File size: 15,974 Bytes
264da51 3f1e5fe 264da51 3f1e5fe 055fae2 3f1e5fe 5b5768e 3f1e5fe 5b5768e 3f1e5fe 5b5768e 3f1e5fe 055fae2 3f1e5fe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 |
---
license: cc-by-4.0
language:
- he
inference: false
---
# DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew
State-of-the-art language model for parsing Hebrew, released [here](https://arxiv.org/abs/2403.06970).
This is the fine-tuned model for the joint parsing of the following tasks:
- Prefix Segmentation
- Morphological Disabmgiuation
- Lexicographical Analysis (Lemmatization)
- Syntactical Parsing (Dependency-Tree)
- Named-Entity Recognition
This model was initialized from dictabert-**large**-joint and tuned on the Hebrew UD Treebank and NEMO corpora, to align the predictions of the model to the tagging methodology in those corpora.
A live demo of the `dictabert-joint` model with instant visualization of the syntax tree can be found [here](https://huggingface.co/spaces/dicta-il/joint-demo).
For a faster model, you can use the equivalent bert-tiny model for this task [here](https://huggingface.co/dicta-il/dictabert-tiny-parse).
For the bert-base models for other tasks, see [here](https://huggingface.co/collections/dicta-il/dictabert-6588e7cc08f83845fc42a18b).
---
The model currently supports 3 types of output:
1. **JSON**: The model returns a JSON object for each sentence in the input, where for each sentence we have the sentence text, the NER entities, and the list of tokens. For each token we include the output from each of the tasks.
```python
model.predict(..., output_style='json')
```
1. **UD**: The model returns the full UD output for each sentence, according to the style of the Hebrew UD Treebank.
```python
model.predict(..., output_style='ud')
```
1. **UD, in the style of IAHLT**: This model returns the full UD output, with slight modifications to match the style of IAHLT. This differences are mostly granularity of some dependency relations, how the suffix of a word is broken up, and implicit definite articles. The actual tagging behavior doesn't change.
```python
model.predict(..., output_style='iahlt_ud')
```
---
If you only need the output for one of the tasks, you can tell the model to not initialize some of the heads, for example:
```python
model = AutoModel.from_pretrained('dicta-il/dictabert-parse', trust_remote_code=True, do_lex=False)
```
The list of options are: `do_lex`, `do_syntax`, `do_ner`, `do_prefix`, `do_morph`.
---
Sample usage:
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-large-parse')
model = AutoModel.from_pretrained('dicta-il/dictabert-large-parse', trust_remote_code=True)
model.eval()
