Datasets:
davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
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Community
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apache-2.0
['generated_from_trainer']
false
small-mlm-glue-rte-target-glue-rte This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2116 - Accuracy: 0.6029
1a6af675060830e66776776fe78d0cb2
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4044 | 6.41 | 500 | 1.3568 | 0.6354 | | 0.0595 | 12.82 | 1000 | 2.2538 | 0.6209 | | 0.0294 | 19.23 | 1500 | 2.6675 | 0.6209 | | 0.0158 | 25.64 | 2000 | 3.2116 | 0.6029 |
2a365b68d90b48a3501d88cd19a57bda
apache-2.0
['tabular-classification', 'baseline-trainer']
false
Baseline Model trained on UCI_Credit_Cardyi6q1ptm to apply classification on PAY_0 **Metrics of the best model:** accuracy 0.715467 recall_macro 0.777916 precision_macro 0.578960 f1_macro 0.596625 Name: DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01), dtype: float64 **See model plot below:** <style>
302575a3ef0cbc043ff5cc3b1f226e50
apache-2.0
['tabular-classification', 'baseline-trainer']
false
sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}
9769b148645a4a38afa546822c41bfbe
apache-2.0
['tabular-classification', 'baseline-trainer']
false
x27;,EasyPreprocessor(types= continuous dirty_float ... free_string useless LIMIT_BAL False False ... False False SEX False False ... False False EDUCATION False False ... False False MARRIAGE False False ... False False AGE False False ... False False PAY_2 False False ... False False PAY_3 False False ... False False PAY_4 False False ... False False PAY_5 False False ...... PAY_AMT1 True False ... False False PAY_AMT2 True False ... False False PAY_AMT3 True False ... False False PAY_AMT4 True False ... False False PAY_AMT5 True False ... False False PAY_AMT6 True False ... False False default.payment.next.month False False ... False False[23 rows x 7 columns])),(&
5df4bb198b11b91adf74580a1e402410
apache-2.0
['tabular-classification', 'baseline-trainer']
false
x27;,min_impurity_decrease=0.01))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-15" type="checkbox" ><label for="sk-estimator-id-15" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&
53e0b25809428e18bdfd602d180360df
apache-2.0
['tabular-classification', 'baseline-trainer']
false
x27;,min_impurity_decrease=0.01))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float ... free_string useless LIMIT_BAL False False ... False False SEX False False ... False False EDUCATION False False ... False False MARRIAGE False False ... False False AGE False False ... False False PAY_2 False False ... False False PAY_3 False False ... False False PAY_4 False False ... False False PAY_5 False False ... False False PAY_6 False False ... False Fal... BILL_AMT3 True False ... False False BILL_AMT4 True False ... False False BILL_AMT5 True False ... False False BILL_AMT6 True False ... False False PAY_AMT1 True False ... False False PAY_AMT2 True False ... False False PAY_AMT3 True False ... False False PAY_AMT4 True False ... False False PAY_AMT5 True False ... False False PAY_AMT6 True False ... False False default.payment.next.month False False ... False False[23 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-17" type="checkbox" ><label for="sk-estimator-id-17" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(class_weight=&
bc6ceb7c1511215b323c231a3ec031fc
apache-2.0
['tabular-classification', 'baseline-trainer']
false
x27;, min_impurity_decrease=0.01)</pre></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt
9d5846a4ba427501c34393bcde2e9173
mit
['translation', 'pytorch']
false
OpenNMT-py-English-German-Transformer [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework. OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for German to English translation. - Configuration: 2-layer BiLSTM with hidden size 500 trained for 20 epochs - Data: IWSLT ‘14 DE-EN - BLEU: 30.33
eeddd7e4afc4b01b739cf9ecd2447dfd
apache-2.0
['generated_from_trainer']
false
