---
language: hsb
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Sorbian by Adam Montgomerie
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice hsb
      type: common_voice
      args: hsb
    metrics:
       - name: Test WER
         type: wer
         value: 41.74
---

# Wav2Vec2-Large-XLSR-53-Sorbian

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Sorbian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:

```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "hsb", split="test[:2%]").

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-sorbian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-sorbian")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```

## Evaluation

The model can be evaluated as follows on the Sorbian test data of Common Voice.

```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "hsb", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-sorbian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-sorbian")
model.to("cuda")

chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\‘\\\\\\\\\\\\\\\\”\\\\\\\\\\\\\\\\�\\\\\\\\\\\\\\\\–\\\\\\\\\\\\\\\\—\\\\\\\\\\\\\\\\¬\\\\\\\\\\\\\\\\⅛]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: 41.74 %

## Training

The Common Voice `train`, `validation` datasets were used for training.

The script used for training can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Sorbian/XLSR_Sorbian.ipynb)

A notebook of the evaluation script can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Sorbian/wav2vec2_hsb_eval.ipynb)


---
language: fy-NL
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Frisian by Adam Montgomerie
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice fy-NL
      type: common_voice
      args: fy-NL
    metrics:
       - name: Test WER
         type: wer
         value: 21.72
---

# Wav2Vec2-Large-XLSR-53-Frisian

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Frisian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:

```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "fy-NL", split="test[:2%]").

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```

## Evaluation

The model can be evaluated as follows on the Frisian test data of Common Voice.

```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "fy-NL", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model.to("cuda")

chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\–\\—\\¬\\⅛]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: 21.72 %

## Training

The Common Voice `train`, `validation` datasets were used for training.

The script used for training can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Frisian/XLSR_Frisian.ipynb)

A notebook of the evaluation script can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Frisian/wav2vec2_fyNL_eval.ipynb)


This a BERT-based QA model finetuned to answer causal questions. The original model this is based on can be found [here](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2).  Analysis of this model is associated with the work found at the following [repo](https://github.com/kstats/CausalQG).


---
tags:
- conversational
---

## Model description

Finetuned version of DialogPT-medium released. Finetuned on data scraped from the r/Berkeley subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when this model until further analysis of its biases can be performed.


---
tags:
- conversational
---

## Model description

Finetuned version of DialogPT-large released. Finetuned on data scraped from the r/Kanye subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when using this model until further analysis of its biases can be performed.


---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-finetuned-funsd-test
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# layoutlmv2-finetuned-funsd-test

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000

### Training results

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1
- Datasets 1.18.0
- Tokenizers 0.11.0


## FinBERT

Code for importing and using this model is available [here](https://github.com/ipuneetrathore/BERT_models)


---
language:
- en
thumbnail:
tags:
- conversational
license: apache-2.0
datasets:
- rick_and_morty
metrics:
- perplexity
---

## Model description
Fine-tuning facebook/blenderbot-400M-distill on subtitles rick and morty


---
pipeline_tag: sentence-similarity
language: fr
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- fr
datasets:
- xnli
- stsb_multi_mt
---

# hugorosen/flaubert_base_uncased-xnli-sts

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"]

model = SentenceTransformer('hugorosen/flaubert_base_uncased-xnli-sts')
embeddings = model.encode(sentences)
print(embeddings)
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for
sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')
model = AutoModel.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```

## Evaluation Results

<!--- Describe how your model was evaluated -->

This model scores 76.9% on STS test (french)

## Training
### Pre-training

We use the pre-trained [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_cased). Please refer to the model card for more detailed information about the pre-training procedure.

### Fine-tuning

we fine-tune the model using a `CosineSimilarityLoss` on XNLI and STS dataset (french).

Parameters of the fit()-Method:
```
{
    "epochs": 4,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 144,
    "weight_decay": 0.01
}
```

## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: FlaubertModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.

