---
tags:
- multilingual
datasets:
- xquad
---
# xlm-roberta-base for multilingual QA
# Overview
**Language Model**: xlm-roberta-base \
**Downstream task**: Extractive QA \
**Training data**: [XQuAD](https://github.com/deepmind/xquad)\
**Testing Data**: [XQuAD](https://github.com/deepmind/xquad)

# Hyperparameters
```python
batch_size = 40
n_epochs = 10
max_seq_len = 384
doc_stride = 128
learning_rate = 3e-5
```
# Performance
Evaluated on held-out test set from XQuAD
```python
"exact_match": 79.44756554307116,
"f1": 89.79318021513376,
"test_samples": 2307
```

# Usage

## In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "alon-albalak/xlm-roberta-base-xquad"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Why is model conversion important?',
    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## In FARM
```python
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.tokenization import Tokenizer
from farm.infer import QAInferencer

model_name = "alon-albalak/xlm-roberta-base-xquad"

# a) Get predictions
nlp = QAInferencer.load(model_name)
QA_input = [{"questions": ["Why is model conversion important?"],
             "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)

# b) Load model & tokenizer
model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
tokenizer = Tokenizer.load(model_name)
```

## In Haystack

```python
reader = FARMReader(model_name_or_path="alon-albalak/xlm-roberta-base-xquad")
# or
reader = TransformersReader(model="alon-albalak/xlm-roberta-base-xquad",tokenizer="alon-albalak/xlm-roberta-base-xquad")
```

Usage instructions for FARM and Haystack were adopted from https://huggingface.co/deepset/xlm-roberta-large-squad2


---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- albert-base-v2
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "dont play [MASK] leh"
- text: "die [MASK] must try"
---

# Model name

SingBert Lite - Bert for Singlish (SG) and Manglish (MY).

## Model description

Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the lite-version, which was initialized from [Albert base v2](https://github.com/google-research/albert#albert), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.

## Intended uses & limitations

#### How to use

```python
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert-lite-sg')
>>> nlp("die [MASK] must try")

[{'sequence': '[CLS] die die must try[SEP]',
  'score': 0.7731555700302124,
  'token': 1327,
  'token_str': '▁die'},
 {'sequence': '[CLS] die also must try[SEP]',
  'score': 0.04763784259557724,
  'token': 67,
  'token_str': '▁also'},
 {'sequence': '[CLS] die still must try[SEP]',
  'score': 0.01859409362077713,
  'token': 174,
  'token_str': '▁still'},
 {'sequence': '[CLS] die u must try[SEP]',
  'score': 0.015824034810066223,
  'token': 287,
  'token_str': '▁u'},
 {'sequence': '[CLS] die is must try[SEP]',
  'score': 0.011271446943283081,
  'token': 25,
  'token_str': '▁is'}]

>>> nlp("dont play [MASK] leh")

[{'sequence': '[CLS] dont play play leh[SEP]',
  'score': 0.4365769624710083,
  'token': 418,
  'token_str': '▁play'},
 {'sequence': '[CLS] dont play punk leh[SEP]',
  'score': 0.06880936771631241,
  'token': 6769,
  'token_str': '▁punk'},
 {'sequence': '[CLS] dont play game leh[SEP]',
  'score': 0.051739856600761414,
  'token': 250,
  'token_str': '▁game'},
 {'sequence': '[CLS] dont play games leh[SEP]',
  'score': 0.045703962445259094,
  'token': 466,
  'token_str': '▁games'},
 {'sequence': '[CLS] dont play around leh[SEP]',
  'score': 0.013458190485835075,
  'token': 140,
  'token_str': '▁around'}]

>>> nlp("catch no [MASK]")

[{'sequence': '[CLS] catch no ball[SEP]',
  'score': 0.6197211146354675,
  'token': 1592,
  'token_str': '▁ball'},
 {'sequence': '[CLS] catch no balls[SEP]',
  'score': 0.08441998809576035,
  'token': 7152,
  'token_str': '▁balls'},
 {'sequence': '[CLS] catch no joke[SEP]',
  'score': 0.0676785409450531,
  'token': 8186,
  'token_str': '▁joke'},
 {'sequence': '[CLS] catch no?[SEP]',
  'score': 0.040638409554958344,
  'token': 60,
  'token_str': '?'},
 {'sequence': '[CLS] catch no one[SEP]',
  'score': 0.03546864539384842,
  'token': 53,
  'token_str': '▁one'}]

>>> nlp("confirm plus [MASK]")

[{'sequence': '[CLS] confirm plus chop[SEP]',
  'score': 0.9608421921730042,
  'token': 17144,
  'token_str': '▁chop'},
 {'sequence': '[CLS] confirm plus guarantee[SEP]',
  'score': 0.011784233152866364,
  'token': 9120,
  'token_str': '▁guarantee'},
 {'sequence': '[CLS] confirm plus confirm[SEP]',
  'score': 0.010571340098977089,
  'token': 10265,
  'token_str': '▁confirm'},
 {'sequence': '[CLS] confirm plus egg[SEP]',
  'score': 0.0033525123726576567,
  'token': 6387,
  'token_str': '▁egg'},
 {'sequence': '[CLS] confirm plus bet[SEP]',
  'score': 0.0008760977652855217,
  'token': 5676,
  'token_str': '▁bet'}]