dz_finetuning-medium-distillbert-95K 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: 0.0047 - Accuracy: 0.9991 - F1: 0.9991
706e5c91c0b498668528dfda54cff9ee
cc-by-4.0
[]
false
A HindBERT (l3cube-pune/hindi-bert-v2) model finetuned on least hateful Hindi Tweets.<br> More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2210.04267'> link </a>)<br> ``` @article{gokhale2022spread, title={Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection}, author={Gokhale, Omkar and Kane, Aditya and Patankar, Shantanu and Chavan, Tanmay and Joshi, Raviraj}, journal={arXiv preprint arXiv:2210.04267}, year={2022} } ```
b8a4251d3481770692414fbc4769d5f8
creativeml-openrail-m
['text-to-image', 'stable-diffusion']
false
dyc0002 Dreambooth model trained by anmol-chawla with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can 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) Sample pictures of this concept:
651f5045dd8eb1337726a1c0454ded04
apache-2.0
['summarization']
false
Metrics for DistilBART models | Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | |:---------------------------|------------:|----------------------:|----------:|----------:|----------:| | distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | | distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | | distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | | distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | | bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | | distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | | bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | | distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | | distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | | distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 |
d2553dbcee624356c82dd099829ab624
mit
['generated_from_trainer']
false
bart-large-cnn-small-billsum-5epochs This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 1.7206 - Rouge1: 0.5406 - Rouge2: 0.312 - Rougel: 0.3945 - Rougelsum: 0.4566
29ea6cac5917fd8b3c988a81e3b73e1c
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.373e-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: 16 - num_epochs: 5 - mixed_precision_training: Native AMP
0d1474da9427619d1056829185d3acc8
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.3723 | 1.33 | 16 | 1.8534 | 0.5204 | 0.299 | 0.3893 | 0.4441 | | 1.6579 | 2.67 | 32 | 1.7208 | 0.5427 | 0.3143 | 0.3915 | 0.459 | | 1.2397 | 4.0 | 48 | 1.7206 | 0.5406 | 0.312 | 0.3945 | 0.4566 |
5c3bc8b7ddd3e93f4bafd18bb75045c7
openrail
[]
false
<h3 align="center">PDF Paragraphs Extraction</h3> <p align="center">A model for extracting paragraphs from PDFs</p> This model uses features from the PDF to extract the text and paragraphs from it. It can be used as a service. The paragraphs contain the page number, the position in the page, the size, and the text.
9ab99c4336c6fce3aa5cf5eb010a2ebe
openrail
[]
false
Quick Start Download the service that uses the model: git clone https://github.com/huridocs/pdf_paragraphs_extraction.git cd pdf_paragraphs_extraction Start the service: ./run start Get the paragraphs from a PDF: curl -X GET -F 'file=@/PATH/TO/PDF/pdf_name.pdf' localhost:5051 To stop the server: ./run stop
b22e8f0dafd971becdc518e85afd4d6a
apache-2.0
['automatic-speech-recognition', 'pl']
false
exp_w2v2t_pl_hubert_s6 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pl)](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.
66a57644ba706afb6bd61a0fb9cdd163
apache-2.0
['translation']
false
opus-mt-es-sg * source languages: es * target languages: sg * OPUS readme: [es-sg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-sg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-sg/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-sg/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-sg/opus-2020-01-16.eval.txt)
f34a66c433327c6c230cdc26cae3bebe
apache-2.0
['deep-narrow']
false
T5-Efficient-XL-NL6 (Deep-Narrow version) T5-Efficient-XL-NL6 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block.