Based on FlauBERT:

```
@InProceedings{le2020flaubert,
  author    = {Le, Hang  and  Vial, Lo\"{i}c  and  Frej, Jibril  and  Segonne, Vincent  and  Coavoux, Maximin  and  Lecouteux, Benjamin  and  Allauzen, Alexandre  and  Crabb\'{e}, Beno\^{i}t  and  Besacier, Laurent  and  Schwab, Didier},
  title     = {FlauBERT: Unsupervised Language Model Pre-training for French},
  booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month     = {May},
  year      = {2020},
  address   = {Marseille, France},
  publisher = {European Language Resources Association},
  pages     = {2479--2490},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}
```


---
tags:
- conversational
---

# Atakan DialoGPT Model


### Finetuned on annual report sentence pair
This marianMT has been further finetuned on annual report sentence pairs

## Test out at huggingface spaces!
https://huggingface.co/spaces/wolfrage89/finance_domain_translation_marianMT

## Sample colab notebook
https://colab.research.google.com/drive/1H57vwiah7n1JXvXYMqJ8dklrIuU6Cljb?usp=sharing

## How to use

```python
!pip install transformers
!pip install sentencepiece

from transformers import MarianMTModel, MarianTokenizer

tokenizer = MarianTokenizer.from_pretrained("wolfrage89/annual_report_translation_id_en")
model = MarianMTModel.from_pretrained("wolfrage89/annual_report_translation_id_en")

#tokenizing bahasa sentence
bahasa_sentence = "Interpretasi ini merupakan interpretasi atas PSAK 46: Pajak Penghasilan yang bertujuan untuk mengklarifikasi dan memberikan panduan dalam merefleksikan ketidakpastian perlakuan pajak penghasilan dalam laporan keuangan."
tokenized_bahasa_sentence = tokenizer([bahasa_sentence], return_tensors='pt', max_length=104, truncation=True)

#feeding tokenized sentence into model, the max_legnth have been set to 104 as the model was trained mostly on sentences with this length
translated_tokens = model.generate(**tokenized_bahasa_sentence, max_length=104)[0]

## decoding the tokens to get english sentence
english_sentence = tokenizer.decode(translated_tokens, skip_special_tokens=True)

print(english_sentence)
# This interpretation is an interpretation of PSAK 46: Income Tax that aims to clarify and provide guidance in reflecting the uncertainty of income tax treatments in the financial statements.

```

### opus-mt-id-en (original model)

* source languages: id
* target languages: en
*  OPUS readme: [id-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/id-en/README.md)


## Roberta based NER
This model will take in a new article label 3 entities [ORGS, SEGNUM, NUM]. This model is train on reuters news articles

## Try out on huggingface Spaces
https://huggingface.co/spaces/wolfrage89/company_segments_ner

## colab sample notebook
https://colab.research.google.com/drive/165utMQzYVAX7-aQjWjpmPHwHpdKTaHBa?usp=sharing

## How to use
```python

from transformers import pipeline

# Minimum code
sentence = """Exxon Mobil Corporation is engaged in energy business. The Company is engaged in the exploration, production, trade, transportation and sale of crude oil and natural gas, and the manufacture, transportation and sale of crude oil, natural gas, petroleum products, petrochemicals and a range of specialty products. The Company's segments include Upstream, Downstream, Chemical, and Corporate and Financing. The Upstream segment operates to explore for and produce crude oil and natural gas. The Downstream manufactures, trades and sells petroleum products. The refining and supply operations consists of a global network of manufacturing plants, transportation systems, and distribution centers that provide a range of fuels, lubricants and other products and feedstocks to its customers around the world. The Chemical segment manufactures and sells petrochemicals. The Chemical business supplies olefins, polyolefins, aromatics, and a variety of other petrochemicals."""

model = pipeline('ner', "wolfrage89/company_segment_ner")
model_output = model(sentence)

print(model_ouput)
# [{'entity': 'B-ORG', 'score': 0.99996805, 'index': 1, 'word': 'Ex', 'start': 0, 'end': 2}, {'entity': 'I-ORG', 'score': 0.99971646, 'index': 2, 'word': 'xon', 'start': 2, 'end': 5}, ....]

# Sample helper function if you want to use
def ner_prediction(model, sentence):
    entity_map = {
        "B-ORG":"ORG",
        "B-SEG":"SEG",
        "B-SEGNUM":"SEGNUM"
    }
    results = []
    model_output = model(sentence)

    accumulate = ""
    current_class = None
    start = 0
    end = 0
    for item in model_output:
        if item['entity'].startswith("B"):
            if len(accumulate) >0:
                results.append((current_class, accumulate, start, end))
            accumulate = item['word'].lstrip("Ġ")
            current_class = entity_map[item['entity']]
            start=item['start']
            end = item['end']

        else:
            if item['word'].startswith("Ġ"):
                accumulate+=" "+item['word'].lstrip("Ġ")

            else:
                accumulate+=item['word']
            end = item['end']

    # clear last cache
    if len(accumulate)>0:
         results.append((current_class, accumulate, start, end))

    return results