```

Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AlbertTokenizer, AlbertModel
tokenizer = AlbertTokenizer.from_pretrained('zanelim/singbert-lite-sg')
model = AlbertModel.from_pretrained("zanelim/singbert-lite-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:
```python
from transformers import AlbertTokenizer, TFAlbertModel
tokenizer = AlbertTokenizer.from_pretrained("zanelim/singbert-lite-sg")
model = TFAlbertModel.from_pretrained("zanelim/singbert-lite-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

#### Limitations and bias
This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.

## Training data
Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.

## Training procedure

Initialized with [albert base v2](https://github.com/google-research/albert#albert) vocab and checkpoints (pre-trained weights).

Pre-training was further finetuned on training data with the following hyperparameters
* train_batch_size: 4096
* max_seq_length: 128
* num_train_steps: 125000
* num_warmup_steps: 5000
* learning_rate: 0.00176
* hardware: TPU v3-8


---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- bert-base-uncased
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "kopi c siew [MASK]"
- text: "die [MASK] must try"
---

# Model name

SingBert - Bert for Singlish (SG) and Manglish (MY).

## Model description

[BERT base uncased](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.

## Intended uses & limitations

#### How to use

```python
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert')
>>> nlp("kopi c siew [MASK]")

[{'sequence': '[CLS] kopi c siew dai [SEP]',
  'score': 0.5092713236808777,
  'token': 18765,
  'token_str': 'dai'},
 {'sequence': '[CLS] kopi c siew mai [SEP]',
  'score': 0.3515934646129608,
  'token': 14736,
  'token_str': 'mai'},
 {'sequence': '[CLS] kopi c siew bao [SEP]',
  'score': 0.05576375499367714,
  'token': 25945,
  'token_str': 'bao'},
 {'sequence': '[CLS] kopi c siew. [SEP]',
  'score': 0.006019321270287037,
  'token': 1012,
  'token_str': '.'},
 {'sequence': '[CLS] kopi c siew sai [SEP]',
  'score': 0.0038361591286957264,
  'token': 18952,
  'token_str': 'sai'}]

>>> nlp("one teh c siew dai, and one kopi [MASK].")

[{'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]',
  'score': 0.6176503300666809,
  'token': 1039,
  'token_str': 'c'},
 {'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]',
  'score': 0.21094971895217896,
  'token': 1051,
  'token_str': 'o'},
 {'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]',
  'score': 0.13027705252170563,
  'token': 1012,
  'token_str': '.'},
 {'sequence': '[CLS] one teh c siew dai, and one kopi! [SEP]',
  'score': 0.004680239595472813,
  'token': 999,
  'token_str': '!'},
 {'sequence': '[CLS] one teh c siew dai, and one kopi w [SEP]',
  'score': 0.002034128177911043,
  'token': 1059,
  'token_str': 'w'}]

>>> nlp("dont play [MASK] leh")