c0b34dea86e9aa42be31534bd41ef959
apache-2.0
['deep-narrow']
false
Details model architecture This model checkpoint - **t5-efficient-xl-nl6** - is of model type **Xl** with the following variations: - **nl** is **6** It has **737.59** million parameters and thus requires *ca.* **2950.37 MB** of memory in full precision (*fp32*) or **1475.18 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh |
365d273fe1b98ac3c25f88ae1e036786
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2156 - Accuracy: 0.924 - F1: 0.9243
b5e772551f9c8d2791b7e15f332ebefe
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8151 | 1.0 | 250 | 0.3062 | 0.9115 | 0.9089 | | 0.2428 | 2.0 | 500 | 0.2156 | 0.924 | 0.9243 |
b838aa438964efbc7998a96fc4c5c255
apache-2.0
['generated_from_keras_callback']
false
bigmorning_whisper This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set:
15daace63725be3740cf1ccc9f56884b
mit
['generated_from_trainer']
false
bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3 This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 2.1265 - Rouge1: 41.9656 - Rouge2: 15.3793 - Rougel: 24.0382 - Rougelsum: 37.6057 - Gen Len: 130.8531
fc21b1cde3ccbd0154466d99ee3e4a43
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP
f63a4c200084886b180b8e341dd36051
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.1485 | 1.0 | 33840 | 2.1265 | 41.9656 | 15.3793 | 24.0382 | 37.6057 | 130.8531 |
72ebbc69f1c3186c4c818ec8fb8ddd02
apache-2.0
['classification']
false
Overview State-funded social media operators are a hard-to-detect but significant threat to any democracy with free speech, and that threat is growing. In recent years, the extent of these state-funded campaigns has become clear. Russian campaigns undertaken to influence [elections](https://www.brennancenter.org/our-work/analysis-opinion/new-evidence-shows-how-russias-election-interference-has-gotten-more) are most prominent in the news, but other campaigns have been identified, with the intent to [turn South American countries against the US](https://www.nbcnews.com/news/latino/russia-disinformation-ukraine-spreading-spanish-speaking-media-rcna22843), spread disinformation on the [invasion of Ukraine](https://www.forbes.com/sites/petersuciu/2022/03/10/russian-sock-puppets-spreading-misinformation-on-social-media-about-ukraine/), and foment conflict in America's own culture wars by [influencing all sides](https://journals.sagepub.com/doi/10.1177/19401612221082052) as part of an effort to weaken America's hegemonic status. Iranian and [Chinese](https://www.bbc.com/news/56364952) efforts are also well-funded, though not as widespread or aggressive as those of Russia. Even so, Chinese influence is growing, and often it uses social media to spread specific narratives on [Xinjiang and the Uyghur situation](https://www.lawfareblog.com/understanding-pro-china-propaganda-and-disinformation-tool-set-xinjiang), Hong Kong, COVID-19, and Taiwan as well as sometimes supporting [Russian efforts](https://www.brookings.edu/techstream/china-and-russia-are-joining-forces-to-spread-disinformation/). We need better tools to combat this disinformation, both for social media administrators as well as the public. As part of an effort towards that, we have created a proof-of-concept tool that can be operated via browser extension to identify likely state-funded social media operators on Twitter through inference performed on tweet content. The core of the tool is a DistilBERT language transformer model that has been finetuned on 250K samples of known state operator tweets and natural tweets pulled from the Twitter API. It is highly accurate at distinguishing normal users from state operators (99%), but has some limitations due to sampling recency bias. We intend to iteratively improve the model as time goes on.
d7ccea973d98cea827e4d0ce277457e3
apache-2.0
['classification']
false
Usage You can try out the model by entering in a sequence of 1-10 tweets. Each should be separated by pipes, as follows: "this is tweet one | this is tweet two." The model will then classify the sequence as belonging to a state operator or a normal user.
0ffd4023295354d5d69d3b7c972f1d2a
apache-2.0
['classification']
false
Further Information You can obtain further information on the data collection and training used to create this model at the following Github repo: [State Social Operator Detection](https://github.com/curt-tigges/state-social-operator-detection)
cebb0259f4ea83ee2d9673d6dd2b7e64
apache-2.0
['summarization', 'summary', 'booksum', 'long-document', 'long-form']
false
Updates _As I update this WIP checkpoint, I will post a note here._ - July 26, 2022: add two more epochs of training, metrics starting to be _almost_ as good as the more-tuned `base` variant - July 8, 2022: add checkpoint with ~4 epochs of training on A100, equating to approx 350 steps of functional batch size 128 - July 4, 2022: add checkpoint with six additional epochs of training with the dataset summary outputs filtered to 1024 **tokens**, resolving the prior issue of short summaries.