```


---
tags:
- conversational
---

# Peppa Pig DialogGPT-small Model


---
tags:
- spacy
- token-classification
language:
- pt
license: cc-by-sa-4.0
model-index:
- name: pt_udv25_portuguesebosque_trf
  results:
  - task:
      name: TAG
      type: token-classification
    metrics:
    - name: TAG (XPOS) Accuracy
      type: accuracy
      value: 0.9809207592
  - task:
      name: POS
      type: token-classification
    metrics:
    - name: POS (UPOS) Accuracy
      type: accuracy
      value: 0.9814163239
  - task:
      name: MORPH
      type: token-classification
    metrics:
    - name: Morph (UFeats) Accuracy
      type: accuracy
      value: 0.9733881758
  - task:
      name: LEMMA
      type: token-classification
    metrics:
    - name: Lemma Accuracy
      type: accuracy
      value: 0.980030722
  - task:
      name: UNLABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Unlabeled Attachment Score (UAS)
      type: f_score
      value: 0.938526243
  - task:
      name: LABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Labeled Attachment Score (LAS)
      type: f_score
      value: 0.91190874
  - task:
      name: SENTS
      type: token-classification
    metrics:
    - name: Sentences F-Score
      type: f_score
      value: 0.9582222222
---
UD v2.5 benchmarking pipeline for UD_Portuguese-Bosque

| Feature | Description |
| --- | --- |
| **Name** | `pt_udv25_portuguesebosque_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |

### Label Scheme

<details>

<summary>View label scheme (2079 labels for 6 components)</summary>

| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADP_ADV`, `ADP_DET`, `ADP_NUM`, `ADP_PRON`, `ADP_PROPN`, `ADV`, `ADV_PRON`, `ADV_PROPN`, `AUX`, `AUX_PRON`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PART_NOUN`, `PRON`, `PRON_PRON`, `PROPN`, `PROPN_DET`, `PROPN_PROPN`, `PUNCT`, `SCONJ`, `SCONJ_DET`, `SCONJ_PRON`, `SYM`, `VERB`, `VERB_PRON`, `X` |
| **`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`, `Gender=Unsp\|Number=Sing\|POS=PROPN`, `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`, `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`, `Gender=Unsp\|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\|Gender=Unsp\|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=PRON\|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`, `Gender=Unsp\|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`, `Gender=Unsp\|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`, `Gender=Unsp\|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\|Gender=Unsp\|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\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Unsp\|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\|Gender=Unsp\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=AUX`, `Case=Acc\|Gender=Unsp\|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\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Unsp\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Gender=Unsp\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Unsp\|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\|Gender=Unsp\|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=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\|Gender=Unsp\|POS=VERB\|PronType=Prs\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Unsp\|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`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `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`, `Case=Acc\|Gender=Unsp\|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`, `Gender=Unsp\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Unsp\|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\|Gender=Unsp\|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`, `Definite=Def\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `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\|Gender=Unsp\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|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\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `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`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Case=Acc\|Gender=Unsp\|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`, `Number=Sing\|POS=NOUN`, `Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Unsp\|Number=Plur\|POS=PROPN`, `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\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Gender=Unsp\|POS=PRON\|PronType=Prs`, `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`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Prs`, `Gender=Unsp\|Number=Unsp\|POS=PRON\|PronType=Rel`, `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\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Acc\|Gender=Unsp\|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\|Gender=Unsp\|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`, `Gender=Unsp\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `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`, `Gender=Unsp\|Number=Sing\|POS=NOUN`, `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`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Gender=Unsp\|Number=Unsp\|POS=PRON\|PronType=Dem`, `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`, `Gender=Unsp\|Number=Unsp\|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`, `Gender=Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