license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0641 - Precision: 0.9233 - Recall: 0.9322 - F1: 0.9277 - Accuracy: 0.9829 | 4d23df5dda9372238e488425b96ebdc8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2448 | 1.0 | 878 | 0.0718 | 0.9110 | 0.9165 | 0.9138 | 0.9803 | | 0.0547 | 2.0 | 1756 | 0.0635 | 0.9197 | 0.9297 | 0.9247 | 0.9821 | | 0.0303 | 3.0 | 2634 | 0.0641 | 0.9233 | 0.9322 | 0.9277 | 0.9829 | | bb63a1d3d2f6ce7624d1515519973729 |
apache-2.0 | ['automatic-speech-recognition'] | false | Thai Wav2Vec2 with CommonVoice V8 (deepcut tokenizer) + language model This model trained with CommonVoice V8 dataset by increase data from CommonVoice V7 dataset that It was use in [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). It was finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53). | fc38d07c110fa13e671566a00bdf998d |
apache-2.0 | ['automatic-speech-recognition'] | false | Datasets It is increase new data from The Common Voice V8 dataset to Common Voice V7 dataset or remove all data in Common Voice V7 dataset before split Common Voice V8 then add CommonVoice V7 dataset back to dataset. It use [ekapolc/Thai_commonvoice_split](https://github.com/ekapolc/Thai_commonvoice_split) script for split Common Voice dataset. | 259c45c8348cb05f9a1f71cd6973a4d9 |
apache-2.0 | ['automatic-speech-recognition'] | false | Models This model was finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model with Thai Common Voice V8 dataset and It use pre-tokenize with deepcut.tokenize. | dd17017f66033b8a9c33a345949b998a |
apache-2.0 | ['automatic-speech-recognition'] | false | Evaluation **Test with CommonVoice V8 Testset** | Model | WER by newmm (%) | WER by deepcut (%) | CER | |-----------------------|------------------|--------------------|----------| | AIResearch.in.th and PyThaiNLP | 17.414503 | 11.923089 | 3.854153 | | wav2vec2 with deepcut | 16.354521 | 11.424476 | 3.684060 | | wav2vec2 with newmm | 16.698299 | 11.436941 | 3.737407 | | **wav2vec2 with deepcut + language model** | 12.630260 | 9.613886 | 3.292073 | | wav2vec2 with newmm + language model | 12.583706 | 9.598305 | 3.276610 | **Test with CommonVoice V7 Testset (same test by CV V7)** | Model | WER by newmm (%) | WER by deepcut (%) | CER | |-----------------------|------------------|--------------------|----------| | AIResearch.in.th and PyThaiNLP | 13.936698 | 9.347462 | 2.804787 | | wav2vec2 with deepcut | 12.776381 | 8.773006 | 2.628882 | | wav2vec2 with newmm | 12.750596 | 8.672616 | 2.623341 | | **wav2vec2 with deepcut + language model** | 9.940050 | 7.423313 | 2.344940 | | wav2vec2 with newmm + language model | 9.559724 | 7.339654 | 2.277071 | This is use same testset from [https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). **Links:** - GitHub Dataset: [https://github.com/wannaphong/thai_commonvoice_dataset](https://github.com/wannaphong/thai_commonvoice_dataset) - Technical report: [Thai Wav2Vec2.0 with CommonVoice V8](https://arxiv.org/abs/2208.04799) | dfe64b6115027487c4333cdfd6a46048 |
apache-2.0 | ['automatic-speech-recognition'] | false | BibTeX entry and citation info ``` @misc{phatthiyaphaibun2022thai, title={Thai Wav2Vec2.0 with CommonVoice V8}, author={Wannaphong Phatthiyaphaibun and Chompakorn Chaksangchaichot and Peerat Limkonchotiwat and Ekapol Chuangsuwanich and Sarana Nutanong}, year={2022}, eprint={2208.04799}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | 36cd6bad09cc4f7cf161a7ed10763fe0 |
mit | ['generated_from_trainer'] | false | SST2_XLNet_5E This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5502 - Accuracy: 0.9133 | e426d52c67b030570099c2ea9d506c57 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6038 | 0.12 | 50 | 0.2830 | 0.8933 | | 0.3903 | 0.23 | 100 | 0.3346 | 0.9 | | 0.3476 | 0.35 | 150 | 0.4187 | 0.8533 | | 0.3528 | 0.46 | 200 | 0.3177 | 0.9 | | 0.3372 | 0.58 | 250 | 0.4171 | 0.8333 | | 0.3106 | 0.69 | 300 | 0.2825 | 0.9 | | 0.295 | 0.81 | 350 | 0.3152 | 0.9 | | 0.2828 | 0.92 | 400 | 0.4360 | 0.88 | | 0.2359 | 1.04 | 450 | 0.3971 | 0.9 | | 0.2224 | 1.15 | 500 | 0.3380 | 0.88 | | 0.2136 | 1.27 | 550 | 0.3889 | 0.8933 | | 0.264 | 1.39 | 600 | 0.4182 | 0.8667 | | 0.1864 | 1.5 | 650 | 0.4887 | 0.88 | | 0.1817 | 1.62 | 700 | 0.3626 | 0.9133 | | 0.2021 | 1.73 | 750 | 0.4481 | 0.8933 | | 0.2154 | 1.85 | 800 | 0.3702 | 0.8933 | | 0.2392 | 1.96 | 850 | 0.5025 | 0.8933 | | 0.1496 | 2.08 | 900 | 0.4606 | 0.9133 | | 0.1537 | 2.19 | 950 | 0.5008 | 0.8933 | | 0.1015 | 2.31 | 1000 | 0.5612 | 0.9067 | | 0.0915 | 2.42 | 1050 | 0.5249 | 0.8933 | | 0.1239 | 2.54 | 1100 | 0.4234 | 0.9133 | | 0.1135 | 2.66 | 1150 | 0.4910 | 0.9067 | | 0.1738 | 2.77 | 1200 | 0.3844 | 0.92 | | 0.1428 | 2.89 | 1250 | 0.4282 | 0.92 | | 0.1282 | 3.0 | 1300 | 0.4320 | 0.9 | | 0.059 | 3.12 | 1350 | 0.4957 | 0.9133 | | 0.0517 | 3.23 | 1400 | 0.4927 | 0.92 | | 0.0853 | 3.35 | 1450 | 0.4187 | 0.92 | | 0.0808 | 3.46 | 1500 | 0.4304 | 0.92 | | 0.09 | 3.58 | 1550 | 0.3447 | 0.9267 | | 0.044 | 3.7 | 1600 | 0.4994 | 0.9067 | | 0.0443 | 3.81 | 1650 | 0.4516 | 0.9133 | | 0.0974 | 3.93 | 1700 | 0.4172 | 0.92 | | 0.0768 | 4.04 | 1750 | 0.4777 | 0.9133 | | 0.0418 | 4.16 | 1800 | 0.4924 | 0.9267 | | 0.0237 | 4.27 | 1850 | 0.5254 | 0.92 | | 0.0426 | 4.39 | 1900 | 0.5532 | 0.9133 | | 0.0336 | 4.5 | 1950 | 0.5838 | 0.9067 | | 0.0188 | 4.62 | 2000 | 0.5775 | 0.9067 | | 0.0318 | 4.73 | 2050 | 0.5781 | 0.9067 | | 0.0348 | 4.85 | 2100 | 0.5526 | 0.9133 | | 0.0524 | 4.97 | 2150 | 0.5502 | 0.9133 | | 3051576a8fe5f7250b21b1f181636330 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Miyo-Waifu-Diffusion This model is a fine-tuned Waifu-Diffusion v1.3 by dreambooth. that can generate illustrations of Miyo Harada from THE IDOLM@STER CINDERELLA GIRLS. To use at a minimum,Please type "miyoshort" or "miyopony" at the prompt miyoshort sample %2C(Driving%20red%20car_1.0)%2C1girl%2Chighly%20detailed%2Crim%20light%2Cwrinkles%20in%20clothes%2C8k%2Ckawaii%2C(masterpiece)%2Chigh%20quali.png) Prompts:(sks miyoshort:1.0),(Driving red car:1.0),1girl,highly detailed,rim light,wrinkles in clothes,8k,kawaii,(masterpiece),high quality,green eyes,large breasts,kawaii, T-shirt,(holding steering wheel),in the car,looking side Negative prompt: ( 2girls:1.2),3girls,(long hair:1.2),One-color background,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,bad anatomy, bad hands, bad quality, blurry, cropped, disconnected limbs, extra digit, extra limbs, fewer digits, jpeg artifacts, explicit, text,bad art, ugly, messy drawing, flesh pile,mutated hands and fingers, intricate human hands fingers, poorly drawn hands, malformed hands, bad hands,long neck,big face,big head,neck choker miyopony sample %2C(Driving%20red%20car_1.1)%2C1girl%2Chighly%20detailed%2Crim%20light%2Cwrinkles%20in%20clothes%2C8k%2Ckawaii%2C(masterpiece)%2Chigh%20qualit.png) (sks miyopony:1.0),(Driving red car:1.1),1girl,highly detailed,rim light,wrinkles in clothes,8k,kawaii,(masterpiece),high quality,green eyes,large breasts,kawaii, T-shirt,(holding steering wheel),in the car,(looking side) Negative prompt: ( 2girls:1.2),3girls,(long hair:1.2),One-color background,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,bad anatomy, bad hands, bad quality, blurry, cropped, disconnected limbs, extra digit, extra limbs, fewer digits, jpeg artifacts, explicit, text,bad art, ugly, messy drawing, flesh pile,mutated hands and fingers, intricate human hands fingers, poorly drawn hands, malformed hands, bad hands,long neck,big face,big head,neck choker If the generated image resembles Miyo Harada, the copyright may belong to Bandai Namco Entertainment Inc. | a40c7b906515f0fadab467addf4c29e1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8575 - Matthews Correlation: 0.5443 | b531cfe066b5af2692bd0d57332d136a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5242 | 1.0 | 535 | 0.5258 | 0.4391 | | 0.346 | 2.0 | 1070 | 0.5264 | 0.5074 | | 0.2334 | 3.0 | 1605 | 0.6808 | 0.5074 | | 0.1711 | 4.0 | 2140 | 0.7737 | 0.5373 | | 0.1205 | 5.0 | 2675 | 0.8575 | 0.5443 | | 80df073d9eb50b4d4b05d4097a953f2d |
apache-2.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: ./conf/train_vits.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/44k/tts_train_vits_raw_char_tacotron ngpu: 1 seed: 777 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false collect_stats: false write_collected_feats: false max_epoch: 20 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - total_count - max keep_nbest_models: 2 nbest_averaging_interval: 0 grad_clip: -1 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 5 use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 20 batch_size: 20 valid_batch_size: null batch_bins: 500 valid_batch_bins: null train_shape_file: - exp/44k/tts_stats_raw_linear_spectrogram_char_tacotron/train/text_shape.char - exp/44k/tts_stats_raw_linear_spectrogram_char_tacotron/train/speech_shape valid_shape_file: - exp/44k/tts_stats_raw_linear_spectrogram_char_tacotron/valid/text_shape.char - exp/44k/tts_stats_raw_linear_spectrogram_char_tacotron/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/org/train/text - text - text - - dump/raw/org/train/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/raw/org/test/text - text - text - - dump/raw/org/test/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adamw optim_conf: lr: 0.0002 betas: - 0.8 - 0.99 eps: 1.0e-09 weight_decay: 0.0 scheduler: exponentiallr scheduler_conf: gamma: 0.999875 optim2: adamw optim2_conf: lr: 0.0002 betas: - 0.8 - 0.99 eps: 1.0e-09 weight_decay: 0.0 scheduler2: exponentiallr scheduler2_conf: gamma: 0.999875 generator_first: false token_list: - <blank> - <unk> - <space> - N - E - A - I - O - U - L - K - M - S - B - W - T - F - R - Y - Z - D - G - J - P - C - V - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: tacotron g2p: g2p_en feats_extract: linear_spectrogram feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null normalize: null normalize_conf: {} tts: vits tts_conf: generator_type: vits_generator generator_params: hidden_channels: 192 spks: -1 global_channels: -1 segment_size: 32 text_encoder_attention_heads: 2 text_encoder_ffn_expand: 4 text_encoder_blocks: 6 text_encoder_positionwise_layer_type: conv1d text_encoder_positionwise_conv_kernel_size: 3 text_encoder_positional_encoding_layer_type: rel_pos text_encoder_self_attention_layer_type: rel_selfattn text_encoder_activation_type: swish text_encoder_normalize_before: true text_encoder_dropout_rate: 