711cb2e8850b1d5066680605a354789e
apache-2.0
['summarization', 'summary', 'booksum', 'long-document', 'long-form']
false
About - a checkpoint of [Stancld/longt5-tglobal-large-16384-pubmed-3k_steps](https://huggingface.co/Stancld/longt5-tglobal-large-16384-pubmed-3k_steps) trained on `kmfoda/booksum` for about 26 epochs - max input lengths during training vary between 8192 and 16384 tokens depending on GPU availability. This checkpoint was **trained with 16384 tokens as the max input length for the final 10+ epochs**
d08322316e30ea119700e8cb98acb801
apache-2.0
['summarization', 'summary', 'booksum', 'long-document', 'long-form']
false
Comparisons - compare to [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). - **inference API has been disabled because it's too compute-intensive :/**
ca7602abd3156a3b8e6f8e117f5758fe
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 16 - 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: 0.01 - training_steps: 12588 - mixed_precision_training: Native AMP
5c24c616ec50fa97fbe8c15f0aa77c8c
apache-2.0
['generated_from_trainer']
false
Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'batch_size': 128, 'every_n_steps': 384, 'force_call_on': [12588], 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'bad_words_ids': [[32769]], 'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'bad_words_ids': [[32769]], 'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 384, 'force_call_on': [12588], 'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>', 'should_insert_prefix': False}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': 'cf05a2b0558c03b08c78f07662c22989785b9520'}, 'num_additional_tokens': 2, 'path_or_name': 'kejian/mighty-mle'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'kejian/mighty-mle', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'literal-conditional', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 12588, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
b0821f86ed41d18be7b4d8adcaf8e250
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 12588 - mixed_precision_training: Native AMP
4fa722b807a31caea5e76afeaa1e646c
apache-2.0
['generated_from_trainer']
false
Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'every_n_steps': 256, 'force_call_on': [6294], 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 256, 'force_call_on': [6294], 'gpt3_kwargs': {'model_name': 'code-cushman-001'}, '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': 'codeparrot/codeparrot-small'}, 'objective': {'alpha': 0.05, 'beta': 1, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 256, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'vigor-awr', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 6294, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
b2aba4bbe0625ab7ba9e997cf01751d5
apache-2.0
['generated_from_trainer']
false
finetuning-sentiment-model-imdb 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: 0.2368 - Accuracy: 0.9309 - F1: 0.9316
182c169a41fd1b3cac59f1ea15acf778
apache-2.0
['generated_from_keras_callback']
false
S1d-dha-nth3/ncert_bio This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.6150 - Validation Loss: 2.5873 - Epoch: 14
ccfb0d6542bde25f5b923fdee1f3c6fc
apache-2.0
['generated_from_keras_callback']
false
Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -647, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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: float32
cd4e8dee465bda43c243bcc6bebf4484
apache-2.0
['generated_from_keras_callback']
false
Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.5434 | 2.8928 | 0 | | 2.9142 | 2.6476 | 1 | | 2.6884 | 2.5008 | 2 | | 2.6079 | 2.5775 | 3 | | 2.5748 | 2.5737 | 4 | | 2.6031 | 2.5074 | 5 | | 2.6237 | 2.5028 | 6 | | 2.5849 | 2.5862 | 7 | | 2.6154 | 2.4751 | 8 | | 2.5584 | 2.4866 | 9 | | 2.6107 | 2.5268 | 10 | | 2.5852 | 2.5659 | 11 | | 2.5915 | 2.5768 | 12 | | 2.5678 | 2.7020 | 13 | | 2.6150 | 2.5873 | 14 |
00cb23269d2a9615a6509acd0435e26e
apache-2.0
['generated_from_trainer']
false
wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn) on the None dataset.