Unsp\|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\|Gender=Unsp\|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\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Unsp\|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\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|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`, `Gender=Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `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\|Gender=Unsp\|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`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Ind`, `Case=Dat\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Unsp\|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\|Gender=Unsp\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Plur\|POS=VERB\|Person=2\|PronType=Prs\|VerbForm=Inf`, `Gender=Unsp\|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`, `Gender=Unsp\|Number=Unsp\|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\|Gender=Unsp\|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\|Gender=Unsp\|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`, `Gender=Unsp\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Unsp\|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`, `POS=PRON\|PronType=Dem`, `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`, `Gender=Unsp\|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\|Gender=Unsp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Unsp\|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`, `Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Unsp\|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\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Unsp\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Unsp\|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\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Unsp\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Gender=Unsp\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Unsp\|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`, `Gender=Unsp\|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\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Gender=Unsp\|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\|Gender=Unsp\|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`, `Number=Unsp\|POS=PRON\|PronType=Ind`, `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`, `Gender=Masc\|POS=NOUN`, `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`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Ger`, `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=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `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\|Gender=Unsp\|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`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Unsp\|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\|Gender=Unsp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `POS=VERB`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Unsp\|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,Unsp\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Unsp\|Number=Unsp\|POS=ADV\|PronType=Int`, `Gender=Unsp\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `POS=PROPN`, `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\|Gender=Unsp\|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\|Gender=Unsp\|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`, `Gender=Unsp\|Number=Sing\|POS=ADV`, `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`, `Case=Dat\|Gender=Unsp\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Unsp\|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=Fem\|Number=Sing\|POS=DET`, `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`, `Gender=Masc\|Number=Plur\|POS=DET`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=VERB`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Fin`, `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