0.1 text_encoder_positional_dropout_rate: 0.0 text_encoder_attention_dropout_rate: 0.1 use_macaron_style_in_text_encoder: true use_conformer_conv_in_text_encoder: false text_encoder_conformer_kernel_size: -1 decoder_kernel_size: 7 decoder_channels: 512 decoder_upsample_scales: - 8 - 8 - 2 - 2 decoder_upsample_kernel_sizes: - 16 - 16 - 4 - 4 decoder_resblock_kernel_sizes: - 3 - 7 - 11 decoder_resblock_dilations: - - 1 - 3 - 5 - - 1 - 3 - 5 - - 1 - 3 - 5 use_weight_norm_in_decoder: true posterior_encoder_kernel_size: 5 posterior_encoder_layers: 16 posterior_encoder_stacks: 1 posterior_encoder_base_dilation: 1 posterior_encoder_dropout_rate: 0.0 use_weight_norm_in_posterior_encoder: true flow_flows: 4 flow_kernel_size: 5 flow_base_dilation: 1 flow_layers: 4 flow_dropout_rate: 0.0 use_weight_norm_in_flow: true use_only_mean_in_flow: true stochastic_duration_predictor_kernel_size: 3 stochastic_duration_predictor_dropout_rate: 0.5 stochastic_duration_predictor_flows: 4 stochastic_duration_predictor_dds_conv_layers: 3 vocabs: 27 aux_channels: 513 discriminator_type: hifigan_multi_scale_multi_period_discriminator discriminator_params: scales: 1 scale_downsample_pooling: AvgPool1d scale_downsample_pooling_params: kernel_size: 4 stride: 2 padding: 2 scale_discriminator_params: in_channels: 1 out_channels: 1 kernel_sizes: - 15 - 41 - 5 - 3 channels: 128 max_downsample_channels: 1024 max_groups: 16 bias: true downsample_scales: - 2 - 2 - 4 - 4 - 1 nonlinear_activation: LeakyReLU nonlinear_activation_params: negative_slope: 0.1 use_weight_norm: true use_spectral_norm: false follow_official_norm: false periods: - 2 - 3 - 5 - 7 - 11 period_discriminator_params: in_channels: 1 out_channels: 1 kernel_sizes: - 5 - 3 channels: 32 downsample_scales: - 3 - 3 - 3 - 3 - 1 max_downsample_channels: 1024 bias: true nonlinear_activation: LeakyReLU nonlinear_activation_params: negative_slope: 0.1 use_weight_norm: true use_spectral_norm: false generator_adv_loss_params: average_by_discriminators: false loss_type: mse discriminator_adv_loss_params: average_by_discriminators: false loss_type: mse feat_match_loss_params: average_by_discriminators: false average_by_layers: false include_final_outputs: true mel_loss_params: fs: 44100 n_fft: 1024 hop_length: 256 win_length: null window: hann n_mels: 80 fmin: 0 fmax: null log_base: null lambda_adv: 1.0 lambda_mel: 45.0 lambda_feat_match: 2.0 lambda_dur: 1.0 lambda_kl: 1.0 sampling_rate: 44100 cache_generator_outputs: true pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list version: '202204' distributed: false ``` </details> | 768b587e04741afe36e7bacb927b6e7e |
apache-2.0 | [] | false | bert-base-en-fr-lt-no-pl-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). | b70afed8fa8833ad0581121ebdd90457 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-fr-lt-no-pl-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-fr-lt-no-pl-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). | d6505bafef084090b66fd53b41f8c28c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 200 - mixed_precision_training: Native AMP | e148bd63adb46238cff878d2b36b7094 |
mit | ['summarization'] | false | BART (large-sized model), fine-tuned on CNN Daily Mail BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart). Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. | f63d7a8344f2aef3859091634454c1de |
mit | ['summarization'] | false | How to use Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html): ```python from transformers import pipeline summarizer = pipeline("summarization", model="facebook/bart-large-cnn") ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the 2010 marriage license application, according to court documents. Prosecutors said the marriages were part of an immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages. Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted. The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18. """ print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False)) >>> [{'summary_text': 'Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002. She is believed to still be married to four men.'}] ``` | cc336e18b52b918ac6c1db83fd4b2979 |
mit | ['summarization'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } | 73870efe772a23d65c8414d6a716a524 |
apache-2.0 | ['generated_from_trainer'] | false | gpt2-small-spanish-historias-conflicto-col This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2388 | edbdb2ec53280253d6222f86e45d9aac |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-sp This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4485 - Wer: 20.6842 | 5db6f14a53371ad96ede44bd6835247e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - 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_steps: 500 - training_steps: 25000 - mixed_precision_training: Native AMP | 1f7c5e6fadb6602e0bd1f6483d14481f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 2.2671 | 0.13 | 1000 | 2.2108 | 76.2667 | | 1.4465 | 0.26 | 2000 | 1.6057 | 67.8753 | | 1.0997 | 0.39 | 3000 | 1.1928 | 54.2433 | | 0.9389 | 0.52 | 4000 | 1.0020 | 47.8307 | | 0.7881 | 0.65 | 5000 | 0.8933 | 46.0046 | | 0.7596 | 0.78 | 6000 | 0.7721 | 38.5595 | | 0.5678 | 0.91 | 7000 | 0.6903 | 36.2897 | | 0.4412 | 1.04 | 8000 | 0.6476 | 32.7473 | | 0.4239 | 1.17 | 9000 | 0.5973 | 30.8142 | | 0.3935 | 1.3 | 10000 | 0.5444 | 29.0208 | | 0.3307 | 1.43 | 11000 | 0.5024 | 27.0434 | | 0.2937 | 1.56 | 12000 | 0.4608 | 24.7318 | | 0.2471 | 1.69 | 13000 | 0.4259 | 22.8940 | | 0.2357 | 1.82 | 14000 | 0.3936 | 21.6018 | | 0.2292 | 1.95 | 15000 | 0.3776 | 20.8004 | | 0.1493 | 2.08 | 16000 | 0.4599 | 24.0491 | | 0.1708 | 2.21 | 17000 | 0.4370 | 23.3443 | | 0.1385 | 2.34 | 18000 | 0.4277 | 22.3171 | | 0.1288 | 2.47 | 19000 | 0.4050 | 21.0118 | | 0.1627 | 2.6 | 20000 | 0.4507 | 23.4004 | | 0.1675 | 2.73 | 21000 | 0.4346 | 22.8261 | | 0.159 | 2.86 | 22000 | 0.4179 | 22.2949 | | 0.1458 | 2.99 | 23000 | 0.3978 | 21.0810 | | 0.0487 | 3.12 | 24000 | 0.4456 | 20.8617 | | 0.0401 | 3.25 | 25000 | 0.4485 | 20.6842 | | 58a13b29442c3eaaf092753553a7dd01 |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-imdb This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.7710 - Accuracy: 0.8146 - F1: 0.8978 | 95ae7578ea8199527978125ad46eb3c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3417 | 0.64 | 500 | 0.1678 | 0.9286 | 0.9630 | | 0.2401 | 1.28 | 1000 | 0.1262 | 0.9525 | 0.9757 | | 0.1907 | 1.92 | 1500 | 0.2724 | 0.8963 | 0.9453 | | 0.1397 | 2.56 | 2000 | 0.2378 | 0.9247 | 0.9609 | | 0.11 | 3.2 | 2500 | 0.7710 | 0.8146 | 0.8978 | | ef38cf6939d3f8c046a4e07d021611c6 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_hubert_s596 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | dba1e1b0bf196bb7f697a0e85b4608df |
apache-2.0 | question answering | false | BERT-base-cased for QA
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
| ce2d0a1e4ed190cfb51584b16b1da4e2 |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Vietnamese texts for POS-tagging and dependency-parsing, derived from [vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/)(Universal Part-Of-Speech). | c4ddcaad8c42f089c13dd1c21461ef3b |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-vietnamese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-vietnamese-upos") pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple") nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)] print(nlp("Hai cái đầu thì tốt hơn một.")) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/bert-base-vietnamese-upos") print(nlp("Hai cái đầu thì tốt hơn một.")) ``` | 46c41a5a71e7b49d9a1de60f864f51c2 |
apache-2.0 | ['generated_from_trainer'] | false | swin-base-patch4-window7-224-20epochs-finetuned-memes This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7090 - Accuracy: 0.8478 | 10feda7275433c235349999c34eefaab |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 | 1fd7f8d4d397ddc8d95f52b4369771ec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0238 | 0.99 | 40 | 0.9636 | 0.6445 | | 0.777 | 1.99 | 80 | 0.6591 | 0.7666 | | 0.4763 | 2.99 | 120 | 0.5381 | 0.8130 | | 0.3215 | 3.99 | 160 | 0.5244 | 0.8253 | | 0.2179 | 4.99 | 200 | 0.5123 | 0.8238 | | 0.1868 | 5.99 | 240 | 0.5052 | 0.8308 | | 0.154 | 6.99 | 280 | 0.5444 | 0.8338 | | 0.1166 | 7.99 | 320 | 0.6318 | 0.8238 | | 0.1099 | 8.99 | 360 | 0.5656 | 0.8338 | | 0.0925 | 9.99 | 400 | 0.6057 | 0.8338 | | 0.0779 | 10.99 | 440 | 0.5942 | 0.8393 | | 0.0629 | 11.99 | 480 | 0.6112 | 0.8400 | | 0.0742 | 12.99 | 520 | 0.6588 | 0.8331 | | 0.0752 | 13.99 | 560 | 0.6143 | 0.8408 | | 0.0577 | 14.99 | 600 | 0.6450 | 0.8516 | | 0.0589 | 15.99 | 640 | 0.6787 | 0.8400 | | 0.0555 | 16.99 | 680 | 0.6641 | 0.8454 | | 0.052 | 17.99 | 720 | 0.7213 | 0.8524 | | 0.0589 | 18.99 | 760 | 0.6917 | 0.8470 | | 0.0506 | 19.99 | 800 | 0.7090 | 0.8478 | | df4c0e41d31bba49bb67aeb0aa5900dd |
apache-2.0 | ['t5', 'translation', 'seq2seq'] | false | t5-base-36L-ccmatrix-multi A [t5-base-36L-dutch-english-cased](https://huggingface.co/yhavinga/t5-base-36L-dutch-english-cased) model finetuned for Dutch to English and English to Dutch translation on the CCMatrix dataset. Evaluation metrics of this model are listed in the **Translation models** section below. You can use this model directly with a pipeline for text translation: ```python model_name = "yhavinga/t5-base-36L-ccmatrix-multi" from transformers import AutoTokenizer from transformers import AutoModelForSeq2SeqLM from transformers import pipeline import torch device_num = 0 if torch.cuda.is_available() else -1 device = "cpu" if device_num < 0 else f"cuda:{device_num}" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) params = {"max_length": 128, "num_beams": 4, "early_stopping": True} en_to_nl = pipeline("translation_en_to_nl", tokenizer=tokenizer, model=model, device=device_num) print(en_to_nl("""Young Wehling was hunched in his chair, his head in his hand. He was so rumpled, so still and colorless as to be virtually invisible.""", **params)[0]['translation_text']) nl_to_en = pipeline("translation_nl_to_en", tokenizer=tokenizer, model=model, device=device_num) print(nl_to_en("""De jonge Wehling zat gebogen in zijn stoel, zijn hoofd in zijn hand. Hij was zo stoffig, zo stil en kleurloos dat hij vrijwel onzichtbaar was.""", **params)[0]['translation_text']) ``` This **t5 eff** model has **728M** parameters. It was pre-trained with masked language modeling (denoise token span corruption) objective on the dataset `mc4_nl_cleaned` config `large_en_nl` for **1** epoch(s) and a duration of **17d15h**, with a sequence length of **512**, batch size **512** and **212963** total steps (**56B** tokens). Pre-training evaluation loss and accuracy are **1,05** and **0,76**. Refer to the evaluation section below for a comparison of the pre-trained models on summarization and translation. | 508c223800f0c3d91524a869e882be37 |