3bf0bd1d4e83e9106f6d697a22357d62
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 32 - 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: 1 - num_epochs: 1 - mixed_precision_training: Native AMP
e4cdac36af473e7bc56a11ebb640f05d
apache-2.0
['Quality Estimation', 'siamesetransquest', 'da']
false
TransQuest: Translation Quality Estimation with Cross-lingual Transformers The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level. With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
bcef934c89a2bf166a9e0198d23cdade
apache-2.0
['Quality Estimation', 'siamesetransquest', 'da']
false
Features - Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment. - Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps. - Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented. - Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
571187852aa4215b65cba632b899ca27
apache-2.0
['Quality Estimation', 'siamesetransquest', 'da']
false
Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-multilingual") predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]]) print(predictions) ```
87d327cce5ea4c0eabdb839e969dae47
apache-2.0
['Quality Estimation', 'siamesetransquest', 'da']
false
Documentation For more details follow the documentation. 1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip. 2. **Architectures** - Checkout the architectures implemented in TransQuest 1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation. 2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation. 3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks. 1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/) 2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/) 4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level 1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/) 2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/) 5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
b26c40444b856b2326aeca1aa6f1b03b
apache-2.0
['Quality Estimation', 'siamesetransquest', 'da']
false
Citations If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/). ```bash @InProceedings{ranasinghe2021, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers}, booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics}, year = {2021} } ``` If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020. ```bash @InProceedings{transquest:2020a, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, year = {2020} } ``` ```bash @InProceedings{transquest:2020b, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest at WMT2020: Sentence-Level Direct Assessment}, booktitle = {Proceedings of the Fifth Conference on Machine Translation}, year = {2020} } ```
c6f61ea898692993048dc2f29db0e6bc
apache-2.0
['automatic-speech-recognition', 'ja']
false
exp_w2v2t_ja_hubert_s334 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ja)](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.
995c9592c6c261519cacb153edcf9a89
apache-2.0
['generated_from_trainer']
false
cvt-13-384-in22k-FV-finetuned-memes This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5595 - Accuracy: 0.8346 - Precision: 0.8327 - Recall: 0.8346 - F1: 0.8322
147841c52272cf517949be07565ad5b7
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - 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
0ae730b74cb3c051d074693c37a13380