`, `NumType=Ord\|POS=NUM`, `POS=DET\|PronType=Int`, `Case=Acc\|Gender=Unsp\|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\|Gender=Unsp\|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\|Gender=Unsp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Unsp\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=SCONJ\|Person=3\|PronType=Prs`, `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\|Gender=Unsp\|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`, `dislocated`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `3`, `4`, `6`, `8`, `9`, `11`, `13`, `15`, `17`, `20`, `22`, `24`, `14`, `7`, `26`, `28`, `30`, `32`, `34`, `36`, `38`, `40`, `42`, `44`, `45`, `48`, `53`, `54`, `55`, `57`, `58`, `60`, `62`, `65`, `66`, `67`, `70`, `72`, `74`, `76`, `79`, `83`, `85`, `87`, `89`, `91`, `95`, `99`, `101`, `102`, `104`, `106`, `108`, `110`, `113`, `115`, `117`, `119`, `120`, `122`, `124`, `125`, `126`, `128`, `130`, `132`, `134`, `136`, `138`, `141`, `142`, `144`, `147`, `150`, `152`, `154`, `155`, `159`, `162`, `163`, `165`, `166`, `169`, `171`, `172`, `174`, `175`, `178`, `180`, `181`, `184`, `186`, `189`, `191`, `193`, `195`, `198`, `200`, `111`, `202`, `204`, `207`, `209`, `212`, `214`, `216`, `218`, `220`, `221`, `223`, `224`, `226`, `228`, `230`, `232`, `234`, `236`, `239`, `242`, `244`, `245`, `246`, `247`, `249`, `251`, `252`, `253`, `256`, `257`, `259`, `261`, `263`, `267`, `269`, `270`, `271`, `273`, `277`, `278`, `281`, `282`, `283`, `285`, `286`, `288`, `289`, `290`, `292`, `293`, `295`, `297`, `298`, `300`, `302`, `303`, `305`, `307`, `309`, `310`, `311`, `313`, `314`, `316`, `319`, `168`, `322`, `323`, `326`, `327`, `329`, `331`, `333`, `335`, `336`, `338`, `341`, `343`, `345`, `347`, `348`, `350`, `351`, `354`, `356`, `359`, `361`, `363`, `364`, `365`, `366`, `367`, `369`, `373`, `376`, `378`, `379`, `380`, `381`, `383`, `384`, `386`, `389`, `392`, `394`, `395`, `396`, `398`, `400`, `403`, `405`, `407`, `409`, `410`, `412`, `415`, `416`, `417`, `418`, `419`, `420`, `422`, `424`, `429`, `431`, `432`, `438`, `439`, `441`, `442`, `445`, `448`, `449`, `450`, `452`, `454`, `457`, `458`, `461`, `463`, `465`, `468`, `469`, `470`, `473`, `475`, `477`, `478`, `481`, `484`, `485`, `486`, `488`, `491`, `495`, `497`, `499`, `503`, `506`, `507`, `508`, `509`, `510`, `511`, `513`, `514`, `516`, `517`, `519`, `521`, `522`, `523`, `525`, `528`, `530`, `533`, `534`, `536`, `538`, `540`, `541`, `542`, `544`, `545`, `547`, `549`, `551`, `552`, `554`, `555`, `558`, `559`, `560`, `562`, `563`, `565`, `566`, `570`, `572`, `579`, `582`, `583`, `585`, `586`, `587`, `590`, `592`, `594`, `595`, `597`, `599`, `601`, `603`, `606`, `608`, `609`, `611`, `612`, `614`, `615`, `616`, `619`, `621`, `622`, `625`, `626`, `627`, `629`, `630`, `631`, `633`, `634`, `637`, `638`, `639`, `640`, `642`, `644`, `646`, `647`, `652`, `653`, `656`, `657`, `659`, `660`, `661`, `664`, `666`, `669`, `671`, `672`, `673`, `674`, `675`, `677`, `678`, `680`, `682`, `685`, `687`, `689`, `691`, `692`, `693`, `695`, `699`, `701`, `702`, `703`, `706`, `707`, `709`, `710`, `711`, `712`, `714`, `716`, `718`, `719`, `720`, `721`, `724`, `725`, `729`, `730`, `732`, `735`, `738`, `740`, `742`, `744`, `746`, `749`, `750`, `751`, `754`, `756`, `760`, `762`, `767`, `769`, `771`, `774`, `776`, `778`, `780`, `781`, `784`, `785`, `787`, `788`, `789`, `791`, `793`, `794`, `795`, `798`, `800`, `801`, `803`, `804`, `806`, `808`, `810`, `811`, `812`, `814`, `816`, `819`, `820`, `823`, `824`, `825`, `828`, `829`, `832`, `833`, `835`, `836`, `839`, `840`, `844`, `845`, `847`, `850`, `851`, `853`, `854`, `855`, `858`, `861`, `862`, `863`, `865`, `868`, `871`, `873`, `875`, `877`, `879`, `880`, `881`, `882`, `883`, `884`, `885`, `887`, `889`, `892`, `894`, `895`, `537`, `896`, `898`, `899`, `902`, `904`, `905`, `908`, `909`, `912`, `914`, `916`, `917`, `920`, `921`, `922`, `924`, `925`, `928`, `929`, `930`, `931`, `933`, `936`, `939