apache-2.0 | ['question-generation', 'multitask-model', 'idt5'] | false | idT5 for Indonesian Question Generation and Question Answering [idT5](https://huggingface.co/muchad/idt5-base) (Indonesian version of [mT5](https://huggingface.co/google/mt5-base)) is fine-tuned on 30% of [translated SQuAD v2.0](https://github.com/Wikidepia/indonesian_datasets/tree/master/question-answering/squad) for **Question Generation** and **Question Answering** tasks. | cb65f3db5d268875494e42d9450c587b |
apache-2.0 | ['question-generation', 'multitask-model', 'idt5'] | false | Question Generation [](https://colab.research.google.com/github/muchad/qaqg/blob/main/idT5_Question_Generation.ipynb) ``` from pipeline_qg import pipeline | 11ff04644ac5a697ba453b89b675094f |
apache-2.0 | ['question-generation', 'multitask-model', 'idt5'] | false | Question Answering [](https://colab.research.google.com/github/muchad/qaqg/blob/main/idT5_Question_Answering.ipynb) ``` from pipeline_qa import pipeline | dc7b76ea378499bdeefa181073036997 |
apache-2.0 | ['question-generation', 'multitask-model', 'idt5'] | false | Citation Paper: [idT5: Indonesian Version of Multilingual T5 Transformer](https://arxiv.org/abs/2302.00856) ``` @misc{https://doi.org/10.48550/arxiv.2302.00856, doi = {10.48550/ARXIV.2302.00856}, url = {https://arxiv.org/abs/2302.00856}, author = {Fuadi, Mukhlish and Wibawa, Adhi Dharma and Sumpeno, Surya}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7}, title = {idT5: Indonesian Version of Multilingual T5 Transformer}, publisher = {arXiv}, year = {2023} } ``` | 27e6853dfeb348ac39ef3007fd5631b1 |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | Usage You can load the CodeParrot model and tokenizer directly in `transformers`: ```Python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small") model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small") inputs = tokenizer("def hello_world():", return_tensors="pt") outputs = model(**inputs) ``` or with a `pipeline`: ```Python from transformers import pipeline pipe = pipeline("text-generation", model="codeparrot/codeparrot-small") outputs = pipe("def hello_world():") ``` | ec271c695f0a99da624faa8f1bdf7532 |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | Training The model was trained on the cleaned [CodeParrot 🦜 dataset](https://huggingface.co/datasets/codeparrot/codeparrot-clean) with the following settings: |Config|Value| |-------|-----| |Batch size| 192 | |Context size| 1024 | |Training steps| 150'000| |Gradient accumulation| 1| |Gradient checkpointing| False| |Learning rate| 5e-4 | |Weight decay | 0.1 | |Warmup steps| 2000 | |Schedule| Cosine | The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 29 billion tokens. | 53765cba1db51abcda12101e53214d82 |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | Performance We evaluated the model on OpenAI's [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark which consists of programming challenges: | Metric | Value | |-------|-----| |pass@1 | 3.80% | |pass@10 | 6.57% | |pass@100 | 12.78% | The [pass@k metric](https://huggingface.co/metrics/code_eval) tells the probability that at least one out of k generations passes the tests. | 1df4429afb08f3657cfb42d41c6b88ac |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | Resources - Dataset: [full](https://huggingface.co/datasets/codeparrot/codeparrot-clean), [train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-train), [valid](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid) - Code: [repository](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot) - Spaces: [generation](), [highlighting]() | 1603fbb824df181af7fe06b413c900b4 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4787 - Accuracy: 0.8138 - F1: 0.8785 - Precision: 0.8489 - Recall: 0.9101 | dcb9ad2960fd269bd2f18b771df2be3f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 390 | 0.4335 | 0.7732 | 0.8533 | 0.8209 | 0.8883 | | 0.5141 | 2.0 | 780 | 0.4196 | 0.8037 | 0.8721 | 0.8446 | 0.9015 | | 0.3368 | 3.0 | 1170 | 0.4519 | 0.8098 | 0.8779 | 0.8386 | 0.9212 | | 0.2677 | 4.0 | 1560 | 0.4787 | 0.8122 | 0.8785 | 0.8452 | 0.9146 | | 0.2677 | 5.0 | 1950 | 0.4912 | 0.8146 | 0.8794 | 0.8510 | 0.9097 | | b8e913de8461403156b55c98694e6fb6 |
apache-2.0 | ['vision', 'image-classification'] | false | densenet121-res224-nih A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers. | bdd6ab553ac21266e95cb70de98caff4 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of xray: Note: Each pretrained model has 18 outputs. The `all` model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs are listed in the field `{dataset}.pathologies` on the dataset that corresponds to the weights. Benchmarks of the modes are here: [BENCHMARKS.md](https://github.com/mlmed/torchxrayvision/blob/master/BENCHMARKS.md) ```python import urllib.request import skimage import torch import torch.nn.functional as F import torchvision import torchvision.transforms import torchxrayvision as xrv model_name = "densenet121-res224-nih" img_url = "https://huggingface.co/spaces/torchxrayvision/torchxrayvision-classifier/resolve/main/16747_3_1.jpg" img_path = "xray.jpg" urllib.request.urlretrieve(img_url, img_path) model = xrv.models.get_model(model_name, from_hf_hub=True) img = skimage.io.imread(img_path) img = xrv.datasets.normalize(img, 255) | 04dfac17b918e855e1c0bbc10b27c2e2 |
apache-2.0 | ['generated_from_trainer'] | false | binary-skills-classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1373 - Accuracy: 0.9702 | 4a4ee9c0d78e76dd6aafb4b90786b99e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.098 | 1.0 | 1557 | 0.0917 | 0.9663 | | 0.0678 | 2.0 | 3114 | 0.0982 | 0.9712 | | 0.0344 | 3.0 | 4671 | 0.1140 | 0.9712 | | 0.0239 | 4.0 | 6228 | 0.1373 | 0.9702 | | f8a3c30010372214ac71b74bb136b5d2 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'rm-vallader', 'robust-speech-event'] | false | sammy786/wav2vec2-xlsr-romansh_vallader This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - rm-vallader dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets): - Loss: 30.31 - Wer: 26.32 | 4b66c93cf83fc954bc4d73134c87ffb6 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'rm-vallader', 'robust-speech-event'] | false | Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 200 | 5.895100 | 3.136624 | 0.999713 | | 400 | 1.545700 | 0.445069 | 0.471584 | | 600 | 0.693900 | 0.340700 | 0.363088 | | 800 | 0.510600 | 0.295432 | 0.289610 | | 1000 | 0.318800 | 0.286795 | 0.281860 | | 1200 | 0.194000 | 0.307468 | 0.274110 | | 1400 | 0.151800 | 0.304849 | 0.264351 | | 1600 | 0.148300 | 0.303112 | 0.263203 | | aba7d40fa7b2d126eeed2d9a535a5451 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'rm-vallader', 'robust-speech-event'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-romansh_vallader --dataset mozilla-foundation/common_voice_8_0 --config rm-vallader --split test ``` | 43c29378946490f55fdfc3da223aff52 |
apache-2.0 | ['generated_from_trainer'] | false | test-model-lg-data This model is a fine-tuned version of [Monsia/test-model-lg-data](https://huggingface.co/Monsia/test-model-lg-data) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3354 - Wer: 0.4150 | d564d206e21d50f3ac2403532cb9f08f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 5 - mixed_precision_training: Native AMP | 2b888c9b3524c794b9664d736974b681 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0236 | 0.67 | 100 | 0.4048 | 0.4222 | | 0.0304 | 1.35 | 200 | 0.4266 | 0.4809 | | 0.0545 | 2.03 | 300 | 0.4309 | 0.4735 | | 0.0415 | 2.7 | 400 | 0.4269 | 0.4595 | | 0.033 | 3.38 | 500 | 0.4085 | 0.4537 | | 0.0328 | 4.05 | 600 | 0.3642 | 0.4224 | | 0.0414 | 4.73 | 700 | 0.3354 | 0.4150 | | 887ab48c861f3cddd308b2954f187376 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Latvian-LVTB | Feature | Description | | --- | --- | | **Name** | `lv_udv25_latvianlvtb_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) | | 1e9fb267ce85341fcf340cd4b0f6d6a2 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (6012 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `X`, `affpanc`, `affpanp`, `affpayc`, `affpayp`, `affpays`, `affpdnc`, `affpdnp`, `affpdyc`, `affpdyp`, `affpdys`, `affpgnp`, `affpgyc`, `affpgyp`, `affplnc`, `affplnp`, `affplyc`, `affplyp`, `affpnnc`, `affpnnp`, `affpnyc`, `affpnyp`, `affpnys`, `affsanc`, `affsanp`, `affsayc`, `affsayp`, `affsays`, `affsdnc`, `affsdnp`, `affsdyc`, `affsdyp`, `affsgnc`, `affsgnp`, `affsgyc`, `affsgyp`, `affsgys`, `affslnc`, `affslnp`, `affslyc`, `affslyp`, `affslys`, `affsnnc`, `affsnnp`, `affsnyc`, `affsnyp`, `affsnys`, `affsvyp`, `afmpanc`, `afmpanp`, `afmpayc`, `afmpayp`, `afmpays`, `afmpdnc`, `afmpdnp`, `afmpdyc`, `afmpdyp`, `afmpdys`, `afmpgnc`, `afmpgnp`, `afmpgyc`, `afmpgyp`, `afmpgys`, `afmplnc`, `afmplnp`, `afmplyc`, `afmplyp`, `afmplys`, `afmpnnc`, `afmpnnp`, `afmpnyc`, `afmpnyp`, `afmpnys`, `afmpvyp`, `afmsanc`, `afmsanp`, `afmsayc`, `afmsayp`, `afmsays`, `afmsdnc`, `afmsdnp`, `afmsdyc`, `afmsdyp`, `afmsdys`, `afmsgnc`, `afmsgnp`, `afmsgyc`, `afmsgyp`, `afmsgys`, `afmslnc`, `afmslnp`, `afmslyc`, `afmslyp`, `afmslys`, `afmsnnc`, `afmsnnp`, `afmsnyc`, `afmsnyp`, `afmsnys`, `arfpanp`, `arfpayp`, `arfpdnp`, `arfpdyc`, `arfpdyp`, `arfpgnp`, `arfpgyp`, `arfplnc`, `arfplnp`, `arfplyc`, `arfplyp`, `arfpnnc`, `arfpnnp`, `arfpnyp`, `arfpnys`, `arfsanp`, `arfsayp`, `arfsdnp`, `arfsdyp`, `arfsgnc`, `arfsgnp`, `arfsgyp`, `arfslnp`, `arfslyp`, `arfsnnc`, `arfsnnp`, `arfsnyc`, `arfsnyp`, `arfsvyp`, `armpanp`, `armpayc`, `armpayp`, `armpdnp`, `armpdyc`, `armpdyp`, `armpdys`, `armpgnp`, `armpgyp`, `armplnp`, `armplyc`, `armplyp`, `armpnnc`, `armpnnp`, `armpnyc`, `armpnyp`, `armsanp`, `armsayc`, `armsayp`, `armsdnp`, `armsdyp`, `armsgnp`, `armsgyp`, `armslnp`, `armslyp`, `armsnnp`, `armsnyp`, `armsnys`, `cc`, `cs`, `i`, `mcc0p0`, `mccfpa`, `mccmpn`, `mccmsa`, `mcs0p0`, `mcsfp0`, `mcsfpa`, `mcsfpd`, `mcsfpg`, `mcsfpl`, `mcsfpn`, `mcsfsa`, `mcsfsd`, `mcsfsg`, `mcsfsl`, `mcsfsn`, `mcsmpa`, `mcsmpd`, `mcsmpg`, `mcsmpl`, `mcsmpn`, `mcsmsa`, `mcsmsd`, `mcsmsg`, `mcsmsl`, `mcsmsn`, `mfcfsa`, `mfcfsg`, `mfcfsn`, `mfsmsg`, `mocfsg`, `mocmsg`, `mosfpa`, `mosfpd`, `mosfpg`, `mosfpl`, `mosfpn`, `mosfsa`, `mosfsd`, `mosfsg`, `mosfsl`, `mosfsn`, `mosmpa`, `mosmpd`, `mosmpg`, `mosmpl`, `mosmpn`, `mosmsa`, `mosmsd`, `mosmsg`, `mosmsl`, `mosmsn`, `n0msa1`, `nc0000`, `nc000g`, `nc00g1`, `nc00gg`, `ncfda4`, `ncfda5`, `ncfda6`, `ncfdd4`, `ncfdd5`, `ncfdd6`, `ncfdg4`, `ncfdg5`, `ncfdg6`, `ncfdgg`, `ncfdl4`, `ncfdl5`, `ncfdl6`, `ncfdn4`, `ncfdn5`, `ncfdn6`, `ncfpa4`, `ncfpa5`, `ncfpa6`, `ncfpar`, `ncfpd4`, `ncfpd5`, `ncfpd6`, `ncfpdr`, `ncfpg1`, `ncfpg2`, `ncfpg4`, `ncfpg5`, `ncfpg6`, `ncfpgg`, `ncfpl4`, `ncfpl5`, `ncfpl6`, `ncfpn1`, `ncfpn4`, `ncfpn5`, `ncfpn6`, `ncfpnr`, `ncfsa1`, `ncfsa2`, `ncfsa4`, `ncfsa5`, `ncfsa6`, `ncfsar`, `ncfsd4`, `ncfsd5`, `ncfsd6`, `ncfsg1`, `ncfsg4`, `ncfsg5`, `ncfsg6`, `ncfsgg`, `ncfsgr`, `ncfsl1`, `ncfsl4`, `ncfsl5`, `ncfsl6`, `ncfslr`, `ncfsn4`, `ncfsn5`, `ncfsn6`, `ncfsnr`, `ncfsv4`, `ncfsv5`, `ncfva4`, `ncfva5`, `ncfvd5`, `ncfvg4`, `ncfvg5`, `ncfvl4`, `ncfvl5`, `ncfvn5`, `ncm000`, `ncmda1`, `ncmda2`, `ncmda6`, `ncmdd1`, `ncmdd2`, `ncmdd3`, `ncmdd6`, `ncmdg1`, `ncmdg2`, `ncmdg3`, `ncmdg6`, `ncmdgg`, `ncmdl1`, `ncmdl2`, `ncmdn1`, `ncmdn2`, `ncmdn6`, `ncmpa1`, `ncmpa2`, `ncmpa3`, `ncmpa4`, `ncmpd1`, `ncmpd2`, `ncmpd3`, `ncmpd5`, `ncmpg1`, `ncmpg2`, `ncmpg3`, `ncmpg4`, `ncmpg5`, `ncmpg6`, `ncmpgg`, `ncmpl1`, `ncmpl2`, `ncmpl3`, `ncmpl4`, `ncmpn0`, `ncmpn1`, `ncmpn2`, `ncmpn3`, `ncmpn4`, `ncmpn5`, `ncmpv1`, `ncmpv2`, `ncmsa1`, `ncmsa2`, `ncmsa3`, `ncmsa4`, `ncmsa5`, `ncmsd1`, `ncmsd2`, `ncmsd3`, `ncmsd4`, `ncmsg0`, `ncmsg1`, `ncmsg2`, `ncmsg3`, `ncmsg4`, `ncmsgg`, `ncmsgr`, `ncmsl1`, `ncmsl2`, `ncmsl3`, `ncmsl4`, `ncmsl5`, `ncmsn1`, `ncmsn2`, `ncmsn3`, `ncmsn4`, `ncmsnr`, `ncmsv1`, `ncmsv2`, `ncmva1`, `ncmva3`, `ncmvd1`, `ncmvd3`, `ncmvg1`, `ncmvg3`, `ncmvl1`, `ncmvl3`, `ncmvn1`, `ncmvn3`, `np0000`, `npfda4`, `npfdd4`, `npfdd6`, `npfdg1`, `npfdg4`, `npfdg6`, `npfdl4`, `npfdl6`, `npfdn4`, `npfdn5`, `npfdn6`, `npfpa5`, `npfpd5`, `npfpg2`, `npfpg4`, `npfpn4`, `npfpn5`, `npfsa4`, `npfsa5`, `npfsa6`, `npfsd4`, `npfsd5`, `npfsg1`, `npfsg3`, `npfsg4`, `npfsg5`, `npfsg6`, `npfsl4`, `npfsl5`, `npfsl6`, `npfsn3`, `npfsn4`, `npfsn5`, `npfsn6`, `npfsv4`, `npfsv5`, `npmda1`, `npmda2`, `npmdd1`, `npmdd2`, `npmdg1`, `npmdg2`, `npmdl1`, `npmdl2`, `npmdn1`, `npmdn2`, `npmpa1`, `npmpd1`, `npmpd2`, `npmpg1`, `npmpg2`, `npmpgg`, `npmpl1`, `npmpl2`, `npmpn1`, `npmpn2`, `npmsa1`, `npmsa2`, `npmsa3`, `npmsa4`, `npmsa5`, `npmsd1`, `npmsd2`, `npmsd3`, `npmsd4`, `npmsd5`, `npmsg0`, `npmsg1`, `npmsg2`, `npmsg3`, `npmsg4`, `npmsg5`, `npmsl1`, `npmsl2`, `npmsn1`, `npmsn2`, `npmsn3`, `npmsn4`, `npmsn5`, `npmsv1`, `npmsv2`, `pd0fpan`, `pd0fpdn`, `pd0fpgn`, `pd0fpln`, `pd0fpnn`, `pd0fsan`, `pd0fsdn`, `pd0fsgn`, `pd0fsln`, `pd0fsnn`, `pd0mpan`, `pd0mpdn`, `pd0mpgn`, `pd0mpln`, `pd0mply`, `pd0mpnn`, `pd0msan`, `pd0msdn`, `pd0msgn`, `pd0msln`, `pd0msnn`, `pd3fpan`, `pd3fpdn`, `pd3fpgn`, `pd3fpln`, `pd3fpnn`, `pd3fsan`, `pd3fsdn`, `pd3fsgn`, `pd3fsln`, `pd3fsnn`, `pd3mpan`, `pd3mpdn`, `pd3mpgn`, `pd3mpln`, `pd3mpnn`, `pd3msan`, `pd3msdn`, `pd3msgn`, `pd3msln`, `pd3msnn`, `pg0fpan`, `pg0fpdn`, `pg0fpgn`, `pg0fpln`, `pg0fpnn`, `pg0fsan`, `pg0fsdn`, `pg0fsgn`, `pg0fsln`, `pg0fsnn`, `pg0mpan`, `pg0mpdn`, `pg0mpgn`, `pg0mpln`, `pg0mpnn`, `pg0msan`, `pg0msdn`, `pg0msgn`, `pg0msln`, `pg0msnn`, `pi000an`, `pi000ay`, `pi000dn`, `pi000dy`, `pi000gn`, `pi000gy`, `pi000nn`, `pi000ny`, `pi0fpan`, `pi0fpay`, `pi0fpdn`, `pi0fpgn`, `pi0fpgy`, `pi0fpln`, `pi0fply`, `pi0fpnn`, `pi0fpny`, `pi0fsan`, `pi0fsay`, `pi0fsdn`, `pi0fsgn`, `pi0fsgy`, `pi0fsln`, `pi0fsnn`, `pi0fsny`, `pi0mpan`, `pi0mpay`, `pi0mpdn`, `pi0mpgn`, `pi0mpgy`, `pi0mpln`, `pi0mpnn`, `pi0mpny`, `pi0msan`, `pi0msay`, `pi0msdn`, `pi0msdy`, `pi0msgn`, `pi0msgy`, `pi0msln`, `pi0msly`, `pi0msnn`, `pi0msny`, `pi3msnn`, `pp10pan`, `pp10pdn`, `pp10pgn`, `pp10pln`, `pp10pnn`, `pp10san`, `pp10sdn`, `pp10sgn`, `pp10sln`, `pp10snn`, `pp1mpgn`, `pp20pan`, `pp20pdn`, `pp20pgn`, `pp20pnn`, `pp20san`, `pp20sdn`, `pp20sgn`, `pp20sln`, `pp20snn`, `pp2fsln`, `pp3fpan`, `pp3fpdn`, `pp3fpgn`, `pp3fpnn`, `pp3fsan`, `pp3fsdn`, `pp3fsgn`, `pp3fsln`, `pp3fsnn`, `pp3mpan`, `pp3mpdn`, `pp3mpgn`, `pp3mpln`, `pp3mpnn`, `pp3msan`, `pp3msdn`, `pp3msgn`, `pp3msln`, `pp3msnn`, `pq000an`, `pq000dn`, `pq000gn`, `pq000nn`, `pq0fpan`, `pq0fpnn`, `pq0fsnn`, `pq0mpnn`, `pq0msan`, `pq0msdn`, `pq0msln`, `pq0msnn`, `pr000an`, `pr000dn`, `pr000gn`, `pr000nn`, `pr00pgn`, `pr0fpan`, `pr0fpdn`, `pr0fpgn`, `pr0fpln`, `pr0fpnn`, `pr0fsan`, `pr0fsdn`, `pr0fsgn`, `pr0fsln`, `pr0fsnn`, `pr0mpan`, `pr0mpdn`, `pr0mpgn`, `pr0mpln`, `pr0mpnn`, `pr0msan`, `pr0msdn`, `pr0msgn`, `pr0msln`, `pr0msnn`, `ps0fpan`, `ps0fpdn`, `ps0fpgn`, `ps0fpln`, `ps0fpnn`, `ps0fsan`, `ps0fsdn`, `ps0fsgn`, `ps0fsln`, `ps0fsnn`, `ps0mpan`, `ps0mpdn`, `ps0mpgn`, `ps0mpln`, `ps0mpnn`, `ps0msan`, `ps0msdn`, `ps0msgn`, `ps0msln`, `ps0msnn`, `ps10sgn`, `ps1mpnn`, `ps1msgn`, `ps1msnn`, `ps2fsnn`, `px000an`, `px000dn`, `px000gn`, `px000ln`, `q`, `r0c`, `r0m`, `r0p`, `r0q`, `r0t`, `rcc`, `rcm`, `rcp`, `rcq`, `rct`, `rpc`, `rpm`, `rpp`, `rpq`, `rpt`, `rrm`, `rrp`, `rrt`, `rsm`, `rsp`, `rsq`, `rst`, `sp00`, `sppd`, `sppg`, `spsa`, `spsd`, `spsg`, `stpg`, `stsg`, `vcnc0ii00an`, `vcnc0ii00ay`, `vcnd0ii00an`, `vcnifi130an`, `vcnifii1pan`, `vcnifii1pay`, `vcnifii1san`, `vcnifii1say`, `vcnifii2pan`, `vcnifii2pay`, `vcnifii2san`, `vcnifii2say`, `vcnifii30an`, `vcnifii30ay`, `vcnipii1pan`, `vcnipii1pay`, `vcnipii1san`, `vcnipii1say`, `vcnipii2pan`, `vcnipii2pay`, `vcnipii2san`, `vcnipii2say`, `vcnipii30an`, `vcnipii30ay`, `vcnisii1pan`, `vcnisii1pay`, `vcnisii1san`, `vcnisii1say`, `vcnisii2pay`, `vcnisii30an`, `vcnisii30ay`, `vcnist330an`, `vcnm0ii2pan`, `vcnm0ii2san`, `vcnn0ii000n`, `vcnn0ii000y`, `vcnn0ii00an`, `vcnn0t3000n`, `vcnpdfpnasnpn`, `vcnpdfsaasypn`, `vcnpdfsgapypn`, `vcnpdfsnapnpn`, `vcnpdfsnasnpn`, `vcnpdmplasypn`, `vcnpdmpnasnpn`, `vcnpdmsaasnpy`, `vcnpdmsaasypn`, `vcnpdmsnasn0n`, `vcnpdmsnasnpn`, `vcnppfsn0000n`, `vcnppmpn0000n`, `vcnppmsn0000n`, `vcnpu0000000n`, `vcnrfii00an`, `vcnrpii00an`, `vcnrpii00ay`, `venipi130an`, `venipi130ay`, `venisi130an`, `veyifii30an`, `veyipi130an`, `veyipi130ay`, `veyipi330an`, `veyipii30an`, `veyipii30ay`, `veyisi130an`, `veyisi330an`, `veyisii30an`, `veyisii30ay`, `veypdmpnasnpn`, `veypdmsnasnpn`, `vgnpdmsgapypn`, `vmnc0i100an`, `vmnc0i100ay`, `vmnc0i10say`, `vmnc0i200an`, `vmnc0i300an`, `vmnc0i300ay`, `vmnc0ii000n`, `vmnc0ii00an`, `vmnc0ii00ay`, `vmnc0t100an`, `vmnc0t100ay`, `vmnc0t200an`, `vmnc0t200ay`, `vmnc0t300an`, `vmnc0t300ay`, `vmnc0ti00an`, `vmnd0i100an`, `vmnd0i200an`, `vmnd0i300an`, `vmnd0ii00an`, `vmnd0t100an`, `vmnd0t130an`, `vmnd0t200an`, `vmnd0t300an`, `vmnd0ti00an`, `vmnd0ti00pn`, `vmnifi11pan`, `vmnifi11pay`, `vmnifi11san`, `vmnifi11say`, `vmnifi12pan`, `vmnifi12san`, `vmnifi130an`, `vmnifi130ay`, `vmnifi13san`, `vmnifi21pan`, `vmnifi21san`, `vmnifi21say`, `vmnifi22san`, `vmnifi230an`, `vmnifi230ay`, `vmnifi31pan`, `vmnifi32san`, `vmnifi32say`, `vmnifi330an`, `vmnifi330ay`, `vmnifii1san`, `vmnifii2san`, `vmnifii30an`, `vmnifii30ay`, `vmnift11pan`, `vmnift11pay`, `vmnift11san`, `vmnift11say`, `vmnift12pan`, `vmnift12san`, `vmnift12say`, `vmnift130an`, `vmnift130ay`, `vmnift21pan`, `vmnift21pay`, `vmnift21san`, `vmnift21say`, `vmnift22pan`, `vmnift22pay`, `vmnift22san`, `vmnift22say`, `vmnift230an`, `vmnift230ay`, `vmnift31pan`, `vmnift31pay`, `vmnift31san`, `vmnift31say`, `vmnift32pan`, `vmnift32san`, `vmnift32say`, `vmnift330an`, `vmnift330ay`, `vmnifti1san`, `vmnifti2san`, `vmnifti30an`, `vmnim0230an`, `vmnipi11pan`, `vmnipi11pay`, `vmnipi11san`, `vmnipi12pan`, `vmnipi12san`, `vmnipi130an`, `vmnipi130ay`, `vmnipi21pan`, `vmnipi21san`, `vmnipi22pan`, `vmnipi22pay`, `vmnipi22san`, `vmnipi230an`, `vmnipi230ay`, `vmnipi23san`, `vmnipi31pan`, `vmnipi31san`, `vmnipi31say`, `vmnipi32pan`, `vmnipi32san`, `vmnipi330an`, `vmnipi330ay`, `vmnipii1pan`, `vmnipii1san`, `vmnipii2pan`, `vmnipii2pay`, `vmnipii2san`, `vmnipii30an`, `vmnipii30ay`, `vmnipt110an`, `vmnipt11pan`, `vmnipt11pay`, `vmnipt11san`, `vmnipt11say`, `vmnipt12pan`, `vmnipt12san`, `vmnipt12say`, `vmnipt130an`, `vmnipt130ay`, `vmnipt21pan`, `vmnipt21pay`, `vmnipt21san`, `vmnipt21say`, `vmnipt22pan`, `vmnipt22san`, `vmnipt22say`, `vmnipt230an`, `vmnipt230ay`, `vmnipt23san`, `vmnipt31pan`, `vmnipt31pay`, `vmnipt31san`, `vmnipt31say`, `vmnipt32pan`, `vmnipt32san`, `vmnipt32say`, `vmnipt330an`, `vmnipt330ay`, `vmnipti1pan`, `vmnipti1san`, `vmnipti2pan`, `vmnipti30an`, `vmnipti30ay`, `vmnipti3san`, `vmnisi11pan`, `vmnisi11san`, `vmnisi11say`, `vmnisi12san`, `vmnisi130an`, `vmnisi130ay`, `vmnisi21pan`, `vmnisi21san`, `vmnisi22pan`, `vmnisi230an`, `vmnisi230ay`, `vmnisi31pan`, `vmnisi31san`, `vmnisi31say`, `vmnisi330an`, `vmnisi330ay`, `vmnisii1pan`, `vmnisii1pay`, `vmnisii1san`, `vmnisii2san`, `vmnisii30an`, `vmnisii30ay`, `vmnist11pan`, `vmnist11pay`, `vmnist11san`, `vmnist11say`, `vmnist12pan`, `vmnist12san`, `vmnist130an`, `vmnist130ay`, `vmnist21pan`, `vmnist21pay`, `vmnist21san`, `vmnist21say`, `vmnist230an`, `vmnist230ay`, `vmnist31pan`, `vmnist31pay`, `vmnist31san`, `vmnist31say`, `vmnist32pan`, `vmnist32san`, `vmnist32say`, `vmnist330an`, `vmnist330ay`, `vmnisti1san`, `vmnisti30an`, `vmnisti30ay`, `vmnm0i12pan`, `vmnm0i12pay`, `vmnm0i12san`, `vmnm0i12say`, `vmnm0i21san`, `vmnm0i22pan`, `vmnm0i22san`, `vmnm0i32pan`, `vmnm0i32san`, `vmnm0i32say`, `vmnm0ii1pan`, `vmnm0ii2pan`, `vmnm0ii2san`, `vmnm0t11san`, `vmnm0t12pan`, `vmnm0t12pay`, `vmnm0t12san`, `vmnm0t12say`, `vmnm0t130an`, `vmnm0t21san`, `vmnm0t21say`, `vmnm0t22pan`, `vmnm0t22san`, `vmnm0t22say`, `vmnm0t230an`, `vmnm0t31san`, `vmnm0t32pan`, `vmnm0t32pay`, `vmnm0t32san`, `vmnm0t32say`, `vmnm0ti2pan`, `vmnm0ti2san`, `vmnmpi130ay`, `vmnmpi32san`, `vmnmpii2pan`, `vmnmpt12pan`, `vmnmpt12say`, `vmnmpt130ay`, `vmnmpt22san`, `vmnmpt32pan`, `vmnmpt32san`, `vmnn0i1000n`, `vmnn0i1000y`, `vmnn0i100an`, `vmnn0i130an`, `vmnn0i2000n`, `vmnn0i2000y`, `vmnn0i200an`, `vmnn0i3000n`, `vmnn0i3000y`, `vmnn0i300an`, `vmnn0ii000n`, `vmnn0ii000y`, `vmnn0t1000n`, `vmnn0t1000y`, `vmnn0t100an`, `vmnn0t2000n`, `vmnn0t2000y`, `vmnn0t200an`, `vmnn0t3000n`, `vmnn0t3000y`, `vmnn0t300an`, `vmnn0ti000n`, `vmnn0ti00an`, `vmnpdfpaapnpn`, `vmnpdfpaapypn`, `vmnpdfpaasnpn`, `vmnpdfpaasypn`, `vmnpdfpappnpn`, `vmnpdfpappnpy`, `vmnpdfpappypn`, `vmnpdfpapsnpn`, `vmnpdfpapsnpy`, `vmnpdfpapsypn`, `vmnpdfpdapnpn`, `vmnpdfpdapnpy`, `vmnpdfpdapypn`, `vmnpdfpdapysn`, `vmnpdfpdasnpn`, `vmnpdfpdasypn`, `vmnpdfpdppnpn`, `vmnpdfpdppnpy`, `vmnpdfpdppypn`, `vmnpdfpdpsnpn`, `vmnpdfpdpsypn`, `vmnpdfpdpsypy`, `vmnpdfpgapncn`, `vmnpdfpgapypn`, `vmnpdfpgppnpn`, `vmnpdfpgppnpy`, `vmnpdfpgppypn`, `vmnpdfpgpsnpn`, `vmnpdfpgpsypn`, `vmnpdfplapnpn`, `vmnpdfplapypn`, `vmnpdfplasnpn`, `vmnpdfplasypn`, `vmnpdfplppnpy`, `vmnpdfplpsnpn`, `vmnpdfplpsypn`, `vmnpdfpnapn0n`, `vmnpdfpnapnpn`, `vmnpdfpnapnpy`, `vmnpdfpnapypn`, `vmnpdfpnasnpn`, `vmnpdfpnasypn`, `vmnpdfpnasypy`, `vmnpdfpnppnpn`, `vmnpdfpnppnpy`, `vmnpdfpnppypn`, `vmnpdfpnpsnpn`, `vmnpdfpnpsnpy`, `vmnpdfpnpsypn`, `vmnpdfpnpsypy`, `vmnpdfsaapn0n`, `vmnpdfsaapncn`, `vmnpdfsaapnpn`, `vmnpdfsaapnpy`, `vmnpdfsaapypn`, `vmnpdfsaasnpn`, `vmnpdfsaasypn`, `vmnpdfsappnpn`, `vmnpdfsappnpy`, `vmnpdfsappypn`, `vmnpdfsappypy`, `vmnpdfsapsncn`, `vmnpdfsapsnpn`, `vmnpdfsapsnpy`, `vmnpdfsapsypn`, `vmnpdfsdapnpn`, `vmnpdfsdapypn`, `vmnpdfsdasnpn`, `vmnpdfsdasypn`, `vmnpdfsdppnpn`, `vmnpdfsdppypn`, `vmnpdfsdpsnpn`, `vmnpdfsdpsnpy`, `vmnpdfsdpsypn`, `vmnpdfsgapnpn`, `vmnpdfsgapypn`, `vmnpdfsgasnpn`, `vmnpdfsgasypn`, `vmnpdfsgppnpn`, `vmnpdfsgppnpy`, `vmnpdfsgppypn`, `vmnpdfsgpsnpn`, `vmnpdfsgpsypn`, `vmnpdfsgpsypy`, `vmnpdfslapnpn`, `vmnpdfslapypn`, `vmnpdfslasnpn`, `vmnpdfslasypn`, `vmnpdfslppnpn`, `vmnpdfslppypn`, `vmnpdfslpsnpn`, `vmnpdfslpsypn`, `vmnpdfslpsypy`, `vmnpdfsnapnpn`, `vmnpdfsnapnpy`, `vmnpdfsnapypn`, `vmnpdfsnapysn`, `vmnpdfsnasn0n`, `vmnpdfsnasnpn`, `vmnpdfsnasnpy`, `vmnpdfsnasypn`, `vmnpdfsnppncn`, `vmnpdfsnppnpn`, `vmnpdfsnppnpy`, `vmnpdfsnppypn`, `vmnpdfsnppypy`, `vmnpdfsnpsncn`, `vmnpdfsnpsnpn`, `vmnpdfsnpsnpy`, `vmnpdfsnpsypn`, `vmnpdfsnpsypy`, `vmnpdmpaapnpn`, `vmnpdmpaapycn`, `vmnpdmpaapypn`, `vmnpdmpaasnpn`, `vmnpdmpaasypn`, `vmnpdmpappnpn`, `vmnpdmpappypn`, `vmnpdmpapsnpn`, `vmnpdmpapsnpy`, `vmnpdmpapsypn`, `vmnpdmpapsypy`, `vmnpdmpdapnpn`, `vmnpdmpdapypn`, `vmnpdmpdasnpn`, `vmnpdmpdasypn`, `vmnpdmpdppnpn`, `vmnpdmpdppycn`, `vmnpdmpdppypn`, `vmnpdmpdpsnpn`, `vmnpdmpdpsnpy`, `vmnpdmpdpsycn`, `vmnpdmpdpsypn`, `vmnpdmpdpsypy`, `vmnpdmpgapnpn`, `vmnpdmpgapypn`, `vmnpdmpgasnpn`, `vmnpdmpgasypn`, `vmnpdmpgppypn`, `vmnpdmpgpsnpn`, `vmnpdmpgpsypn`, `vmnpdmpgpsypy`, `vmnpdmplapnpn`, `vmnpdmplapypn`, `vmnpdmplpsnpn`, `vmnpdmplpsypn`, `vmnpdmpnapnpn`, `vmnpdmpnapypn`, `vmnpdmpnasnpn`, `vmnpdmpnasypn`, `vmnpdmpnppn0n`, `vmnpdmpnppnpn`, `vmnpdmpnppnpy`, `vmnpdmpnppypn`, `vmnpdmpnpsnpn`, `vmnpdmpnpsnpy`, `vmnpdmpnpsypn`, `vmnpdmpnpsypy`, `vmnpdmpvppypn`, `vmnpdmsaapnpn`, `vmnpdmsaapypn`, `vmnpdmsaasnpn`, `vmnpdmsaasypn`, `vmnpdmsappnpn`, `vmnpdmsappnpy`, `vmnpdmsappypn`, `vmnpdmsappypy`, `vmnpdmsapsnpn`, `vmnpdmsapsnpy`, `vmnpdmsapsypn`, `vmnpdmsapsypy`, `vmnpdmsdapnpn`, `vmnpdmsdapypn`, `vmnpdmsdasnpn`, `vmnpdmsdppnpn`, `vmnpdmsdppypn`, `vmnpdmsdppypy`, `vmnpdmsdpsnpn`, `vmnpdmsdpsypn`, `vmnpdmsdpsypy`, `vmnpdmsgapnpn`, `vmnpdmsgapypn`, `vmnpdmsgasnpn`, `vmnpdmsgasypn`, `vmnpdmsgppnpn`, `vmnpdmsgppy0n`, `vmnpdmsgppypn`, `vmnpdmsgppypy`, `vmnpdmsgpsnpn`, `vmnpdmsgpsycn`, `vmnpdmsgpsypn`, `vmnpdmsgpsypy`, `vmnpdmslapnpn`, `vmnpdmslapypn`, `vmnpdmslasnpn`, `vmnpdmslasypn`, `vmnpdmslppnpn`, `vmnpdmslppy0n`, `vmnpdmslppypn`, `vmnpdmslpsnpn`, `vmnpdmslpsypn`, `vmnpdmsnapnpn`, `vmnpdmsnapnpy`, `vmnpdmsnapypn`, `vmnpdmsnasn0n`, `vmnpdmsnasnpn`, `vmnpdmsnasnpy`, `vmnpdmsnasypn`, `vmnpdmsnppnpn`, `vmnpdmsnppnpy`, `vmnpdmsnppypn`, `vmnpdmsnppypy`, `vmnpdmsnpsnpn`, `vmnpdmsnpsnpy`, `vmnpdmsnpsycn`, `vmnpdmsnpsypn`, `vmnpdmsnpsypy`, `vmnppfpn0000y`, `vmnppfsn0000n`, `vmnppmpn0000n`, `vmnppmpnap00n`, `vmnppmpnap0pn`, `vmnppmpnap0py`, `vmnppmsn0000n`, `vmnpu0000000n`, `vmnpu0000000y`, `vmnpu000000pn`, `vmnpu00000n0n`, `vmnpu000apnpn`, `vmnpumpgpsnpn`, `vmnr0t100an`, `vmnr0t3000n`, `vmnrfi100an`, `vmnrft100an`, `vmnrft200an`, `vmnrft200ay`, `vmnrft300an`, `vmnrpi1000y`, `vmnrpi100an`, `vmnrpi2000n`, `vmnrpi200an`, `vmnrpi300an`, `vmnrpii00an`, `vmnrpii00ay`, `vmnrpt100an`, `vmnrpt100ay`, `vmnrpt200an`, `vmnrpt200ay`, `vmnrpt300an`, `vmnrpt300ay`, `vmyc0i100an`, `vmyc0i100ay`, `vmyc0i200an`, `vmyc0i200ay`, `vmyc0i300an`, `vmyc0i300ay`, `vmyc0t100an`, `vmyc0t200an`, `vmyc0t300an`, `vmyc0ti00an`, `vmyd0i100an`, `vmyd0i200an`, `vmyd0i300an`, `vmyd0ii00an`, `vmyd0t100an`, `vmyd0t200an`, `vmyd0t300an`, `vmyd0ti00an`, `vmyifi11pan`, `vmyifi11san`, `vmyifi11say`, `vmyifi12pan`, `vmyifi12san`, `vmyifi130an`, `vmyifi130ay`, `vmyifi21san`, `vmyifi230an`, `vmyifi230ay`, `vmyifi31pan`, `vmyifi31san`, `vmyifi31say`, `vmyifi32san`, `vmyifi330an`, `vmyifi330ay`, `vmyift11pan`, `vmyift130an`, `vmyift21san`, `vmyift31pan`, `vmyift32san`, `vmyift330an`, `vmyifti1san`, `vmyifti30an`, `vmyipi110ay`, `vmyipi11pan`, `vmyipi11san`, `vmyipi12pan`, `vmyipi12san`, `vmyipi12say`, `vmyipi130an`, `vmyipi130ay`, `vmyipi21pan`, `vmyipi21san`, `vmyipi21say`, `vmyipi22pan`, `vmyipi22san`, `vmyipi230an`, `vmyipi230ay`, `vmyipi31pan`, `vmyipi31san`, `vmyipi31say`, `vmyipi32pan`, `vmyipi32san`, `vmyipi330an`, `vmyipi330ay`, `vmyipii1pan`, `vmyipt11pan`, `vmyipt11san`, `vmyipt12san`, `vmyipt130an`, `vmyipt130ay`, `vmyipt21san`, `vmyipt22san`, `vmyipt230an`, `vmyipt31pan`, `vmyipt31san`, `vmyipt31say`, `vmyipt32pan`, `vmyipt32san`, `vmyipt32say`, `vmyipt330an`, `vmyipt330ay`, `vmyipti1pan`, `vmyipti1san`, `vmyipti2pan`, `vmyipti30an`, `vmyipti30ay`, `vmyisi11pan`, `vmyisi11san`, `vmyisi12san`, `vmyisi130an`, `vmyisi130ay`, `vmyisi13pan`, `vmyisi21pan`, `vmyisi21san`, `vmyisi22san`, `vmyisi230an`, `vmyisi230ay`, `vmyisi31pan`, `vmyisi31san`, `vmyisi31say`, `vmyisi32san`, `vmyisi330an`, `vmyisi330ay`, `vmyisii1san`, `vmyisii30an`, `vmyist11pan`, `vmyist11san`, `vmyist130an`, `vmyist21pan`, `vmyist230an`, `vmyist230ay`, `vmyist31pan`, `vmyist31san`, `vmyist32pan`, `vmyist330an`, `vmyist330ay`, `vmyisti1pan`, `vmyisti1san`, `vmyisti30an`, `vmyisti30ay`, `vmym0i11san`, `vmym0i12pan`, `vmym0i12san`, `vmym0i12say`, `vmym0i22pan`, `vmym0i22san`, `vmym0i22say`, `vmym0i32pan`, `vmym0i32pay`, `vmym0i32san`, `vmym0t22pan`, `vmym0t22san`, `vmym0t32pan`, `vmym0t32san`, `vmympi32san`, `vmympt32san`, `vmyn0i1000n`, `vmyn0i1000y`, `vmyn0i2000n`, `vmyn0i3000n`, `vmyn0i3000y`, `vmyn0ii000n`, `vmyn0ii00an`, `vmyn0t1000n`, `vmyn0t1000y`, `vmyn0t100an`, `vmyn0t2000n`, `vmyn0t3000n`, `vmyn0t3000y`, `vmyn0ti000n`, `vmypdfpaasnpn`, `vmypdfpnasnpn`, `vmypdfpnasnpy`, `vmypdfpnasypn`, `vmypdfpnppypn`, `vmypdfsaasnpn`, `vmypdfsaasnpy`, `vmypdfsnasn0n`, `vmypdfsnasnpn`, `vmypdmpaapnpn`, `vmypdmpaasypn`, `vmypdmpnasn0n`, `vmypdmpnasnpn`, `vmypdmsaapnpn`, `vmypdmsaasnpn`, `vmypdmsnasn0n`, `vmypdmsnasnpn`, `vmypdmsnasnpy`, `vmypdmsnpsnpn`, `vmyppf0n0000n`, `vmyppfsn0000n`, `vmyppfsn0000y`, `vmyppm0n0000n`, `vmyppmpn0000n`, `vmyppms00000n`, `vmyppmsn0000n`, `vmypu0000000n`, `vmypu0000000y`, `vmypu000000pn`, `vmypumsnasnpn`, `vmyrfi100an`, `vmyrpi200an`, `vmyrpi300an`, `vmyrpt100an`, `vmyrpt300an`, `vmyrpt300ay`, `vonc0i100an`, `vonc0i100ay`, `vonc0i300an`, `vonc0i300ay`, `vonc0t300ay`, `vond0i100an`, `vond0t300an`, `vondpi300an`, `vonifi11pay`, `vonifi12pay`, `vonifi130an`, `vonifi130ay`, `vonifi230an`, `vonifi31pan`, `vonifi31san`, `vonifi31say`, `vonifi32san`, `vonifi32say`, `vonifi330an`, `vonifi330ay`, `vonift31say`, `vonift32san`, `vonift330an`, `vonift330ay`, `vonipi11pan`, `vonipi11pay`, `vonipi11san`, `vonipi11say`, `vonipi12pan`, `vonipi130an`, `vonipi130ay`, `vonipi21pan`, `vonipi230an`, `vonipi230ay`, `vonipi300ay`, `vonipi31pan`, `vonipi31pay`, `vonipi31san`, `vonipi31say`, `vonipi32pan`, `vonipi32pay`, `vonipi32san`, `vonipi32say`, `vonipi330an`, `vonipi330ay`, `vonipii30an`, `vonipt130an`, `vonipt230an`, `vonipt31pan`, `vonipt31pay`, `vonipt31san`, `vonipt31say`, `vonipt32pan`, `vonipt32san`, `vonipt330an`, `vonipt330ay`, `vonisi11san`, `vonisi11say`, `vonisi130an`, `vonisi130ay`, `vonisi230an`, `vonisi31pan`, `vonisi31pay`, `vonisi31san`, `vonisi31say`, `vonisi32pan`, `vonisi330an`, `vonisi330ay`, `vonist130an`, `vonist330an`, `vonist330ay`, `vonm0i32san`, `vonmpi32san`, `vonn0i3000n`, `vonn0t3000n`, `vonpdfpn00npy`, `vonpdfpnasnpn`, `vonpdfsnasnpn`, `vonpdfsnasnpy`, `vonpdmpnasnpn`, `vonpdmsnasnpn`, `vonpdmsnpsnpn`, `vonpdmsnpsypn`, `vonppfsn0000n`, `vonppmsn0000n`, `vonppmsn0000y`, `vonpu0000000n`, `vonpu0000000y`, `vonrft300an`, `vonrpi100ay`, `vonrpi300an`, `vonrpi300ay`, `vonrpt300an`, `vonrpt300ay`, `voyc0i100an`, `voyc0i100ay`, `voyc0i300an`, `voyc0i300ay`, `voyc0t300an`, `voyd0i100an`, `voyifi12san`, `voyifi130an`, `voyifi330an`, `voyifi330ay`, `voyifii30an`, `voyipi11pan`, `voyipi11san`, `voyipi11say`, `voyipi130an`, `voyipi130ay`, `voyipi230ay`, `voyipi300ay`, `voyipi31pan`, `voyipi31san`, `voyipi31say`, `voyipi32pan`, `voyipi330an`, `voyipi330ay`, `voyipii30an`, `voyipt11pan`, `voyipt130an`, `voyipt31san`, `voyipt32san`, `voyipt330an`, `voyipt330ay`, `voyisi11pan`, `voyisi11san`, `voyisi11say`, `voyisi130an`, `voyisi230an`, `voyisi31san`, `voyisi31say`, `voyisi330an`, `voyisi330ay`, `voyist11san`, `voyist330an`, `voym0i12pay`, `voyn0i1000n`, `voyn0i3000n`, `voyn0t1000n`, `voyp0msnap00n`, `voypdfsnasnpn`, `voypdmpnasnpn`, `voypdmsnasnpn`, `voypdmsnasnpy`, `voypu0000000n`, `voyrfi100an`, `voyrpi100an`, `voyrpi300ay`, `vpnc0i100an`, `vpnc0i300an`, `vpnd0i100an`, `vpnd0t100an`, `vpnifi12san`, `vpnifi130an`, `vpnifi31pan`, `vpnifi330an`, `vpnift130an`, `vpnift31pan`, `vpnipi11pan`, `vpnipi11pay`, `vpnipi11san`, `vpnipi130an`, `vpnipi130ay`, `vpnipi330an`, `vpnipt11pan`, `vpnipt11san`, `vpnipt130an`, `vpnipt31pan`, `vpnisi11pan`, `vpnisi11san`, `vpnisi11say`, `vpnisi130an`, `vpnisi130ay`, `vpnisi230an`, `vpnisi31san`, `vpnisi330an`, `vpnist11san`, `vpnist130an`, `vpnist330an`, `vpnisti30an`, `vpnm0i12san`, `vpnm0i32san`, `vpnm0t32san`, `vpnn0i1000n`, `vpnn0i3000n`, `vpnn0t1000n`, `vpnn0t3000n`, `vpnpdfpnasnpn`, `vpnpdfsgasypn`, `vpnpdfsnasnpn`, `vpnpdmpnasnpn`, `vpnpdmsnasnpn`, `vpnpdmsnpsnpn`, `vpnppmsn0000n`, `vpnpu0000000n`, `vpyifi130an`, `vpyipi130an`, `vpyisi130an`, `vtnc0i100an`, `vtnc0i100ay`, `vtnc0t200an`, `vtnd0i100an`, `vtnifi11pay`, `vtnifi11san`, `vtnifi130an`, `vtnifi130ay`, `vtnift130an`, `vtnipi11pan`, `vtnipi11san`, `vtnipi130an`, `vtnipi130ay`, `vtnipi230an`, `vtnipii30an`, `vtnipt230an`, `vtnipt330an`, `vtnisi11san`, `vtnisi12san`, `vtnisi130an`, `vtnisi130ay`, `vtnist330an`, `vtnn0i1000n`, `vtnn0i100an`, `vtnn0t1000n`, `vtnpdfpnasnpn`, `vtnpdfsnasnpn`, `vtnpdmpnasnpn`, `vtnpdmsnasnpn`, `vtnppmsn0000n`, `vtnpu0000000n`, `vtnrpi100an`, `vtyc0i300ay`, `vtyifi330an`, `vtyipi11san`, `vtyipi130an`, `vtyipi130ay`, `vtyipi330an`, `vtyipi330ay`, `vtyipt11pay`, `vtyipt11say`, `vtyipt130an`, `vtyipt330an`, `vtyisi11san`, `vtyisi130an`, `vtyisi330an`, `vtyist11pan`, `vtyist11san`, `vtyist130an`, `vtyist330an`, `vtyn0i1000n`, `vtyn0i3000n`, `vtyn0t1000n`, `vtyn0t3000n`, `vtypdfsnasnpn`, `vtypdmsnasnpn`, `xf`, `xn`, `xo`, `xu`, `xx`, `ya`, `yd`, `yn`, `yp`, `yr`, `yv`, `z_`, `zb`, `zc`, `zd`, `zo`, `zq`, `zs`, `zx` | | **`morphologizer`** | `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PUNCT`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART`, `POS=CCONJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADV\|PronType=Dem`, `POS=ADV\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=SCONJ`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Rel`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|POS=PRON\|PronType=Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=CCONJ\|Polarity=Neg`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|POS=PROPN`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Neg`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|POS=PRON\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Cnd\|POS=VERB\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Mood=Cnd\|POS=AUX\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|POS=AUX\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PART\|Polarity=Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|POS=PRON\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Polarity=Neg\|VerbForm=Inf`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `NumType=Ord\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=PROPN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Gender=Masc\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Mood=Nec\|POS=AUX\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Abbr=Yes\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=SYM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Foreign=Yes\|POS=X\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=CCONJ\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `POS=X\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=SYM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Foreign=Yes\|POS=X`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|POS=NOUN`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|POS=DET\|PronType=Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `POS=PART\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Int,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|POS=PRON\|PronType=Ind,Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|POS=DET\|PronType=Ind,Neg`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|POS=PRON\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Int`, `Case=Gen\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=ADV\|PronType=Tot`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|POS=DET\|PronType=Ind`, `Case=Acc\|POS=PRON\|PronType=Ind,Neg`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Conv`, `POS=INTJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PART\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `POS=ADV\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Nec\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `NumType=Mult\|POS=ADV`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Ind`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADP\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|POS=DET\|PronType=Rel`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|Typo=Yes`, `Case=Nom\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Inf`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|POS=DET\|PronType=Rel`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADV`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Coll\|POS=NOUN\|PronType=Int`, `POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Inf`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Mood=Nec\|POS=AUX\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|Typo=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=PUNCT\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|POS=PRON\|PronType=Rel`, `Mood=Imp\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `POS=SCONJ\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|POS=ADV\|Typo=Yes`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|POS=SYM\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=X`, `POS=ADV\|PronType=Neg\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Degree=Cmp\|POS=ADV\|Typo=Yes`, `POS=NOUN\|Typo=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=AUX\|Polarity=Neg\|VerbForm=Inf`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=ADV\|PronType=Int\|Typo=Yes`, `Case=Dat\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|POS=VERB`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `NumType=Ord\|POS=ADJ\|Typo=Yes`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Neg`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` | | **`experimental_edit_tree_lemmatizer`** | `0`, `2`, `4`, `6`, `8`, `10`, `11`, `13`, `15`, `18`, `20`, `22`, `26`, `28`, `31`, `34`, `37`, `39`, `41`, `43`, `45`, `47`, `49`, `52`, `54`, `56`, `58`, `60`, `61`, `64`, `66`, `67`, `69`, `71`, `73`, `74`, `76`, `78`, `80`, `83`, `85`, `86`, `87`, `89`, `91`, `93`, `95`, `97`, `98`, `101`, `104`, `107`, `109`, `110`, `112`, `113`, `116`, `119`, `122`, `124`, `126`, `128`, `131`, `134`, `138`, `140`, `142`, `145`, `147`, `149`, `152`, `153`, `155`, `157`, `160`, `163`, `164`, `166`, `168`, `172`, `175`, `177`, `179`, `181`, `183`, `184`, `187`, `190`, `193`, `195`, `196`, `198`, `200`, `201`, `203`, `205`, `208`, `209`, `210`, `212`, `214`, `218`, `220`, `222`, `224`, `227`, `230`, `233`, `235`, `237`, `239`, `243`, `245`, `246`, `248`, `250`, `251`, `253`, `255`, `256`, `259`, `260`, `262`, `265`, `269`, `272`, `274`, `275`, `276`, `278`, `281`, `283`, `287`, `291`, `293`, `295`, `298`, `300`, `303`, `305`, `306`, `308`, `311`, `313`, `314`, `316`, `319`, `322`, `324`, `326`, `328`, `329`, `332`, `333`, `335`, `337`, `339`, `341`, `343`, `345`, `348`, `349`, `350`, `352`, `354`, `355`, `358`, `359`, `361`, `362`, `365`, `368`, `370`, `372`, `374`, `376`, `377`, `379`, `381`, `382`, `384`, `387`, `389`, `390`, `392`, `396`, `398`, `400`, `401`, `405`, `408`, `409`, `410`, `412`, `415`, `417`, `419`, `420`, `422`, `425`, `426`, `428`, `430`, `432`, `434`, `436`, `438`, `439`, `440`, `443`, `445`, `447`, `448`, `450`, `452`, `454`, `455`, `458`, `461`, `462`, `464`, `466`, `468`, `469`, `471`, `473`, `474`, `476`, `477`, `480`, `483`, `484`, `485`, `487`, `488`, `491`, `494`, `495`, `497`, `498`, `499`, `500`, `501`, `502`, `504`, `506`, `508`, `510`, `511`, `512`, `513`, `515`, `517`, `518`, `519`, `520`, `524`, `525`, `527`, `529`, `532`, `535`, `536`, `537`, `539`, `540`, `541`, `543`, `546`, `548`, `549`, `550`, `551`, `553`, `555`, `556`, `560`, `562`, `564`, `566`, `567`, `569`, `571`, `572`, `575`, `577`, `579`, `582`, `584`, `585`, `586`, `587`, `588`, `593`, `595`, `596`, `599`, `601`, `603`, `605`, `607`, `610`, `613`, `616`, `619`, `622`, `624`, `625`, `627`, `629`, `631`, `633`, `636`, `638`, `640`, `642`, `644`, `645`, `646`, `650`, `651`, `653`, `655`, `657`, `660`, `662`, `665`, `667`, `668`, `670`, `673`, `676`, `678`, `680`, `681`, `682`, `684`, `687`, `688`, `690`, `691`, `692`, `693`, `694`, `697`, `699`, `700`, `701`, `702`, `705`, `706`, `708`, `709`, `712`, `714`, `715`, `718`, `721`, `723`, `725`, `726`, `728`, `729`, `731`, `732`, `733`, `735`, `736`, `737`, `738`, `739`, `741`, `743`, `745`, `746`, `748`, `749`, `750`, `751`, `753`, `754`, `755`, `756`, `758`, `759`, `760`, `761`, `762`, `764`, `766`, `767`, `768`, `771`, `773`, `774`, `775`, `776`, `777`, `778`, `779`, `781`, `783`, `785`, `786`, `787`, `790`, `791`, `792`, `793`, `795`, `796`, `798`, `799`, `800`, `801`, `804`, `805`, `806`, `807`, `808`, `809`, `810`, `811`, `812`, `813`, `814`, `816`, `823`, `825`, `826`, `828`, `830`, `831`, `832`, `835`, `838`, `839`, `840`, `842`, `843`, `844`, `846`, `847`, `849`, `851`, `853`, `855`, `856`, `858`, `859`, `861`, `862`, `864`, `865`, `866`, `869`, `870`, `871`, `873`, `876`, `878`, `879`, `880`, `881`, `882`, `883`, `886`, `888`, `889`, `891`, `894`, `897`, `898`, `899`, `900`, `901`, `904`, `907`, `908`, `909`, `912`, `914`, `916`, `917`, `919`, `921`, `922`, `923`, `925`, `928`, `930`, `932`, `933`, `936`, `937`, `938`, `940`, `942`, `943`, `944`, `946`, `947`, `949`, `951`, `953`, `955`, `956`, `957`, `961`, `963`, `966`, `967`, `968`, `969`, `972`, `974`, `976`, `977`, `979`, `981`, `982`, `983`, `986`, `988`, `989`, `990`, `991`, `995`, `998`, `999`, `1002`, `1004`, `1007`, `1008`, `1009`, `1012`, `1015`, `1016`, `1018`, `1019`, `1021`, `1024`, `1027`, `1028`, `1031`, `1034`, `1035`, `1037`, `1039`, `1041`, `1043`, `1044`, `1046`, `1049`, `1051`, `1053`, `1054`, `1056`, `1058`, `1060`, `1061`, `1062`, `1064`, `1065`, `1067`, `1069`, `1070`, `1071`, `1073`, `1074`, `1075`, `1076`, `1078`, `1079`, `1082`, `1083`, `1085`, `1088`, `1089`, `1092`, `1095`, `1097`, `1099`, `1100`, `1102`, `1104`, `1105`, `1108`, `1110`, `1114`, `1116`, `1117`, `1119`, `1121`, `1123`, `1127`, `1128`, `1129`, `1130`, `1131`, `1133`, `1135`, `1137`, `1139`, `1140`, `1142`, `1143`, `1145`, `1147`, `1149`, `1150`, `1153`, `1158`, `1160`, `1162`, `1167`, `1168`, `1169`, `1171`, `1172`, `1174`, `1176`, `1178`, `1180`, `1181`, `1182`, `1183`, `1185`, `1188`, `1191`, `1193`, `1195`, `1196`, `1197`, `1200`, `1201`, `1204`, `1205`, `1206`, `1208`, `1209`, `1211`, `1213`, `1216`, `1218`, `1220`, `1221`, `1222`, `1223`, `1225`, `1226`, `1227`, `1229`, `1230`, `1232`, `1233`, `1235`, `1236`, `1237`, `1238`, `1240`, `1241`, `1242`, `1243`, `1245`, `1247`, `1248`, `1250`, `1251`, `1252`, `1253`, `1255`, `1256`, `1257`, `1258`, `1259`, `1260`, `1261`, `1262`, `1263`, `1264`, `1267`, `1269`, `1270`, `1272`, `1274`, `523`, `1276`, `1279`, `1280`, `1281`, `1282`, `1284`, `1285`, `1287`, `1289`, `1292`, `1293`, `1294`, `1297`, `1298`, `1300`, `1301`, `1305`, `1307`, `1309`, `1310`, `1313`, `1314`, `1317`, `1318`, `1319`, `1321`, `1323`, `1324`, `1325`, `1326`, `1327`, `1329`, `1330`, `1333`, `1335`, `1337`, `1338`, `1340`, `1342`, `1344`, `1346`, `1347`, `1350`, `1351`, `1353`, `1356`, `1357`, `1358`, `1360`, `1362`, `1364`, `1367`, `1368`, `1369`, `1370`, `1371`, `1373`, `1375`, `1377`, `1378`, `1381`, `1383`, `1384`, `1386`, `1388`, `1390`, `1391`, `1392