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.4066 | 0.99 | 20 | 1.2430 | 0.5124 | 0.5141 | 0.5124 | 0.4371 | | 1.0813 | 1.99 | 40 | 0.8244 | 0.6893 | 0.6834 | 0.6893 | 0.6616 | | 0.8392 | 2.99 | 60 | 0.6334 | 0.7612 | 0.7670 | 0.7612 | 0.7570 | | 0.7065 | 3.99 | 80 | 0.5819 | 0.7767 | 0.7799 | 0.7767 | 0.7672 | | 0.5751 | 4.99 | 100 | 0.5365 | 0.8176 | 0.8216 | 0.8176 | 0.8130 | | 0.4896 | 5.99 | 120 | 0.4943 | 0.8308 | 0.8257 | 0.8308 | 0.8265 | | 0.4487 | 6.99 | 140 | 0.5399 | 0.8107 | 0.8069 | 0.8107 | 0.8054 | | 0.4349 | 7.99 | 160 | 0.4892 | 0.8300 | 0.8285 | 0.8300 | 0.8273 | | 0.43 | 8.99 | 180 | 0.4984 | 0.8454 | 0.8465 | 0.8454 | 0.8426 | | 0.4372 | 9.99 | 200 | 0.5573 | 0.8192 | 0.8221 | 0.8192 | 0.8157 | | 0.3994 | 10.99 | 220 | 0.5158 | 0.8300 | 0.8284 | 0.8300 | 0.8281 | | 0.3883 | 11.99 | 240 | 0.5495 | 0.8354 | 0.8317 | 0.8354 | 0.8314 | | 0.406 | 12.99 | 260 | 0.5298 | 0.8284 | 0.8285 | 0.8284 | 0.8246 | | 0.3355 | 13.99 | 280 | 0.5401 | 0.8393 | 0.8346 | 0.8393 | 0.8357 | | 0.395 | 14.99 | 300 | 0.5915 | 0.8308 | 0.8278 | 0.8308 | 0.8261 | | 0.3612 | 15.99 | 320 | 0.5852 | 0.8408 | 0.8378 | 0.8408 | 0.8368 | | 0.3765 | 16.99 | 340 | 0.5509 | 0.8385 | 0.8351 | 0.8385 | 0.8356 | | 0.3688 | 17.99 | 360 | 0.5668 | 0.8416 | 0.8398 | 0.8416 | 0.8387 | | 0.3503 | 18.99 | 380 | 0.5626 | 0.8393 | 0.8371 | 0.8393 | 0.8365 | | 0.3611 | 19.99 | 400 | 0.5595 | 0.8346 | 0.8327 | 0.8346 | 0.8322 |
11d60fee6990cfaedcb41733507d0507
apache-2.0
['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sl']
false
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset. It achieves the following results on the evaluation set: - Loss: 0.2855 - Wer: 0.2401
11ea88f56c9bab8d3f311b1a84c82c59
apache-2.0
['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sl']
false
Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1
7b0b4561c3ce77c48c10d63d5a44d45d
apache-2.0
['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sl']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP
25ae406ca0cd4d7348ac2cc96d57f454
apache-2.0
['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sl']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.9294 | 6.1 | 500 | 2.9712 | 1.0 | | 2.8305 | 12.2 | 1000 | 1.7073 | 0.9479 | | 1.4795 | 18.29 | 1500 | 0.5756 | 0.6397 | | 1.3433 | 24.39 | 2000 | 0.4968 | 0.5424 | | 1.1766 | 30.49 | 2500 | 0.4185 | 0.4743 | | 1.0017 | 36.59 | 3000 | 0.3303 | 0.3578 | | 0.9358 | 42.68 | 3500 | 0.3003 | 0.3051 | | 0.8358 | 48.78 | 4000 | 0.3045 | 0.2884 | | 0.7647 | 54.88 | 4500 | 0.2866 | 0.2677 | | 0.7482 | 60.98 | 5000 | 0.2829 | 0.2585 | | 0.6943 | 67.07 | 5500 | 0.2782 | 0.2478 | | 0.6586 | 73.17 | 6000 | 0.2911 | 0.2537 | | 0.6425 | 79.27 | 6500 | 0.2817 | 0.2462 | | 0.6067 | 85.37 | 7000 | 0.2910 | 0.2436 | | 0.5974 | 91.46 | 7500 | 0.2875 | 0.2430 | | 0.5812 | 97.56 | 8000 | 0.2852 | 0.2396 |
c524395c0bc3407e0ea0de7a5bf78b0b
mit
['conversational']
false
A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT) DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev
b67190f1c7c9182d4e9ec8c9e69f3b4c
mit
['conversational']
false