`, `940`, `942`, `943`, `945`, `948`, `949`, `951`, `953`, `956`, `957`, `960`, `961`, `963`, `964`, `965`, `966`, `969`, `970`, `971`, `973`, `976`, `977`, `979`, `981`, `983`, `985`, `987`, `988`, `990`, `991`, `993`, `994`, `995`, `996`, `997`, `998`, `1000`, `1001`, `1004`, `1006`, `1007`, `1009`, `1011`, `1013`, `1014`, `1015`, `1019`, `1021`, `1023`, `1025`, `1026`, `1029`, `1030`, `1033`, `1034`, `1036`, `1037`, `1039`, `1041`, `1042`, `1044`, `1046`, `1048`, `1050`, `1051`, `1054`, `1056`, `1057`, `1059`, `1061`, `1062`, `1064`, `1066`, `1067`, `1068`, `1069`, `1071`, `1072`, `1073`, `1074`, `1075`, `1077`, `1078`, `1079`, `1081`, `1083`, `1084`, `1085`, `1086`, `1088`, `1089`, `1092`, `1093`, `1097`, `1100`, `1101`, `1103`, `1104`, `1106`, `1108`, `1110`, `1114`, `1115`, `1117`, `1118`, `1119`, `1121`, `1123`, `1124`, `1126`, `1127`, `1128`, `1130`, `1133`, `1135`, `1136`, `1140`, `1143`, `1146`, `1148`, `1149`, `1151`, `1152`, `1155`, `1157`, `1158`, `1160`, `1163`, `1164`, `1165`, `1167`, `1168`, `1170`, `1172`, `1176`, `1177`, `1178`, `1180`, `1182`, `1184`, `1186`, `1187`, `1189`, `1190`, `1193`, `1196`, `1198`, `1202`, `1203`, `1204`, `1205`, `1206`, `1207`, `1208`, `1209`, `1210`, `1211`, `1214`, `1215`, `1216`, `1218`, `1219`, `1220`, `1221`, `1223`, `1225`, `1226`, `1228`, `1229`, `1230`, `1233`, `1234`, `1236`, `1237`, `1238`, `1239`, `1240`, `1242`, `1244`, `1247`, `1248`, `1249`, `1250`, `1251`, `1254`, `1256`, `1257`, `1258`, `1260`, `1262`, `1263`, `1266`, `1271`, `1272`, `1273`, `1274`, `1275`, `1277`, `1278`, `1279`, `1280`, `1283`, `1285`, `1287`, `1288`, `1290`, `1293`, `1294`, `1296`, `1299`, `1301`, `1302`, `1304`, `1307`, `1308`, `1309`, `1311`, `1312`, `1314`, `1315`, `1317`, `1320`, `1322`, `1324`, `1325`, `1326`, `1329`, `1330`, `1332`, `1333`, `1334`, `1336`, `1338`, `1339`, `1340`, `1341`, `1344`, `1345`, `1346`, `1348`, `1350`, `1351`, `1352`, `1354`, `1356`, `1358`, `1359`, `1360`, `1361`, `1362`, `1363`, `1367`, `1370`, `1371`, `1373`, `1375`, `1377`, `1378`, `1379`, `1381`, `1382`, `1383`, `1385`, `1386`, `1388`, `1389`, `1393`, `1395`, `1399`, `1401`, `1402`, `1403`, `1404`, `1405`, `1407`, `1408`, `1411`, `1413`, `1417`, `1418`, `1419`, `1420`, `1421`, `1423`, `1424`, `1425`, `1429`, `1430`, `1431`, `1433`, `1434`, `1436`, `1437`, `1438`, `1439`, `1442`, `1444`, `1446`, `1447`, `1449`, `1451`, `1453`, `1454`, `1455`, `1458`, `1461`, `1463`, `1464`, `1465`, `1467`, `1468`, `1469`, `1470`, `1471`, `1473`, `1476`, `1477`, `1478`, `1479`, `1482`, `1483`, `1484`, `1489`, `1491`, `1492`, `1494`, `1496`, `1497`, `1499`, `1502`, `1504`, `1505`, `1506`, `1507`, `1508`, `1509`, `1511`, `1514`, `1515`, `1517`, `1520`, `1521`, `1524`, `1525`, `1528`, `1529`, `1530`, `1532`, `1533`, `1534`, `1536`, `1538`, `1539`, `1541`, `1543`, `1544`, `1545`, `1546`, `1547`, `1548`, `1552`, `1556`, `1558`, `1560`, `1562`, `1563`, `1566`, `1567`, `1569`, `1570`, `1572`, `1574`, `1577`, `761`, `1579`, `1583`, `1585`, `1586`, `1587`, `1590`, `1592`, `1593`, `1595`, `1596`, `1597`, `1599`, `1603`, `1605`, `1607`, `1609`, `1610`, `1612`, `1614`, `1615`, `1617`, `1618`, `1620`, `1621`, `1622`, `1625`, `1627`, `1629`, `1630`, `1631`, `1633`, `1634`, `1636`, `1637`, `1638`, `1640`, `1641`, `1643`, `1644`, `1646`, `1647`, `1648`, `1651`, `1652`, `1657`, `1658`, `1659`, `1661`, `1662`, `1663`, `1664`, `1666`, `1669`, `1672`, `1673`, `1675`, `1676`, `1677`, `1679`, `1682`, `1684`, `1409`, `1685`, `1686`, `1687`, `1688`, `1690`, `1692`, `1693`, `1694`, `1695`, `1697`, `1699`, `1700`, `1704`, `1707`, `1708`, `1709`, `1711`, `1712`, `1715`, `1716`, `1717`, `1718`, `1719`, `1721`, `1722`, `1723`, `1725`, `1726`, `1729`, `1730`, `1732`, `1733`, `1734`, `1735`, `1737`, `1738`, `1741`, `1743`, `1744`, `1746`, `1747`, `1748`, `1750`, `1752`, `1754`, `1755`, `1756`, `1758`, `1759`, `1760`, `1762`, `1765`, `1766`, `1768`, `1769`, `1770`, `1773`, `1774`, `1775`, `1777`, `1778`, `1781`, `1782`, `1783`, `1785