`, `1393`, `1395`, `1396`, `1398`, `1399`, `1401`, `1402`, `1403`, `1405`, `1406`, `1407`, `1408`, `1410`, `1411`, `1412`, `1413`, `1416`, `1418`, `1419`, `1422`, `1423`, `1425`, `1427`, `1428`, `1431`, `1432`, `1433`, `1434`, `1437`, `1438`, `1439`, `1441`, `1442`, `1443`, `1444`, `1445`, `1446`, `1448`, `1450`, `1452`, `1454`, `1455`, `1456`, `1457`, `1458`, `1460`, `1462`, `1466`, `1467`, `1469`, `1470`, `1474`, `1476`, `1477`, `1479`, `1481`, `1482`, `1483`, `1484`, `1485`, `1487`, `1489`, `1492`, `1493`, `1495`, `1496`, `1498`, `1499`, `1501`, `1502`, `1503`, `1506`, `1507`, `1508`, `1509`, `1511`, `1513`, `1514`, `1517`, `1518`, `1520`, `1523`, `1525`, `1527`, `1528`, `1530`, `1532`, `1534`, `1535`, `1536`, `1537`, `1539`, `1540`, `1542`, `1543`, `1545`, `1546`, `1547`, `1549`, `1551`, `1552`, `1553`, `1554`, `1557`, `1558`, `1560`, `1562`, `1564`, `1567`, `1569`, `1571`, `1572`, `1573`, `1574`, `1576`, `1577`, `1579`, `1581`, `1583`, `1584`, `1531`, `1585`, `1587`, `1588`, `1589`, `1591`, `1592`, `1595`, `1596`, `1598`, `1600`, `1601`, `1604`, `1605`, `1607`, `1608`, `1610`, `1612`, `1613`, `1616`, `1618`, `1619`, `1621`, `1623`, `1625`, `1626`, `1629`, `1630`, `1631`, `1633`, `1637`, `1639`, `1640`, `1642`, `1643`, `1645`, `1647`, `1648`, `1651`, `1652`, `1654`, `1655`, `1656`, `1657`, `1659`, `1661`, `1664`, `1665`, `1668`, `1670`, `1672`, `1673`, `1674`, `1675`, `1678`, `1679`, `1681`, `1682`, `1685`, `1688`, `1690`, `1692`, `1694`, `1695`, `1697`, `1699`, `1701`, `1705`, `1708`, `1709`, `1710`, `1711`, `1714`, `1715`, `1718`, `1721`, `1723`, `1725`, `1727`, `1729`, `1731`, `1734`, `1736`, `1739`, `1741`, `1743`, `1745`, `1746`, `1748`, `1749`, `1752`, `1754`, `1756`, `1757`, `1758`, `1759`, `1760`, `1761`, `1766`, `1768`, `1769`, `1770`, `1771`, `1773`, `1775`, `1776`, `1777`, `1779`, `1781`, `1784`, `1785`, `1786`, `1788`, `1789`, `1790`, `1792`, `1794`, `1796`, `1798`, `1800`, `1802`, `1805`, `1807`, `1809`, `1810`, `1811`, `1813`, `1815`, `1816`, `1817`, `1818`, `1821`, `1823`, `1824`, `1825`, `1826`, `1828`, `1830`, `1832`, `1833`, `1834`, `1835`, `1837`, `1840`, `1842`, `1846`, `1848`, `1852`, `1853`, `1854`, `1856`, `1857`, `1858`, `1859`, `1860`, `1862`, `1863`, `1866`, `1868`, `1869`, `1871`, `1873`, `1304`, `1874`, `1875`, `1876`, `1878`, `1879`, `1880`, `1881`, `1883`, `1885`, `1886`, `1887`, `1890`, `1892`, `1893`, `1894`, `1897`, `1898`, `1900`, `1488`, `1903`, `1904`, `1905`, `1906`, `1907`, `1908`, `1910`, `1912`, `1913`, `1914`, `1915`, `1916`, `1918`, `1919`, `1920`, `1922`, `1925`, `1927`, `1929`, `1931`, `1933`, `1934`, `1936`, `1938`, `1939`, `1940`, `1943`, `1944`, `1945`, `1946`, `1947`, `1948`, `1950`, `1951`, `1953`, `1955`, `1956`, `1957`, `1960`, `1962`, `1963`, `1964`, `1965`, `1966`, `1969`, `1971`, `1973`, `1975`, `1976`, `1979`, `1980`, `1981`, `1982`, `1985`, `1986`, `1987`, `1988`, `1989`, `1991`, `1992`, `1993`, `1994`, `1995`, `1996`, `1999`, `2002`, `2003`, `2004`, `2006`, `2007`, `2008`, `2010`, `2011`, `2013`, `2015`, `2016`, `2017`, `2018`, `2020`, `2021`, `2023`, `2024`, `2028`, `2030`, `2031`, `2032`, `2033`, `2034`, `2037`, `2038`, `2040`, 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cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.80 | | `TOKEN_P` | 99.79 | | `TOKEN_R` | 99.81 | | `TOKEN_ACC` | 99.97 | | `SENTS_F` | 97.77 | | `SENTS_P` | 98.24 | | `SENTS_R` | 97.30 | | `TAG_ACC` | 91.59 | | `POS_ACC` | 97.94 | | `MORPH_ACC` | 95.69 | | `DEP_UAS` | 91.30 | | `DEP_LAS` | 87.75 | | `LEMMA_ACC` | 95.39 | | c8a76c134e96be5c3723eee25f9e38c8 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_general-massive_social-roberta-large-v1-5-7 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. | 95b77acf3e4023b7f6fd89e0dba1acbc |
openrail | [] | false | <img src = 'https://images.unsplash.com/photo-1628432136678-43ff9be34064?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=663&q=80'> <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> | ca1f1b078c887279e0a0d83438e57e31 |
openrail | [] | false | Usage DialoGPT **large** version, finetuned on Tony Montana sequences (ScarFace main character). Simple snippet of how to infer of this model: ```python from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('s3nh/DialoGPT-tony-montana') model = AutoModelWithLMHead.from_pretrained('s3nh/DialoGPT-tony-montana') for step in range(4): new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) print("MontanaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) | d91b0bd412f8f23b1749f3e4e9a7b8fa |
creativeml-openrail-m | ['text-to-image'] | false | stream_girl Dreambooth model trained by chebao with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: chebao (use that on your prompt)  | 337fb6ce631a39cea2a0fc64027723ac |
apache-2.0 | ['Early Modern French', 'Historical', 'POS', 'flair'] | false | CamemBERT Early Modern French POS model This model is fine-tuned version of a [CamemBERT model](https://huggingface.co/camembert-base) on the [FreEMLPM corpus](https://doi.org/10.5281/zenodo.6481300) for Early Modern French. It was introduced in [this paper](https://aclanthology.org/2022.lrec-1.359/). | 0ef6212dfca113bd25c7384c70f09509 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-Turkish-Tr-small-CommonVoice8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4813 - Wer: 0.7207 | 2d665e51cf2f9309b8137ea8683dcb43 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP | 1274391530bbc33b6950680b7a40ba9a |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2 | 0.53 | 400 | 3.1949 | 0.9964 | | 2.9387 | 1.07 | 800 | 2.5015 | 1.0337 | | 1.5975 | 1.6 | 1200 | 1.0928 | 0.9945 | | 1.0688 | 2.13 | 1600 | 0.8388 | 0.9390 | | 0.8977 | 2.66 | 2000 | 0.7106 | 0.8889 | | 0.789 | 3.2 | 2400 | 0.6051 | 0.8273 | | 0.7116 | 3.73 | 2800 | 0.5580 | 0.7855 | | 0.6576 | 4.26 | 3200 | 0.5033 | 0.7433 | | 0.6002 | 4.79 | 3600 | 0.4813 | 0.7207 | | 52c12efd4bcbe26c592330afb60928ae |
apache-2.0 | ['translation'] | false | opus-mt-loz-de * source languages: loz * target languages: de * OPUS readme: [loz-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/loz-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/loz-de/opus-2020-01-21.zip) * test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/loz-de/opus-2020-01-21.test.txt) * test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/loz-de/opus-2020-01-21.eval.txt) | 4edd4dc93982125a68b12b0000aeec68 |
apache-2.0 | ['translation'] | false | opus-mt-fi-lv * source languages: fi * target languages: lv * OPUS readme: [fi-lv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-lv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-lv/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-lv/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-lv/opus-2020-01-08.eval.txt) | 29359beec5230b918c28bb3b9dce8a6e |
apache-2.0 | [] | false | [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**. The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Trivia QA (TQA)](https://huggingface.co/datasets/trivia_qa). **Note**: The model was fine-tuned on 100% of the train splits of [Trivia QA (TQA)](https://huggingface.co/datasets/trivia_qa) for 10 steps. Other community Checkpoints: [here](https://huggingface.co/models?search=ssm) Paper: [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf) Authors: *Adam Roberts, Colin Raffel, Noam Shazeer* | bdd45ceb77160b9022c53cfed9e9b215 |
apache-2.0 | [] | false | Results on Trivia QA - Test Set |Id | link | Exact Match | |---|---|---| |T5-11b|https://huggingface.co/google/t5-large-ssm-tqa|60.5| |**T5-xxl**|**https://huggingface.co/google/t5-xxl-ssm-tqa**|**61.6**| | 084120be4f7589e141a969e7d5c399cf |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-xxl-ssm-tqa") t5_tok = AutoTokenizer.from_pretrained("google/t5-xxl-ssm-tqa") input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids gen_output = t5_qa_model.generate(input_ids)[0] print(t5_tok.decode(gen_output, skip_special_tokens=True)) ``` | d77b8c3671265fd88b90f259895e8127 |
cc-by-4.0 | ['generated_from_keras_callback'] | false | amitjohn007/roberta-base-finetuned-squad This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4173 - Epoch: 2 | 90d331d5e1db4c8ec8e883a4e404d2e4 |
cc-by-4.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16608, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 2ce0facbe12604730709500bfac1328b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 625f5bd3c9ae76c8d40eddf0dc26ce6b |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-tr-en-finetuned-tr-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tr-en](https://huggingface.co/Helsinki-NLP/opus-mt-tr-en) on the opus_infopankki dataset. It achieves the following results on the evaluation set: - Loss: 0.6924 - Bleu: 54.7617 - Gen Len: 13.5501 | e2a0a9b22d71206a287a1713fa115baa |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP | f7eda2f7b402ff107882e2a59ace4a9b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 412 | 1.1776 | 43.3104 | 12.9297 | | 1.4032 | 2.0 | 824 | 1.0750 | 45.7912 | 12.9155 | | 1.2268 | 3.0 | 1236 | 1.0019 | 47.6255 | 12.9251 | | 1.141 | 4.0 | 1648 | 0.9411 | 49.0649 | 12.9302 | | 1.0651 | 5.0 | 2060 | 0.8929 | 50.4894 | 12.9066 | | 1.0651 | 6.0 | 2472 | 0.8519 | 51.5072 | 12.9067 | | 1.0025 | 7.0 | 2884 | 0.8180 | 52.5035 | 12.8875 | | 0.9582 | 8.0 | 3296 | 0.7893 | 51.7587 | 13.5338 | | 0.9173 | 9.0 | 3708 | 0.7655 | 52.3566 | 13.5376 | | 0.8892 | 10.0 | 4120 | 0.7449 | 53.0488 | 13.5545 | | 0.8639 | 11.0 | 4532 | 0.7285 | 53.5965 | 13.5539 | | 0.8639 | 12.0 | 4944 | 0.7152 | 53.9433 | 13.5547 | | 0.8424 | 13.0 | 5356 | 0.7053 | 54.2509 | 13.5502 | | 0.8317 | 14.0 | 5768 | 0.6981 | 54.5339 | 13.5502 | | 0.817 | 15.0 | 6180 | 0.6938 | 54.7068 | 13.5448 | | 0.8155 | 16.0 | 6592 | 0.6924 | 54.7617 | 13.5501 | | 4cdc4a71a4b4b83d7c9b4ed0f56752ac |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-sigir-support-no-label-40 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1107 | 74df401e0db80b0a7830abd192a5a21f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 30 - eval_batch_size: 30 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40.0 - mixed_precision_training: Native AMP | a0c0d24dddfdd7fcfb868e5cd2b79cf4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7638 | 1.0 | 246 | 2.2805 | | 2.1924 | 2.0 | 492 | 1.9602 | | 1.8921 | 3.0 | 738 | 1.7992 | | 1.7412 | 4.0 | 984 | 1.7229 | | 1.6311 | 5.0 | 1230 | 1.6165 | | 1.5421 | 6.0 | 1476 | 1.5400 | | 