

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "errors": "replace",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "BartTokenizer",
+  "trim_offsets": true,
+  "unk_token": "[UNK]"
+}