`, `1786`, `1787`, `219`, `1788`, `1789`, `1791`, `1792`, `1793`, `1795`, `1799`, `1800`, `1801`, `1802`, `1803`, `1805`, `1806`, `1808`, `1809`, `1811`, `1812`, `1814`, `1815`, `1816`, `1821`, `1823`, `1824`, `1825`, `1826`, `1829`, `1830`, `1831`, `1832`, `1833`, `1835`, `1838`, `1839`, `1840`, `1842`, `1843`, `1845`, `1846`, `1848`, `1849`, `1850`, `1851`, `1855`, `1856`, `1857`, `1859`, `1861`, `1862`, `1864`, `1866`, `1867`, `1869`, `421`, `1870`, `1872`, `1873`, `1874`, `1875`, `1878`, `1879`, `1880`, `1882`, `1883`, `1884`, `1885`, `1888`, `1891`, `1894`, `1895`, `1898`, `1901`, `1903`, `1904`, `1906`, `1907`, `1910`, `1912`, `1915`, `1917`, `1918`, `1920`, `1921`, `1922`, `1924`, `1926`, `1927`, `1930`, `1932`, `1933`, `1936`, `1938`, `1940`, `1941`, `1942`, `1943`, `1945`, `1947`, `1949`, `1951`, `1952`, `1953`, `1954`, `1956`, `1957`, `1958`, `1960`, `1961`, `1963`, `1964`, `1966`, `1968`, `1971`, `1973`, `1974`, `1975`, `1977`, `1979`, `1981`, `1983`, `1985`, `1986`, `1987`, `1988`, `792`, `1990`, `790`, `1992`, `1994`, `1996`, `1998`, `1999`, `2000`, `2001`, `2002`, `2003`, `2005`, `2006`, `2008`, `2010`, `2011`, `2012`, `2014`, `2016`, `2017`, `2018`, `2019`, `2021`, `2022`, `2023`, `2024`, `2025`, `2026`, `2028`, `2029`, `2031`, `2034`, `2036`, `2038`, `2041`, `2042`, `2044`, `2045`, `2046`, `2050`, `2051`, `2052`, `2055`, `2056`, `2057`, `2059`, `2060`, `2061`, `2062`, `2064`, `2066`, `2068`, `2069`, `2070`, `2072`, `2073`, `2075`, `2076`, `2078`, `2079`, `2081`, `2083`, `2084`, `2086`, `2088`, `2089`, `2091`, `2093`, `2095`, `2097`, `2098`, `2099`, `2101`, `2102`, `2103`, `2104`, `2106`, `2107`, `2108`, `2109`, `2110`, `2111`, `2112`, `2114`, `2116`, `2117`, `2118`, `2119`, `2120`, `2121`, `2122`, `2123`, `2124`, `2125`, `2126`, `2127`, `1584`, `2128`, `2130`, `2131`, `2132`, `2134`, `2137`, `2138`, `2139`, `2141`, `2144`, `2145`, `2146`, `2147`, `2150`, `2151`, `2153`, `2154`, `2155`, `2156`, `2157`, `2159`, `2160`, `2161`, `2163`, `2164`, `2165`, `2166`, `2167`, `2168`, `2169`, `2170`, `2173`, `2174`, `2175`, `2176`, `2177`, `2179`, `2182`, `2185`, `2187`, `2188`, `2189`, `2191`, `2193`, `2194`, `2195`, `2196`, `2197`, `2198`, `2200`, `2202`, `2203`, `2204`, `2205`, `2206`, `2207`, `2208`, `2209`, `2210`, `2211`, `2212`, `2213`, `2216`, `2217`, `2219`, `2221`, `2224`, `2227`, `2229`, `2230`, `2232`, `2233`, `2234`, `2235`, `2237`, `2239`, `2240`, `2241`, `2242`, `2243`, `2244`, `2245`, `2246`, `2248`, `2249`, `2250`, `2251`, `2253`, `2254`, `2255`, `2257`, `2258`, `2260`, `2261`, `2262`, `2263`, `2264`, `2265`, `2266`, `2267`, `2268`, `2269`, `2270`, `2271`, `2272`, `2273`, `2274`, `2275`, `2277`, `2278`, `2281`, `2282`, `2283`, `2284`, `2285`, `2287`, `2288`, `2290`, `2291`, `2292`, `2293`, `2294`, `2297`, `2298`, `2299`, `2300`, `2302`, `2304`, `2305`, `2307`, `2308`, `2309`, `2310`, `2312`, `2313`, `2314`, `2315`, `2316`, `2317`, `2318`, `2319`, `2321`, `2322`, `2323`, `2327`, `2329`, `2331`, `2333`, `2335`, `2337`, `2338`, `2339`, `2341`, `2342`, `2343`, `2346`, `2348`, `2349`, `2350`, `2351`, `2352`, `2353`, `37`, `2354`, `2355`, `2357`, `2358`, `2359`, `2360`, `2361`, `2362`, `2364`, `2365`, `2367`, `2368`, `2369`, `2370`, `2372`, `2375`, `2376`, `2378`, `2379`, `2380`, `2381`, `2382`, `2383`, `2384`, `2385`, `2386`, `2389`, `2390`, `2392`, `2393`, `2394`, `2395`, `2398`, `2399`, `2400`, `2402`, `2403`, `2405`, `2406`, `2407`, `2408`, `2409`, `2410`, `2413`, `2415`, `2416`, `2417`, `2418`, `2419`, `2420`, `2422`, `2424`, `2427`, `2428`, `2429`, `2430`, `2431`, `2432`, `2433`, `2435`, `2437`, `1962`, `2438`, `2439`, `2440`, `2442`, `2443`, `2444`, `2445` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.92 |
| `TOKEN_P` | 99.93 |
| `TOKEN_R` | 99.91 |
| `TOKEN_ACC` | 99.99 |
| `SENTS_F` | 95.82 |
| `SENTS_P` | 95.40 |
| `SENTS_R` | 96.25 |
| `TAG_ACC` | 98.09 |
| `POS_ACC` | 98.14 |
| `MORPH_ACC` | 97.34 |
| `DEP_UAS` | 93.85 |
| `DEP_LAS` | 91.19 |
| `LEMMA_ACC` | 98.00 |