[{'sequence': '[CLS] dont play play leh [SEP]',
  'score': 0.9281464219093323,
  'token': 2377,
  'token_str': 'play'},
 {'sequence': '[CLS] dont play politics leh [SEP]',
  'score': 0.010990909300744534,
  'token': 4331,
  'token_str': 'politics'},
 {'sequence': '[CLS] dont play punk leh [SEP]',
  'score': 0.005583590362221003,
  'token': 7196,
  'token_str': 'punk'},
 {'sequence': '[CLS] dont play dirty leh [SEP]',
  'score': 0.0025784350000321865,
  'token': 6530,
  'token_str': 'dirty'},
 {'sequence': '[CLS] dont play cheat leh [SEP]',
  'score': 0.0025066907983273268,
  'token': 21910,
  'token_str': 'cheat'}]

>>> nlp("catch no [MASK]")

[{'sequence': '[CLS] catch no ball [SEP]',
  'score': 0.7922210693359375,
  'token': 3608,
  'token_str': 'ball'},
 {'sequence': '[CLS] catch no balls [SEP]',
  'score': 0.20503675937652588,
  'token': 7395,
  'token_str': 'balls'},
 {'sequence': '[CLS] catch no tail [SEP]',
  'score': 0.0006608376861549914,
  'token': 5725,
  'token_str': 'tail'},
 {'sequence': '[CLS] catch no talent [SEP]',
  'score': 0.0002158183924620971,
  'token': 5848,
  'token_str': 'talent'},
 {'sequence': '[CLS] catch no prisoners [SEP]',
  'score': 5.3481446229852736e-05,
  'token': 5895,
  'token_str': 'prisoners'}]

>>> nlp("confirm plus [MASK]")

[{'sequence': '[CLS] confirm plus chop [SEP]',
  'score': 0.992355227470398,
  'token': 24494,
  'token_str': 'chop'},
 {'sequence': '[CLS] confirm plus one [SEP]',
  'score': 0.0037301010452210903,
  'token': 2028,
  'token_str': 'one'},
 {'sequence': '[CLS] confirm plus minus [SEP]',
  'score': 0.0014284878270700574,
  'token': 15718,
  'token_str': 'minus'},
 {'sequence': '[CLS] confirm plus 1 [SEP]',
  'score': 0.0011354683665558696,
  'token': 1015,
  'token_str': '1'},
 {'sequence': '[CLS] confirm plus chopped [SEP]',
  'score': 0.0003804611915256828,
  'token': 24881,
  'token_str': 'chopped'}]

>>> nlp("die [MASK] must try")

[{'sequence': '[CLS] die die must try [SEP]',
  'score': 0.9552758932113647,
  'token': 3280,
  'token_str': 'die'},
 {'sequence': '[CLS] die also must try [SEP]',
  'score': 0.03644804656505585,
  'token': 2036,
  'token_str': 'also'},
 {'sequence': '[CLS] die liao must try [SEP]',
  'score': 0.003282855963334441,
  'token': 727,
  'token_str': 'liao'},
 {'sequence': '[CLS] die already must try [SEP]',
  'score': 0.0004937972989864647,
  'token': 2525,
  'token_str': 'already'},
 {'sequence': '[CLS] die hard must try [SEP]',
  'score': 0.0003659659414552152,
  'token': 2524,
  'token_str': 'hard'}]

```

Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('zanelim/singbert')
model = BertModel.from_pretrained("zanelim/singbert")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained("zanelim/singbert")
model = TFBertModel.from_pretrained("zanelim/singbert")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

#### Limitations and bias
This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.

## Training data
Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.

## Training procedure

Initialized with [bert base uncased](https://github.com/google-research/bert#pre-trained-models) vocab and checkpoints (pre-trained weights).
Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab.

Pre-training was further finetuned on training data with the following hyperparameters
* train_batch_size: 512
* max_seq_length: 128
* num_train_steps: 300000
* num_warmup_steps: 5000
* learning_rate: 2e-5
* hardware: TPU v3-8


---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- bert-large-uncased
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "kopi c siew [MASK]"
- text: "die [MASK] must try"
---

# Model name

SingBert Large - Bert for Singlish (SG) and Manglish (MY).

## Model description

Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the large version, which was initialized from [BERT large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.

## Intended uses & limitations

#### How to use

```python
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert-large-sg')
>>> nlp("kopi c siew [MASK]")

