README.md

---
language:
- tr
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
config_names:
- BOUN
- IMST
dataset_info:
- config_name: BOUN
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: tokens
    list:
      dtype: string
  - name: upos
    list:
      dtype: string
  - name: heads
    list:
      dtype: int32
  - name: rels
    list:
      dtype: string
  - name: feats
    list:
      dtype: string
  - name: feats_dict_json
    list:
      dtype: string
- config_name: IMST
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: tokens
    list:
      dtype: string
  - name: upos
    list:
      dtype: string
  - name: heads
    list:
      dtype: int32
  - name: rels
    list:
      dtype: string
  - name: feats
    list:
      dtype: string
  - name: feats_dict_json
    list:
      dtype: string
  splits:
  - name: train
    num_bytes: 116892
    num_examples: 3435
  - name: validation
    num_bytes: 116892
    num_examples: 1100
  - name: test
    num_bytes: 116892
    num_examples: 1100
configs:
- config_name: BOUN
  data_files:
  - split: train
    path: BOUN/train.jsonl
  - split: test
    path: BOUN/test.jsonl
  - split: validation
    path: BOUN/dev.jsonl
- config_name: IMST
  data_files:
  - split: train
    path: IMST/train.jsonl
  - split: test
    path: IMST/test.jsonl
  - split: validation
    path: IMST/dev.jsonl
---



<img src="https://raw.githubusercontent.com/turkish-nlp-suite/.github/main/profile/TreeBench.png"  width="30%" height="30%">

# Turkish Treebank Benchmarking 
This is the repo for Turkish treebank benchmarking, namely evaluating Tranformer models on POS-Dep-Morph task. 
For the data, we used two treebank, [IMST](https://github.com/UniversalDependencies/UD_Turkish-IMST) and [BOUN](https://github.com/UniversalDependencies/UD_Turkish-BOUN). We converted conllu format to json lines for being compatible to HF dataset formats.

Here are treebank sizes at a glance: 

| Dataset | train lines | dev lines | test lines| 
|---|---|---|---|
| BOUN  | 7803  | 979  | 979  |
| IMST  | 3435  | 1100 | 1100  |




A typical instance from the dataset looks like:

```
{
  "id": "ins_1267",
  "tokens": [
    "Rüzgâr",
    "yine",
    "güçlü",
    "esiyor",
    "du",
    "."
  ],
  "upos": [
    "NOUN",
    "ADV",
    "ADV",
    "VERB",
    "AUX",
    "PUNCT"
  ],
  "heads": [
    4,
    4,
    4,
    0,
    4,
    4
  ],
  "rels": [
    "nsubj",
    "advmod",
    "advmod",
    "root",
    "cop",
    "punct"
  ],
  "feats": [
    "Case=Nom|Number=Sing|Person=3",
    "_",
    "_",
    "Aspect=Imp|Polarity=Pos|VerbForm=Part",
    "Aspect=Perf|Evident=Fh|Number=Sing|Person=3|Tense=Past",
    "_"
  ],
  "text": "Rüzgâr yine güçlü esiyor du .",
  "feats_dict_json": [
    "{\"Case\":\"Nom\",\"Number\":\"Sing\",\"Person\":\"3\"}",
    "{}",
    "{}",
    "{\"Aspect\":\"Imp\",\"Polarity\":\"Pos\",\"VerbForm\":\"Part\"}",
    "{\"Aspect\":\"Perf\",\"Evident\":\"Fh\",\"Number\":\"Sing\",\"Person\":\"3\",\"Tense\":\"Past\"}",
    "{}"
  ]
}
```

## Benchmarking
Benchmarking is done by scripts on accompanying [Github repo](https://github.com/turkish-nlp-suite/Treebank-Benchmarking). Please proceed to this repo for running the experiments.
Here are the benchmarking results for BERTurk with our scripts: 



| Metric | BOUN | IMST |
|---|---:|---:|
| pos_acc | 0.9263 | 0.9377 |
| uas | 0.8151 | 0.7680 |
| las | 0.7459 | 0.6960 |
| morph_Abbr_acc | 0.4657 | 0.6705 |
| morph_Aspect_acc | 0.1141 | 0.1152 |
| morph_Case_acc | 0.1196 | 0.0586 |
| morph_Echo_acc | 0.4261 | 0.4875 |
| morph_Evident_acc | 0.3072 | 0.3953 |
| morph_Mood_acc | 0.0654 | 0.0651 |
| morph_NumType_acc | 0.2694 | 0.2991 |
| morph_Number_acc | 0.3986 | 0.4782 |
| morph_Number[psor]_acc | 0.4348 | 0.2333 |
| morph_Person_acc | 0.4021 | 0.4726 |
| morph_Person[psor]_acc | 0.2490 | 0.0671 |
| morph_Polarity_acc | 0.3350 | 0.1674 |
| morph_PronType_acc | 0.1535 | 0.2680 |
| morph_Reflex_acc | 0.5620 | 0.7051 |
| morph_Tense_acc | 0.2149 | 0.1241 |
| morph_Typo_acc | 0.5081 | — |
| morph_VerbForm_acc | 0.4912 | 0.2364 |
| morph_Voice_acc | 0.0201 | 0.2602 |
| morph_Polite_acc | — | 0.1436 |
| morph_micro_acc | 0.3076 | 0.2915 |

Notes:
- `—` means that metric wasn’t present in that dataset’s reported results (e.g., `morph_Typo_acc` only in BOUN; `morph_Polite_acc` only in IMST).



## Acknowledgments
This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC), like most of our projects. Many thanks to TRC team once again.