sentence = '讘砖谞转 1948 讛砖诇讬诐 讗驻专讬诐 拽讬砖讜谉 讗转 诇讬诪讜讚讬讜 讘驻讬住讜诇 诪转讻转 讜讘转讜诇讚讜转 讛讗诪谞讜转 讜讛讞诇 诇驻专住诐 诪讗诪专讬诐 讛讜诪讜专讬住讟讬讬诐'
print(model.predict([sentence], tokenizer, output_style='json')) # see below for other return formats
```
Output:
```json
[
{
"text": "讘砖谞转 1948 讛砖诇讬诐 讗驻专讬诐 拽讬砖讜谉 讗转 诇讬诪讜讚讬讜 讘驻讬住讜诇 诪转讻转 讜讘转讜诇讚讜转 讛讗诪谞讜转 讜讛讞诇 诇驻专住诐 诪讗诪专讬诐 讛讜诪讜专讬住讟讬讬诐",
"tokens": [
{
"token": "讘砖谞转",
"offsets": {
"start": 0,
"end": 4
},
"syntax": {
"word": "讘砖谞转",
"dep_head_idx": 2,
"dep_func": "obl",
"dep_head": "讛砖诇讬诐"
},
"seg": [
"讘",
"砖谞转"
],
"lex": "砖谞讛",
"morph": {
"token": "讘砖谞转",
"pos": "NOUN",
"feats": {
"Gender": "Fem",
"Number": "Sing"
},
"prefixes": [
"ADP"
],
"suffix": false
}
},
{
"token": "1948",
"offsets": {
"start": 5,
"end": 9
},
"syntax": {
"word": "1948",
"dep_head_idx": 0,
"dep_func": "compound:smixut",
"dep_head": "讘砖谞转"
},
"seg": [
"1948"
],
"lex": "1948",
"morph": {
"token": "1948",
"pos": "NUM",
"feats": {},
"prefixes": [],
"suffix": false
}
},
{
"token": "讛砖诇讬诐",
"offsets": {
"start": 10,
"end": 15
},
"syntax": {
"word": "讛砖诇讬诐",
"dep_head_idx": -1,
"dep_func": "root",
"dep_head": "讛讜诪讜专讬住讟讬讬诐"
},
"seg": [
"讛砖诇讬诐"
],
"lex": "讛砖诇讬诐",
"morph": {
"token": "讛砖诇讬诐",
"pos": "VERB",
"feats": {
"Gender": "Masc",
"Number": "Sing",
"Person": "3",
"Tense": "Past"
},
"prefixes": [],
"suffix": false
}
},
{
"token": "讗驻专讬诐",
"offsets": {
"start": 16,
"end": 21
},
"syntax": {
"word": "讗驻专讬诐",
"dep_head_idx": 2,
"dep_func": "nsubj",
"dep_head": "讛砖诇讬诐"
},
"seg": [
"讗驻专讬诐"
],
"lex": "讗驻专讬诐",
"morph": {
"token": "讗驻专讬诐",
"pos": "PROPN",
"feats": {},
"prefixes": [],
"suffix": false
}
},
{
"token": "拽讬砖讜谉",
"offsets": {
"start": 22,
"end": 27
},
"syntax": {
"word": "拽讬砖讜谉",
"dep_head_idx": 3,
"dep_func": "flat:name",
"dep_head": "讗驻专讬诐"
},
"seg": [
"拽讬砖讜谉"
],
"lex": "拽讬砖讜谉",
"morph": {
"token": "拽讬砖讜谉",
"pos": "PROPN",
"feats": {},
"prefixes": [],
"suffix": false
}
},
{
"token": "讗转",
"offsets": {
"start": 28,
"end": 30
},
"syntax": {
"word": "讗转",
"dep_head_idx": 6,
"dep_func": "case:acc",
"dep_head": "诇讬诪讜讚讬讜"
},
"seg": [
"讗转"
],
"lex": "讗转",
"morph": {
"token": "讗转",
"pos": "ADP",
"feats": {},
"prefixes": [],
"suffix": false
}
},
{
"token": "诇讬诪讜讚讬讜",
"offsets": {
"start": 31,
"end": 38
},
"syntax": {
"word": "诇讬诪讜讚讬讜",
"dep_head_idx": 2,
"dep_func": "obj",
"dep_head": "讛砖诇讬诐"
},
"seg": [
"诇讬诪讜讚讬讜"
],
"lex": "诇讬诪讜讚",
"morph": {
"token": "诇讬诪讜讚讬讜",
"pos": "NOUN",
"feats": {
"Gender": "Masc",
"Number": "Plur"
},
"prefixes": [],
"suffix": "ADP_PRON",
"suffix_feats": {
"Gender": "Masc",
"Number": "Sing",
"Person": "3"
}
}
},
{
"token": "讘驻讬住讜诇",
"offsets": {
"start": 39,
"end": 45
},
"syntax": {
"word": "讘驻讬住讜诇",
"dep_head_idx": 6,
"dep_func": "nmod",
"dep_head": "诇讬诪讜讚讬讜"
},
"seg": [
"讘",
"驻讬住讜诇"
],
"lex": "驻讬住讜诇",
"morph": {
"token": "讘驻讬住讜诇",
"pos": "NOUN",
"feats": {
"Gender": "Masc",
"Number": "Sing"
},
"prefixes": [
"ADP"
],
"suffix": false
}
},
{
"token": "诪转讻转",
"offsets": {
"start": 46,
"end": 50
},
"syntax": {
"word": "诪转讻转",
"dep_head_idx": 7,
"dep_func": "compound:smixut",
"dep_head": "讘驻讬住讜诇"
},
"seg": [
"诪转讻转"
],
"lex": "诪转讻转",
"morph": {
"token": "诪转讻转",
"pos": "NOUN",
"feats": {
"Gender": "Fem",
"Number": "Sing"
},
"prefixes": [],
"suffix": false
}
},
{
"token": "讜讘转讜诇讚讜转",
"offsets": {
"start": 51,
"end": 59
},
"syntax": {
"word": "讜讘转讜诇讚讜转",
"dep_head_idx": 7,
"dep_func": "conj",
"dep_head": "讘驻讬住讜诇"
},
"seg": [
"讜讘",
"转讜诇讚讜转"
],
"lex": "转讜诇讚讛",
"morph": {
"token": "讜讘转讜诇讚讜转",
"pos": "NOUN",
"feats": {
"Gender": "Fem",
"Number": "Plur"
},
"prefixes": [
"CCONJ",
"ADP"
],
"suffix": false
}
},
{
"token": "讛讗诪谞讜转",
"offsets": {
"start": 60,
"end": 66
},
"syntax": {
"word": "讛讗诪谞讜转",
"dep_head_idx": 9,
"dep_func": "compound:smixut",
"dep_head": "讜讘转讜诇讚讜转"
},
"seg": [
"讛",
"讗诪谞讜转"
],
"lex": "讗讜诪谞讜转",
"morph": {
"token": "讛讗诪谞讜转",
"pos": "NOUN",
"feats": {
"Gender": "Fem",
"Number": "Sing"
},
"prefixes": [
"DET"
],
"suffix": false
}
},
{
"token": "讜讛讞诇",
"offsets": {
"start": 67,
"end": 71
},
"syntax": {
"word": "讜讛讞诇",
"dep_head_idx": 2,
"dep_func": "conj",
"dep_head": "讛砖诇讬诐"
},
"seg": [
"讜",
"讛讞诇"
],
"lex": "讛讞诇",
"morph": {
"token": "讜讛讞诇",
"pos": "VERB",
"feats": {
"Gender": "Masc",
"Number": "Sing",
"Person": "3",
"Tense": "Past"
},
"prefixes": [
"CCONJ"
],
"suffix": false
}
},
{
"token": "诇驻专住诐",
"offsets": {
"start": 72,
"end": 77
},
"syntax": {
"word": "诇驻专住诐",
"dep_head_idx": 11,
"dep_func": "xcomp",
"dep_head": "讜讛讞诇"
},
"seg": [
"诇驻专住诐"
],
"lex": "驻专住诐",
"morph": {
"token": "诇驻专住诐",
"pos": "VERB",
"feats": {},
"prefixes": [],
"suffix": false
}
},
{
"token": "诪讗诪专讬诐",
"offsets": {
"start": 78,
"end": 84
},
"syntax": {
"word": "诪讗诪专讬诐",
"dep_head_idx": 12,
"dep_func": "obj",
"dep_head": "诇驻专住诐"
},
"seg": [
"诪讗诪专讬诐"
],
"lex": "诪讗诪专",
"morph": {
"token": "诪讗诪专讬诐",
"pos": "NOUN",
"feats": {
"Gender": "Masc",
"Number": "Plur"
},
"prefixes": [],
"suffix": false
}
},
{
"token": "讛讜诪讜专讬住讟讬讬诐",
"offsets": {
"start": 85,
"end": 96
},
"syntax": {
"word": "讛讜诪讜专讬住讟讬讬诐",
"dep_head_idx": 13,
"dep_func": "amod",
"dep_head": "诪讗诪专讬诐"
},
"seg": [
"讛讜诪讜专讬住讟讬讬诐"
],
"lex": "讛讜诪讜专讬住讟讬",
"morph": {
"token": "讛讜诪讜专讬住讟讬讬诐",
"pos": "ADJ",
"feats": {
"Gender": "Masc",
"Number": "Plur"
},
"prefixes": [],
"suffix": false
}
}
],
"root_idx": 2,
"ner_entities": [
{
"phrase": "1948",
"label": "TIMEX",
"start": 5,
"end": 9,
"token_start": 1,
"token_end": 1
},
{
"phrase": "讗驻专讬诐 拽讬砖讜谉",
"label": "PER",
"start": 16,
"end": 27,