[{'sequence': '[CLS] kopi c siew dai [SEP]',
  'score': 0.9003700017929077,
  'token': 18765,
  'token_str': 'dai'},
 {'sequence': '[CLS] kopi c siew mai [SEP]',
  'score': 0.0779474675655365,
  'token': 14736,
  'token_str': 'mai'},
 {'sequence': '[CLS] kopi c siew. [SEP]',
  'score': 0.0032227332703769207,
  'token': 1012,
  'token_str': '.'},
 {'sequence': '[CLS] kopi c siew bao [SEP]',
  'score': 0.0017727474914863706,
  'token': 25945,
  'token_str': 'bao'},
 {'sequence': '[CLS] kopi c siew peng [SEP]',
  'score': 0.0012526646023616195,
  'token': 26473,
  'token_str': 'peng'}]

>>> nlp("one teh c siew dai, and one kopi [MASK]")

[{'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]',
  'score': 0.5249741077423096,
  'token': 1012,
  'token_str': '.'},
 {'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]',
  'score': 0.27349168062210083,
  'token': 1051,
  'token_str': 'o'},
 {'sequence': '[CLS] one teh c siew dai, and one kopi peng [SEP]',
  'score': 0.057190295308828354,
  'token': 26473,
  'token_str': 'peng'},
 {'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]',
  'score': 0.04022320732474327,
  'token': 1039,
  'token_str': 'c'},
 {'sequence': '[CLS] one teh c siew dai, and one kopi? [SEP]',
  'score': 0.01191170234233141,
  'token': 1029,
  'token_str': '?'}]

>>> nlp("die [MASK] must try")

[{'sequence': '[CLS] die die must try [SEP]',
  'score': 0.9921030402183533,
  'token': 3280,
  'token_str': 'die'},
 {'sequence': '[CLS] die also must try [SEP]',
  'score': 0.004993876442313194,
  'token': 2036,
  'token_str': 'also'},
 {'sequence': '[CLS] die liao must try [SEP]',
  'score': 0.000317625846946612,
  'token': 727,
  'token_str': 'liao'},
 {'sequence': '[CLS] die still must try [SEP]',
  'score': 0.0002260878391098231,
  'token': 2145,
  'token_str': 'still'},
 {'sequence': '[CLS] die i must try [SEP]',
  'score': 0.00016935862367972732,
  'token': 1045,
  'token_str': 'i'}]

>>> nlp("dont play [MASK] leh")

[{'sequence': '[CLS] dont play play leh [SEP]',
  'score': 0.9079819321632385,
  'token': 2377,
  'token_str': 'play'},
 {'sequence': '[CLS] dont play punk leh [SEP]',
  'score': 0.006846973206847906,
  'token': 7196,
  'token_str': 'punk'},
 {'sequence': '[CLS] dont play games leh [SEP]',
  'score': 0.004041737411171198,
  'token': 2399,
  'token_str': 'games'},
 {'sequence': '[CLS] dont play politics leh [SEP]',
  'score': 0.003728888463228941,
  'token': 4331,
  'token_str': 'politics'},
 {'sequence': '[CLS] dont play cheat leh [SEP]',
  'score': 0.0032805048394948244,
  'token': 21910,
  'token_str': 'cheat'}]

>>> nlp("confirm plus [MASK]")

{'sequence': '[CLS] confirm plus chop [SEP]',
  'score': 0.9749826192855835,
  'token': 24494,
  'token_str': 'chop'},
 {'sequence': '[CLS] confirm plus chopped [SEP]',
  'score': 0.017554156482219696,
  'token': 24881,
  'token_str': 'chopped'},
 {'sequence': '[CLS] confirm plus minus [SEP]',
  'score': 0.002725469646975398,
  'token': 15718,
  'token_str': 'minus'},
 {'sequence': '[CLS] confirm plus guarantee [SEP]',
  'score': 0.000900257145985961,
  'token': 11302,
  'token_str': 'guarantee'},
 {'sequence': '[CLS] confirm plus one [SEP]',
  'score': 0.0004384620988275856,
  'token': 2028,
  'token_str': 'one'}]

>>> nlp("catch no [MASK]")