1.4619 | 7.0 | 1722 | 1.5001 | | 1.3846 | 8.0 | 1968 | 1.4381 | | 1.3414 | 9.0 | 2214 | 1.4285 | | 1.2894 | 10.0 | 2460 | 1.4108 | | 1.2467 | 11.0 | 2706 | 1.3460 | | 1.1992 | 12.0 | 2952 | 1.3434 | | 1.1612 | 13.0 | 3198 | 1.2951 | | 1.1266 | 14.0 | 3444 | 1.2518 | | 1.0933 | 15.0 | 3690 | 1.2825 | | 1.0625 | 16.0 | 3936 | 1.2523 | | 1.0386 | 17.0 | 4182 | 1.2251 | | 1.0066 | 18.0 | 4428 | 1.2339 | | 0.9755 | 19.0 | 4674 | 1.1887 | | 0.9656 | 20.0 | 4920 | 1.2288 | | 0.9517 | 21.0 | 5166 | 1.1391 | | 0.9207 | 22.0 | 5412 | 1.1718 | | 0.8964 | 23.0 | 5658 | 1.1850 | | 0.8891 | 24.0 | 5904 | 1.1306 | | 0.8564 | 25.0 | 6150 | 1.1956 | | 0.851 | 26.0 | 6396 | 1.1263 | | 0.8331 | 27.0 | 6642 | 1.1060 | | 0.8143 | 28.0 | 6888 | 1.0689 | | 0.7972 | 29.0 | 7134 | 1.0772 | | 0.7857 | 30.0 | 7380 | 1.1103 | | 0.7687 | 31.0 | 7626 | 1.1635 | | 0.7653 | 32.0 | 7872 | 1.0736 | | 0.777 | 33.0 | 8118 | 1.1103 | | 0.741 | 34.0 | 8364 | 1.0830 | | 0.7408 | 35.0 | 8610 | 1.0809 | | 0.736 | 36.0 | 8856 | 1.0894 | | 0.7362 | 37.0 | 9102 | 1.0691 | | 0.727 | 38.0 | 9348 | 1.0519 | | 0.715 | 39.0 | 9594 | 1.0919 | | 0.7286 | 40.0 | 9840 | 1.1107 | | 2cdcf98b613735a2d6d2bd427bc3b182 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8229 - Accuracy: 0.54 | cc7bf7b111e5a5cc19d80ff30b025e73 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.7709 | 0.74 | | No log | 2.0 | 14 | 0.7048 | 0.72 | | No log | 3.0 | 21 | 0.8728 | 0.46 | | No log | 4.0 | 28 | 0.7849 | 0.64 | | No log | 5.0 | 35 | 0.8229 | 0.54 | | 4cb5cd8e4087f96a8deda59dad393b06 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2467 - F1: 0.8206 | e03cd592be32c5dbcaacb7c4b9c7c6c0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7897 | 1.0 | 70 | 0.3096 | 0.7519 | | 0.2819 | 2.0 | 140 | 0.2603 | 0.8093 | | 0.1818 | 3.0 | 210 | 0.2467 | 0.8206 | | 889258dd47a6e24a173d1176f398deeb |
mit | ['generated_from_trainer'] | false | deberta-v3-large__sst2__train-8-5 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3078 - Accuracy: 0.6930 | 8b414df4795c55724d46eb56a21fcf51 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6813 | 1.0 | 3 | 0.7842 | 0.25 | | 0.6617 | 2.0 | 6 | 0.7968 | 0.25 | | 0.6945 | 3.0 | 9 | 0.7746 | 0.25 | | 0.5967 | 4.0 | 12 | 0.7557 | 0.25 | | 0.4824 | 5.0 | 15 | 0.6920 | 0.25 | | 0.3037 | 6.0 | 18 | 0.6958 | 0.5 | | 0.2329 | 7.0 | 21 | 0.6736 | 0.5 | | 0.1441 | 8.0 | 24 | 0.3749 | 1.0 | | 0.0875 | 9.0 | 27 | 0.3263 | 0.75 | | 0.0655 | 10.0 | 30 | 0.3525 | 0.75 | | 0.0373 | 11.0 | 33 | 0.1993 | 1.0 | | 0.0173 | 12.0 | 36 | 0.1396 | 1.0 | | 0.0147 | 13.0 | 39 | 0.0655 | 1.0 | | 0.0084 | 14.0 | 42 | 0.0343 | 1.0 | | 0.0049 | 15.0 | 45 | 0.0225 | 1.0 | | 0.004 | 16.0 | 48 | 0.0167 | 1.0 | | 0.003 | 17.0 | 51 | 0.0134 | 1.0 | | 0.0027 | 18.0 | 54 | 0.0114 | 1.0 | | 0.002 | 19.0 | 57 | 0.0104 | 1.0 | | 0.0015 | 20.0 | 60 | 0.0099 | 1.0 | | 0.0014 | 21.0 | 63 | 0.0095 | 1.0 | | 0.0013 | 22.0 | 66 | 0.0095 | 1.0 | | 0.0012 | 23.0 | 69 | 0.0091 | 1.0 | | 0.0011 | 24.0 | 72 | 0.0085 | 1.0 | | 0.0009 | 25.0 | 75 | 0.0081 | 1.0 | | 0.001 | 26.0 | 78 | 0.0077 | 1.0 | | 0.0008 | 27.0 | 81 | 0.0074 | 1.0 | | 0.0009 | 28.0 | 84 | 0.0071 | 1.0 | | 0.0007 | 29.0 | 87 | 0.0068 | 1.0 | | 0.0008 | 30.0 | 90 | 0.0064 | 1.0 | | 0.0007 | 31.0 | 93 | 0.0062 | 1.0 | | 0.0007 | 32.0 | 96 | 0.0059 | 1.0 | | 0.0007 | 33.0 | 99 | 0.0056 | 1.0 | | 0.0005 | 34.0 | 102 | 0.0054 | 1.0 | | 0.0006 | 35.0 | 105 | 0.0053 | 1.0 | | 0.0008 | 36.0 | 108 | 0.0051 | 1.0 | | 0.0007 | 37.0 | 111 | 0.0050 | 1.0 | | 0.0007 | 38.0 | 114 | 0.0049 | 1.0 | | 0.0006 | 39.0 | 117 | 0.0048 | 1.0 | | 0.0005 | 40.0 | 120 | 0.0048 | 1.0 | | 0.0005 | 41.0 | 123 | 0.0048 | 1.0 | | 0.0005 | 42.0 | 126 | 0.0047 | 1.0 | | 0.0005 | 43.0 | 129 | 0.0047 | 1.0 | | 0.0005 | 44.0 | 132 | 0.0047 | 1.0 | | 0.0006 | 45.0 | 135 | 0.0047 | 1.0 | | 0.0005 | 46.0 | 138 | 0.0047 | 1.0 | | 0.0005 | 47.0 | 141 | 0.0047 | 1.0 | | 0.0006 | 48.0 | 144 | 0.0047 | 1.0 | | 0.0005 | 49.0 | 147 | 0.0047 | 1.0 | | 0.0005 | 50.0 | 150 | 0.0047 | 1.0 | | 9f5fdebb430fd76a74e0bd23f1a4267c |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3124 - Accuracy: 0.87 - F1: 0.8704 | c28b361467319b49f0cb0a0d814f09ed |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | bert-base-finetuned-sts This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3951 - Pearsonr: 0.9116 | 0383862ca7a0e26b6840804528ea447e |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearsonr | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2345 | 1.0 | 2917 | 0.7037 | 0.8757 | | 0.1491 | 2.0 | 5834 | 0.4869 | 0.8846 | | 0.097 | 3.0 | 8751 | 0.4023 | 0.9041 | | 0.0735 | 4.0 | 11668 | 0.3960 | 0.9073 | | 0.0644 | 5.0 | 14585 | 0.4838 | 0.8989 | | 0.0446 | 6.0 | 17502 | 0.3990 | 0.9078 | | 0.0355 | 7.0 | 20419 | 0.3951 | 0.9116 | | 0.0277 | 8.0 | 23336 | 0.4284 | 0.9053 | | 0.0239 | 9.0 | 26253 | 0.4166 | 0.9073 | | 0.0205 | 10.0 | 29170 | 0.4234 | 0.9062 | | b50aa44c430180ece63b577e7f810206 |
mit | ['Cometrain AutoCode', 'Cometrain AlphaML'] | false | stocks-news-t5 This model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned [T5](https://huggingface.co/t5-base) allows to analyze financial market news. Automatically trained on [Financial Sentiment Analysis(2022)](https://www.kaggle.com/datasets/sbhatti/financial-sentiment-analysis) dataset. | 537f91b832803a81865d5db2f6378b1c |
mit | ['generated_from_trainer'] | false | cranky_northcutt This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | 730321f800449d959abd7b396cbd50d1 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'every_n_steps': 16, 'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 4096}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'every_n_steps': 16, 'force_call_on': [25354], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 1024, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'cranky_northcutt', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 1673, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 0500afd87b8628e7fd95ce669591f303 |
mit | ['generated_from_keras_callback'] | false | madatnlp/gamza-bart-for-kormath This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1418 - Validation Loss: 0.3009 - Epoch: 29 | e612f20931fbd0c46d5fe44a868e8386 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.4155 | 1.9300 | 0 | | 1.4995 | 1.0293 | 1 | | 1.0445 | 0.8365 | 2 | | 0.8775 | 0.7569 | 3 | | 0.8198 | 0.7778 | 4 | | 0.7619 | 0.7430 | 5 | | 0.7324 | 0.7259 | 6 | | 0.7234 | 0.7214 | 7 | | 0.6697 | 0.6819 | 8 | | 0.6599 | 0.6673 | 9 | | 0.6387 | 0.6433 | 10 | | 0.6227 | 0.6651 | 11 | | 0.6017 | 0.6128 | 12 | | 0.5820 | 0.6430 | 13 | | 0.5229 | 0.5611 | 14 | | 0.4617 | 0.4675 | 15 | | 0.4071 | 0.4463 | 16 | | 0.3495 | 0.4213 | 17 | | 0.3202 | 0.4103 | 18 | | 0.2875 | 0.4477 | 19 | | 0.2528 | 0.3244 | 20 | | 0.2331 | 0.4037 | 21 | | 0.2117 | 0.3041 | 22 | | 0.1943 | 0.3069 | 23 | | 0.1805 | 0.3385 | 24 | | 0.2267 | 0.3347 | 25 | | 0.2049 | 0.2993 | 26 | | 0.1800 | 0.3792 | 27 | | 0.1583 | 0.2905 | 28 | | 0.1418 | 0.3009 | 29 | | 0e1485182f062b182f7eb1eedf8a832f |
mit | ['generated_from_keras_callback'] | false | deepiit98/Wayback_Machine-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3876 - Train End Logits Accuracy: 0.9271 - Train Start Logits Accuracy: 0.8785 - Validation Loss: 0.2313 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | 5edec4b6cf08f5b6c3453e0907dfc6fd |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.3876 | 0.9271 | 0.8785 | 0.2313 | 0.6667 | 1.0 | 0 | | ac861d073d816805f6cbf288e815d2ea |
mit | [] | false | Ilo Kunst on Stable Diffusion This is the `<ilo-kunst>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:      | f2ddb542544ea937854e83cd0c8f23c4 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | GorynichMix Welcome to GorynichMix - a latent diffusion models mix for realistic/anime styles. The user has complete control over whether or not to generate NSFW content, and the user's decision to enjoy either SFW or NSFW is entirely up to the user. | 27c20fa95abd1f0bead4a8aba082cd5d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Recipe **I used old Checkpoint Merger in AUTOMATIC1111 webui (commit hash: b6e5edd74657e3fd1fbd04f341b7a84625d4aa7a) and Merge Block Weighted plugin.** - Step1: AnythingV4.5-FP32 + Elysium Anime V3 -> (Weighted Sum 0.5) = Tmp1 - Step2: Tmp1 + F222 + SD1.5-pruned-emaonly -> (Add Difference 1.0) = Tmp2 - Step3: (Merge Block Weighted) Derrida_final + Easter-E9 -> (In All 0.3 Out All 0.7 M00 0.50 Base Alpha 0.50) = Tmp3 - Step4: Tmp3 + Dreamlike Photoreal V2 -> (Weighted Sum 0.7) = Tmp4 - Step5: Tmp2 + Tmp4 -> (Weighted Sum 0.3) = GorynichMix | ac5101de5fee1bdcf1c0e271147453cf |
cc-by-sa-4.0 | ['spacy', 'floret', 'fasttext', 'feature-extraction', 'token-classification'] | false | Hungarian word vectors for HuSpaCy. The model is trained on the Hungarian Webcorpus 2.0 using floret with the following hyperparameters: `floret cbow -dim 100 -mode floret -bucket 200000 -minn 4 -maxn 6 -minCount 100 -neg 10 -hashCount 2 -lr 0.1 -thread 30 -epoch 5` Vectors are published in fasttext and floret format. | Feature | Description | | --- | --- | | **Name** | `hu_vectors_web_lg` | | **Version** | `1.0` | | **Vectors** | 200000 keys (300 dimensions) | | **Sources** | [Hungarian Webcorpus 2.0](https://hlt.bme.hu/en/resources/webcorpus2) (Dávid Márk Nemeskey (SZTAKI-HLT)) | | **License** | `cc-by-sa-4.0` | | **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) | | 6c680eb5b54a534b3a201505ef5ad731 |
apache-2.0 | ['generated_from_trainer'] | false | bart-mlm-pubmed This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7223 - Rouge2 Precision: 0.6572 - Rouge2 Recall: 0.5164 - Rouge2 Fmeasure: 0.5662 | ea49725679f792fcf172d8f5f7e11fa3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.0322 | 1.0 | 663 | 0.7891 | 0.639 | 0.4989 | 0.5491 | | 0.8545 | 2.0 | 1326 | 0.7433 | 0.6461 | 0.5057 | 0.5556 | | 0.758 | 3.0 | 1989 | 0.7299 | 0.647 | 0.5033 | 0.5547 | | 0.6431 | 4.0 | 2652 | 0.7185 | 0.6556 | 0.5101 | 0.5616 | | 0.6058 | 5.0 | 3315 | 0.7126 | 0.6537 | 0.5144 | 0.5638 | | 0.5726 | 6.0 | 3978 | 0.7117 | 0.6567 | 0.5169 | 0.5666 | | 0.5168 | 7.0 | 4641 | 0.7150 | 0.6585 | 0.5154 | 0.566 | | 0.5011 | 8.0 | 5304 | 0.7220 | 0.6568 | 0.5164 | 0.5664 | | 0.4803 | 9.0 | 5967 | 0.7208 | 0.6573 | 0.5161 | 0.5662 | | 0.4577 | 10.0 | 6630 | 0.7223 | 0.6572 | 0.5164 | 0.5662 | | 81ccf4241b32f7b43726e219be627388 |
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