human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test. The model is trained on 147M multi-turn dialogue from Reddit discussion thread. * Multi-turn generation examples from an interactive environment: |Role | Response | |---------|--------| |User | Does money buy happiness? | | Bot | Depends how much money you spend on it .| |User | What is the best way to buy happiness ? | | Bot | You just have to be a millionaire by your early 20s, then you can be happy . | |User |This is so difficult ! | | Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
0e36e8d9e5555eca4210ada5cdaa6a69
mit
['conversational']
false
How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small") model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
593d6ca8fb05b932dfee3da2d7496fd6
afl-3.0
[]
false
This model is used detecting **abusive speech** in **Code-Mixed Hindi**. It is finetuned on MuRIL model using code-mixed hindi abusive speech dataset. The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive) LABEL_0 :-> Normal LABEL_1 :-> Abusive
d5a4c7bd8f2a23f5bd99854c9bb287fb
afl-3.0
[]
false
For more details about our paper Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022. ***Please cite our paper in any published work that uses any of these resources.*** ~~~ @article{das2022data, title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages}, author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh}, journal={arXiv preprint arXiv:2204.12543}, year={2022} } ~~~
dc15d4b08e2c7fe72431b4faf36b74cc
mit
[]
false
leica on Stable Diffusion This is the `<leica>` 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`: ![<leica> 0](https://huggingface.co/sd-concepts-library/leica/resolve/main/concept_images/3.jpeg) ![<leica> 1](https://huggingface.co/sd-concepts-library/leica/resolve/main/concept_images/0.jpeg) ![<leica> 2](https://huggingface.co/sd-concepts-library/leica/resolve/main/concept_images/2.jpeg) ![<leica> 3](https://huggingface.co/sd-concepts-library/leica/resolve/main/concept_images/1.jpeg)
90147e91254f694f1fdf7935bcb9f6e6
apache-2.0
['generated_from_trainer']
false
tiny-mlm-glue-wnli-target-glue-stsb This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8784 - Pearson: 0.7929 - Spearmanr: 0.7891
493bd5814fb14d8f2525311245f11c6b
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 3.3443 | 2.78 | 500 | 1.5642 | 0.5784 | 0.6011 | | 1.2259 | 5.56 | 1000 | 1.0907 | 0.7358 | 0.7382 | | 0.8948 | 8.33 | 1500 | 0.9367 | 0.7750 | 0.7751 | | 0.7357 | 11.11 | 2000 | 0.8525 | 0.7934 | 0.7905 | | 0.6119 | 13.89 | 2500 | 0.8436 | 0.7977 | 0.7944 | | 0.5301 | 16.67 | 3000 | 0.8999 | 0.7947 | 0.7928 | | 0.4657 | 19.44 | 3500 | 0.8341 | 0.7989 | 0.7943 | | 0.4104 | 22.22 | 4000 | 0.8818 | 0.7972 | 0.7930 | | 0.3686 | 25.0 | 4500 | 0.8811 | 0.7973 | 0.7929 | | 0.3348 | 27.78 | 5000 | 0.8784 | 0.7929 | 0.7891 |
20d045ef6c3cbad5ec4ed7c465637bf3
cc-by-4.0
['espnet', 'audio', 'automatic-speech-recognition']
false
Demo: How to use in ESPnet2 ```bash cd espnet git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b pip install -e . cd egs2/bn_openslr53/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/bengali_blstm ``` <!-- Generated by scripts/utils/show_asr_result.sh -->
db03ceddf134eaed4c7fc2fdfb4fecea
cc-by-4.0
['espnet', 'audio', 'automatic-speech-recognition']
false
Environments - date: `Sun May 22 21:21:37 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b` - Commit date: `Thu Apr 28 19:50:59 2022 -0400`
2dc369b2475aa9c0466c1950f025ae80
cc-by-4.0
['espnet', 'audio', 'automatic-speech-recognition']