[{'sequence': '[CLS] catch no ball [SEP]',
  'score': 0.9381157159805298,
  'token': 3608,
  'token_str': 'ball'},
 {'sequence': '[CLS] catch no balls [SEP]',
  'score': 0.060842301696538925,
  'token': 7395,
  'token_str': 'balls'},
 {'sequence': '[CLS] catch no fish [SEP]',
  'score': 0.00030917322146706283,
  'token': 3869,
  'token_str': 'fish'},
 {'sequence': '[CLS] catch no breath [SEP]',
  'score': 7.552534952992573e-05,
  'token': 3052,
  'token_str': 'breath'},
 {'sequence': '[CLS] catch no tail [SEP]',
  'score': 4.208395694149658e-05,
  'token': 5725,
  'token_str': 'tail'}]

```

Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('zanelim/singbert-large-sg')
model = BertModel.from_pretrained("zanelim/singbert-large-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained("zanelim/singbert-large-sg")
model = TFBertModel.from_pretrained("zanelim/singbert-large-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

#### Limitations and bias
This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.

## Training data
Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.

## Training procedure

Initialized with [bert large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models) vocab and checkpoints (pre-trained weights).
Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab.

Pre-training was further finetuned on training data with the following hyperparameters
* train_batch_size: 512
* max_seq_length: 128
* num_train_steps: 300000
* num_warmup_steps: 5000
* learning_rate: 2e-5
* hardware: TPU v3-8


---
thumbnail: https://huggingface.co/front/thumbnails/google.png

license: apache-2.0
---

BERT Miniatures
===

This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).

We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher.

Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below:

|   |H=128|H=256|H=512|H=768|
|---|:---:|:---:|:---:|:---:|
| **L=2**  |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]|
| **L=4**  |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]|
| **L=6**  |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]|
| **L=8**  |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]|
| **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]|
| **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]|

Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model.

Here are the corresponding GLUE scores on the test set:

|Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX|
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0|
|BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1|
|BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6|
|BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5|

For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs:
- batch sizes: 8, 16, 32, 64, 128
- learning rates: 3e-4, 1e-4, 5e-5, 3e-5

If you use these models, please cite the following paper:

```
@article{turc2019,
  title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
  author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1908.08962v2 },
  year={2019}
}
```

[2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2
[2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4
[2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8
[2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12
[4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2
[4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4
[4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8
[4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12
[6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2
[6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4
[6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8
[6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12
[8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2
[8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4
[8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8
[8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12
[10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2
[10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4
[10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8
[10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12
[12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2
[12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4
[12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8
[12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12


from transformers import GPTNeoForCausalLM, GPT2Tokenizer
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")

prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
...          "previously unexplored valley, in the Andes Mountains. Even more surprising to the " \
...          "researchers was the fact that the unicorns spoke perfect English."

input_ids = tokenizer(prompt, return_tensors="pt").input_ids

gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=100,)
gen_text = tokenizer.batch_decode(gen_tokens)[0]


---
language:
- de
- en
tags:
- translation
- pytorch
license: mit
datasets:
- WMT
metrics:
- bleu
---
# OpenNMT-py-English-German-Transformer
[OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.
OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for English to German translation.

- Configuration: Base Transformer configuration with [standard training options](http://opennmt.net/OpenNMT-py/FAQ.html#how-do-i-use-the-transformer-model-do-you-support-multi-gpu)
- Data: WMT with shared SentencePiece model
- BLEU:
    - newstest2014 = 26.89
    - newstest2017 = 28.09


---
language:
- de
- en
tags:
- translation
- pytorch
license: mit
datasets:
- IWSLT ‘14 DE-EN
metrics:
- bleu
---
# OpenNMT-py-English-German-Transformer
[OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.
OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for German to English translation.

- Configuration: 2-layer BiLSTM with hidden size 500 trained for 20 epochs
- Data: IWSLT ‘14 DE-EN
- BLEU: 30.33


---
language:
- en
- es
- eu
---

# IXAmBERT base cased

This is a multilingual language pretrained for English, Spanish and Basque. The training corpora is composed by the English, Spanish and Basque Wikipedias, together with Basque crawled news articles from online newspapers. The model has been successfully used to transfer knowledge from English to Basque in a conversational QA system, as reported in the paper [Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.55.pdf). In the paper, IXAmBERT performed better than mBERT when transferring knowledge from English to Basque, as shown in the following Table:

| Model              | Zero-shot | Transfer learning |
|--------------------|-----------|-------------------|
| Baseline           |      28.7 |              28.7 |
| mBERT              |      31.5 |              37.4 |
| IXAmBERT           |      38.9 |          **41.2** |
| mBERT + history    |      33.3 |              28.7 |
| IXAmBERT + history |  **40.7** |              40.0 |

This Table shows the results on a Basque CQA dataset. *Zero-shot* means that the model is fine-tuned using using QuaC, an English CQA dataset. In the *Transfer Learning* setting the model is first fine-tuned on QuaC, and then on a Basque CQA dataset.

If using this model, please cite the following paper:
```
@inproceedings{otegi2020conversational,
  title={Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque},
  author={Otegi, Arantxa and Agirre, Aitor and Campos, Jon Ander and Soroa, Aitor and Agirre, Eneko},
  booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
  pages={436--442},
  year={2020}
}
```


---
language: en
---

# SciBERT-SQuAD-QuAC

This is the [SciBERT language representation model](https://huggingface.co/allenai/scibert_scivocab_uncased) fine tuned for Question Answering. SciBERT is a pre-trained language model based on BERT that has been trained on a large corpus of scientific text. When fine tuning for Question Answering we combined [SQuAD2.0](https://www.aclweb.org/anthology/P18-2124/) and [QuAC](https://arxiv.org/abs/1808.07036) datasets.

If using this model, please cite the following paper:
```
@inproceedings{otegi-etal-2020-automatic,
    title = "Automatic Evaluation vs. User Preference in Neural Textual {Q}uestion{A}nswering over {COVID}-19 Scientific Literature",
    author = "Otegi, Arantxa  and
      Campos, Jon Ander  and
      Azkune, Gorka  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.15",
    doi = "10.18653/v1/2020.nlpcovid19-2.15",
}
```


---
language: eu
---

# BERTeus base cased

This is the Basque language pretrained model presented in [Give your Text Representation Models some Love: the Case for Basque](https://arxiv.org/pdf/2004.00033.pdf). This model has been trained on a Basque corpus comprising Basque crawled news articles from online newspapers and the Basque Wikipedia. The training corpus contains 224.6 million tokens, of which 35 million come from the Wikipedia.

BERTeus has been tested on four different downstream tasks for Basque: part-of-speech (POS) tagging, named entity recognition (NER), sentiment analysis and topic classification; improving the state of the art for all tasks. See summary of results below:

| Downstream task | BERTeus | mBERT | Previous SOTA |
| --------------- | ------- | ------| ------------- |
| Topic Classification	  | **76.77**   | 68.42 | 63.00 	    |
| Sentiment    	  | **78.10**   | 71.02 | 74.02 	    |
| POS   	  | **97.76**   | 96.37 | 96.10 	    |
| NER    	  | **87.06**   | 81.52 | 76.72 	    |

If using this model, please cite the following paper:
```
@inproceedings{agerri2020give,
  title={Give your Text Representation Models some Love: the Case for Basque},
  author={Rodrigo Agerri and I{\~n}aki San Vicente and Jon Ander Campos and Ander Barrena and Xabier Saralegi and Aitor Soroa and Eneko Agirre},
  booktitle={Proceedings of the 12th International Conference on Language Resources and Evaluation},
  year={2020}
}
```


---
tags:
- spacy
- token-classification
language:
- pt
license: cc-by-sa-4.0
model-index:
- name: pt_core_news_md
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.8951731436
    - name: NER Recall
      type: recall
      value: 0.8989431838
    - name: NER F Score
      type: f_score
      value: 0.8970542027
  - task:
      name: POS
      type: token-classification
    metrics:
    - name: POS Accuracy
      type: accuracy
      value: 0.8947889836
  - task:
      name: SENTER
      type: token-classification
    metrics:
    - name: SENTER Precision
      type: precision
      value: 0.9384819571
    - name: SENTER Recall
      type: recall
      value: 0.9612742036
    - name: SENTER F Score
      type: f_score
      value: 0.9497413558
  - task:
      name: UNLABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Unlabeled Dependencies Accuracy
      type: accuracy
      value: 0.8974935177
  - task:
      name: LABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Labeled Dependencies Accuracy
      type: accuracy
      value: 0.8974935177
---
### Details: https://spacy.io/models/pt#pt_core_news_md

Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.

| Feature | Description |
| --- | --- |
| **Name** | `pt_core_news_md` |
| **Version** | `3.2.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | [Lemmatization Lists](https://github.com/michmech/lemmatization-lists/) (Michal Měchura)
[UD Portuguese Bosque v2.8](https://github.com/UniversalDependencies/UD_Portuguese-Bosque) (Rademaker, Alexandre; Freitas, Cláudia; de Souza, Elvis; Silveira, Aline; Cavalcanti, Tatiana; Evelyn, Wograine; Rocha, Luisa; Soares-Bastos, Isabela; Bick, Eckhard; Chalub, Fabricio; Paulino-Passos, Guilherme; Real, Livy; de Paiva, Valeria; Zeman, Daniel; Popel, Martin; Mareček, David; Silveira, Natalia; Martins, André)
[WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran)
[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |

### Label Scheme

View label scheme (591 labels for 4 components)

| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=PUNCT`, `NumType=Card\|POS=NUM`, `POS=ADV`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=ADP`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=X`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=CCONJ`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=VERB\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Number=Plur\|POS=AUX\|Person=3\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Art`, `POS=VERB\|VerbForm=Part`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `NumType=Ord\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `POS=AUX\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Number=Sing\|POS=DET\|PronType=Art`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Number=Plur\|POS=VERB\|Person=3\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Definite=Def\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=AUX`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `POS=INTJ`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=NOUN\|Voice=Pass`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=AUX\|Person=3\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=NUM`, `Number=Sing\|POS=NOUN`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PART`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=ADV`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Card\|POS=DET`, `Number=Plur\|POS=VERB\|Person=1\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Gender=Masc\|POS=ADJ`, `POS=NOUN`, `POS=AUX\|VerbForm=Ger`, `Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Prs`, `POS=VERB\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=PROPN\|PronType=Art`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADV\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=X`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=SCONJ`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Gender=Fem\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `POS=ADP\|PronType=Dem`, `Definite=Def\|Gender=Fem\|POS=ADP\|PronType=Art`, `POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `POS=DET`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Art`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=AUX\|Person=1\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=2\|PronType=Prs\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem,Masc\|Number=Sing\|POS=PROPN`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=NUM`, `POS=PRON\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `POS=SYM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=X`, `Case=Dat\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Sets\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|POS=AUX\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Number=Sing\|POS=VERB`, `Number=Sing\|POS=DET`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `NumType=Mult\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Ind\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Rel`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|POS=ADP\|PronType=Art`, `Gender=Masc\|POS=NOUN`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NOUN`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=SCONJ\|PronType=Art`, `POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