"token_start": 3,
"token_end": 4
}
]
}
]
```
You can also choose to get your response in UD format:
```python
sentence = '讘砖谞转 1948 讛砖诇讬诐 讗驻专讬诐 拽讬砖讜谉 讗转 诇讬诪讜讚讬讜 讘驻讬住讜诇 诪转讻转 讜讘转讜诇讚讜转 讛讗诪谞讜转 讜讛讞诇 诇驻专住诐 诪讗诪专讬诐 讛讜诪讜专讬住讟讬讬诐'
print(model.predict([sentence], tokenizer, output_style='ud'))
```
Results:
```json
[
[
"# sent_id = 1",
"# text = 讘砖谞转 1948 讛砖诇讬诐 讗驻专讬诐 拽讬砖讜谉 讗转 诇讬诪讜讚讬讜 讘驻讬住讜诇 诪转讻转 讜讘转讜诇讚讜转 讛讗诪谞讜转 讜讛讞诇 诇驻专住诐 诪讗诪专讬诐 讛讜诪讜专讬住讟讬讬诐",
"1-2\t讘砖谞转\t_\t_\t_\t_\t_\t_\t_\t_",
"1\t讘\t讘\tADP\tADP\t_\t2\tcase\t_\t_",
"2\t砖谞转\t砖谞讛\tNOUN\tNOUN\tGender=Fem|Number=Sing\t4\tobl\t_\t_",
"3\t1948\t1948\tNUM\tNUM\t\t2\tcompound:smixut\t_\t_",
"4\t讛砖诇讬诐\t讛砖诇讬诐\tVERB\tVERB\tGender=Masc|Number=Sing|Person=3|Tense=Past\t0\troot\t_\t_",
"5\t讗驻专讬诐\t讗驻专讬诐\tPROPN\tPROPN\t\t4\tnsubj\t_\t_",
"6\t拽讬砖讜谉\t拽讬砖讜谉\tPROPN\tPROPN\t\t5\tflat:name\t_\t_",
"7\t讗转\t讗转\tADP\tADP\t\t8\tcase:acc\t_\t_",
"8-10\t诇讬诪讜讚讬讜\t_\t_\t_\t_\t_\t_\t_\t_",
"8\t诇讬诪讜讚_\t诇讬诪讜讚\tNOUN\tNOUN\tGender=Masc|Number=Plur\t4\tobj\t_\t_",
"9\t_砖诇_\t砖诇\tADP\tADP\t_\t10\tcase\t_\t_",
"10\t_讛讜讗\t讛讜讗\tPRON\tPRON\tGender=Masc|Number=Sing|Person=3\t8\tnmod:poss\t_\t_",
"11-12\t讘驻讬住讜诇\t_\t_\t_\t_\t_\t_\t_\t_",
"11\t讘\t讘\tADP\tADP\t_\t12\tcase\t_\t_",
"12\t驻讬住讜诇\t驻讬住讜诇\tNOUN\tNOUN\tGender=Masc|Number=Sing\t8\tnmod\t_\t_",
"13\t诪转讻转\t诪转讻转\tNOUN\tNOUN\tGender=Fem|Number=Sing\t12\tcompound:smixut\t_\t_",
"14-16\t讜讘转讜诇讚讜转\t_\t_\t_\t_\t_\t_\t_\t_",
"14\t讜\t讜\tCCONJ\tCCONJ\t_\t16\tcc\t_\t_",
"15\t讘\t讘\tADP\tADP\t_\t16\tcase\t_\t_",
"16\t转讜诇讚讜转\t转讜诇讚讛\tNOUN\tNOUN\tGender=Fem|Number=Plur\t12\tconj\t_\t_",
"17-18\t讛讗诪谞讜转\t_\t_\t_\t_\t_\t_\t_\t_",
"17\t讛\t讛\tDET\tDET\t_\t18\tdet\t_\t_",
"18\t讗诪谞讜转\t讗讜诪谞讜转\tNOUN\tNOUN\tGender=Fem|Number=Sing\t16\tcompound:smixut\t_\t_",
"19-20\t讜讛讞诇\t_\t_\t_\t_\t_\t_\t_\t_",
"19\t讜\t讜\tCCONJ\tCCONJ\t_\t20\tcc\t_\t_",
"20\t讛讞诇\t讛讞诇\tVERB\tVERB\tGender=Masc|Number=Sing|Person=3|Tense=Past\t4\tconj\t_\t_",
"21\t诇驻专住诐\t驻专住诐\tVERB\tVERB\t\t20\txcomp\t_\t_",
"22\t诪讗诪专讬诐\t诪讗诪专\tNOUN\tNOUN\tGender=Masc|Number=Plur\t21\tobj\t_\t_",
"23\t讛讜诪讜专讬住讟讬讬诐\t讛讜诪讜专讬住讟讬\tADJ\tADJ\tGender=Masc|Number=Plur\t22\tamod\t_\t_"
]
]
```
## Citation
If you use DictaBERT-large-parse in your research, please cite ```MRL Parsing without Tears: The Case of Hebrew```
**BibTeX:**
```bibtex
@misc{shmidman2024mrl,
title={MRL Parsing Without Tears: The Case of Hebrew},
author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel and Reut Tsarfaty},
year={2024},
eprint={2403.06970},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
Shield: [![CC BY 4.0][cc-by-shield]][cc-by]
This work is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by].
[![CC BY 4.0][cc-by-image]][cc-by]
[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg
|