|PronType=Tot`, `Number=Sing\|POS=DET\|PronType=Rel`, `Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Range\|POS=NUM`, `Case=Dat\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Dem`, `NumType=Frac\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Ind`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Rel`, `Mood=Cnd\|POS=VERB\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf\|Voice=Pass`, `POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Number=Sing\|POS=X`, `POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Int`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|POS=AUX\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Fem\|POS=AUX\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Fem\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=X`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Fem\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=NUM`, `Number=Plur\|POS=PROPN`, `Case=Dat\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=VERB`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADJ\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `POS=DET\|PronType=Ind`, `POS=SCONJ\|VerbForm=Ger`, `Mood=Cnd\|Number=Sing\|POS=VERB\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=ADP`, `Gender=Fem\|Number=Sing\|POS=VERB`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Fem\|POS=PRON\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|POS=PROPN`, `Gender=Fem\|Number=Plur\|POS=DET`, `Gender=Masc\|Number=Plur\|POS=PRON`, `Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `NumType=Ord\|POS=NUM`, `POS=DET\|PronType=Int`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `POS=PART`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Inf`, `NumType=Card\|POS=ADP`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=SCONJ\|Person=3\|PronType=Prs`, `POS=VERB`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Art`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `xcomp` |
| **`senter`** | `I`, `S` |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |

### Accuracy

| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 100.00 |
| `TOKEN_P` | 99.88 |
| `TOKEN_R` | 99.95 |
| `TOKEN_F` | 99.92 |
| `POS_ACC` | 96.91 |
| `MORPH_ACC` | 95.56 |
| `MORPH_MICRO_P` | 97.97 |
| `MORPH_MICRO_R` | 97.63 |
| `MORPH_MICRO_F` | 97.80 |
| `SENTS_P` | 93.85 |
| `SENTS_R` | 96.13 |
| `SENTS_F` | 94.97 |
| `DEP_UAS` | 89.75 |
| `DEP_LAS` | 85.76 |
| `TAG_ACC` | 89.48 |
| `LEMMA_ACC` | 76.73 |
| `ENTS_P` | 89.52 |
| `ENTS_R` | 89.89 |
| `ENTS_F` | 89.71 |


---
tags:
- spacy
- token-classification
language:
- el
license: cc-by-nc-sa-3.0
model-index:
- name: el_core_news_sm
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.7348837209
    - name: NER Recall
      type: recall
      value: 0.6638655462
    - name: NER F Score
      type: f_score
      value: 0.6975717439
  - task:
      name: POS
      type: token-classification
    metrics:
    - name: POS Accuracy
      type: accuracy
      value: 0.9134743381
  - task:
      name: SENTER
      type: token-classification
    metrics:
    - name: SENTER Precision
      type: precision
      value: 0.9195121951
    - name: SENTER Recall
      type: recall
      value: 0.935483871
    - name: SENTER F Score
      type: f_score
      value: 0.9274292743
  - task:
      name: UNLABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Unlabeled Dependencies Accuracy
      type: accuracy
      value: 0.8446911409
  - task:
      name: LABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Labeled Dependencies Accuracy
      type: accuracy
      value: 0.8446911409
---
### Details: https://spacy.io/models/el#el_core_news_sm

Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.

| Feature | Description |
| --- | --- |
| **Name** | `el_core_news_sm` |
| **Version** | `3.2.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Greek GDT v2.8](https://github.com/UniversalDependencies/UD_Greek-GDT) (Prokopidis, Prokopis)
[Greek NER Corpus (Google Summer of Code 2018)](https://github.com/eellak/gsoc2018-spacy) (Giannis Daras)
[spaCy lookups data](https://github.com/explosion/spacy-lookups-data) (Explosion) |
| **License** | `CC BY-NC-SA 3.0` |
| **Author** | [Explosion](https://explosion.ai) |

### Label Scheme

View label scheme (396 labels for 4 components)

| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=X`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `POS=ADP`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|POS=NUM`, `POS=NOUN`, `POS=ADV`, `POS=PUNCT`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADP`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `POS=AUX`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|POS=VERB\|VerbForm=Conv\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=SCONJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `POS=PART`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Abbr=Yes\|POS=ADV`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Nom\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|NumType=Sets\|Number=Plur\|POS=NUM`, `Aspect=Imp\|POS=AUX\|VerbForm=Conv\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|NumType=Sets\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Fem\|NumType=Sets\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|NumType=Mult\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Degree=Sup\|POS=ADV`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `POS=SYM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|NumType=Mult\|Number=Sing\|POS=NUM`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`senter`** | `I`, `S` |
| **`ner`** | `EVENT`, `GPE`, `LOC`, `ORG`, `PERSON`, `PRODUCT` |

### Accuracy

| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 100.00 |
| `TOKEN_P` | 99.90 |
| `TOKEN_R` | 99.95 |
| `TOKEN_F` | 99.93 |
| `SENTS_P` | 91.95 |
| `SENTS_R` | 93.55 |
| `SENTS_F` | 92.74 |
| `DEP_UAS` | 84.47 |
| `DEP_LAS` | 80.48 |
| `ENTS_P` | 73.49 |
| `ENTS_R` | 66.39 |
| `ENTS_F` | 69.76 |
| `POS_ACC` | 94.35 |
| `MORPH_ACC` | 88.64 |
| `MORPH_MICRO_P` | 94.75 |
| `MORPH_MICRO_R` | 94.54 |
| `MORPH_MICRO_F` | 94.64 |
| `TAG_ACC` | 91.35 |
| `LEMMA_ACC` | 56.20 |