false
ASR config <details><summary>expand</summary> ``` config: conf/train_asr_rnn.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_bn_rnn ngpu: 1 seed: 0 num_workers: 1 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: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: 3 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: - 10 nbest_averaging_interval: 0 grad_clip: 5.0 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: null 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: null batch_size: 30 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_bpe1000/train/speech_shape - exp/asr_stats_raw_bpe1000/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_bpe1000/valid/speech_shape - exp/asr_stats_raw_bpe1000/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 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/sbn_train/wav.scp - speech - sound - - dump/raw/sbn_train/text - text - text valid_data_path_and_name_and_type: - - dump/raw/sbn_dev/wav.scp - speech - sound - - dump/raw/sbn_dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adadelta optim_conf: lr: 0.1 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - র - ে - ন - ের - া - ল - ক - ্ - ো - ত - ি - স - ▁ - ই - ী - য় - ম - ু - ▁আ - প - ব - তে - দ - শ - কে - টি - ্য - হ - ▁এ - ▁না - ▁ব - ও - গ - ট - রা - ▁অ - জ - ▁বি - ▁বা - ▁স - না - ার - ▁করে - ধ - নি - ▁ম - লে - ▁জ - ▁ও - ▁হ - চ - তা - দের - ▁মা - িত - ▁থেকে - ্যা - ণ - '-' - ▁প্র - তি - ▁হয় - ায় - িক - ▁এক - ▁পা - ▁ক - ঁ - ভ - ▁ভ - ▁সা - লা - ▁শ - ',' - ্র - ▁এই - ▁নি - ▁প - বা - ▁পর - ফ - ▁সে - ক্ষ - ছে - মা - ষ - ▁কা - টা - বে - িয়া - ড় - ▁দ - ▁চ - লি - ▁ই - ▁হা - ▁তার - ▁যে - থ - । - ড - ুল - িয়ে - ▁গ - বি - ▁তা - রি - কা - ▁র - ▁ফ - পা - ▁ন - ▁করা - ং - ▁আর - উ - নে - খ - য়ে - ▁নিয়ে - ▁তিনি - ▁একটি - নের - ▁হয়েছে - ্ব - ▁ত - ▁জন্য - ▁যা - বার - ঙ্গ - ান - স্ত - কার - জা - ূ - ঠ - ুর - ▁হবে - ▁মি - দা - াই - ▁জা - ▁বলে - ▁কি - ড়া - ▁ঘ - ▁দু - হা - ত্র - ০ - ছেন - ▁কথা - সি - াম - ▁ছিল - ▁উ - ▁বল - ▁তাদের - ৃ - ▁রা - ▁সঙ্গে - ▁প্রতি - ▁এবং - ▁ধ - ▁ল - ছ - ▁খা - ▁বে - ▁সময় - য়া - জন - মি - ন্ত - ▁করতে - ▁সু - ▁করেন - ীর - ৌ - ▁অনেক - গুলো - ষ্ট - ধা - সা - ▁হয়ে - ▁মধ্যে - ▁চা - ▁লা - ির - ▁১ - ▁সং - োর - ভাবে - ▁আমি - ১ - শা - াল - জি - ▁তারা - ▁যায় - মান - ▁কাজ - ▁কিছু - ▁দিয়ে - টে - রণ - ▁ড - ▁উপ - স্থ - দি - সে - ▁মে - ▁সরকার - ▁খ - ▁পার - ীয় - ক্ত - ওয়া - স্ট - এ - ▁বাংলাদেশ - ড়ে - ন্ট - ▁২ - ▁আছে - ▁সব - ছি - ▁দি - ▁আমার - ▁এখন - মে - ▁বছর - ▁ট - ▁শা - কি - ন্ড - ▁নাম - ▁কোন - দিন - পুর - ▁সম্ - ছিল - ▁পুলিশ - ▁য - ৈ - ▁মানুষ - ▁দা - েই - ▁এর - ▁সালে - ▁কর - ঘ - গ্র - ▁দিন - ▁পারে - ্ম - ৫ - ▁দেশ - ▁দেখ - ▁স্ব - ▁সম - ▁১৯ - ▁সি - ▁শুরু - ▁প্রথম - ত্ - ▁তো - ্ট - ▁আগে - ▁কোনো - ▁রয়েছে - ▁হচ্ছে - ▁অব - ছিলেন - যোগ - জে - ▁ভারত - ▁নে - প্র - ▁সেই - গা - ▁গা - হি - ন্ন - ▁ছ - ▁জন - ▁নির্ - খা - পি - ▁পে - ▁স্ - াব - ▁মো - ▁অনু - ▁কিন্তু - ৯ - ▁পরি - ▁ঢাকা - তার - লো - ▁বিষয় - ▁তাঁর - ৪ - র্থ - ▁অ্যা - ▁ঘটনা - ▁শেষ - ড়ি - লেন - ▁আমাদের - ▁বড় - দেশ - ▁নেই - ▁ব্যা - ানো - ▁বেশি - মার - বাস - ▁তবে - ▁কো - শি - ▁বিভিন্ন - ▁নয় - ৭ - নী - ৩ - ▁দল - ▁দেখা - ঝ - ▁করার - ▁কে - ▁হলে - ুক - ▁গু - ▁৩ - ৬ - ▁মনে - ▁নির্বাচন - ▁রাজ - ▁করেছে - ীন - লের - িতে - ▁একটা - ঞ্চ - ▁রাখ - ▁থাক - ▁আমরা - ▁চল - ২ - ▁কাছে - ▁মু - ▁পড় - ▁সহ - ▁হিসেবে - জ্ঞ - ান্ত - ণ্ড - ৎ - য়ের - ▁পু - ▁একজন - ▁বলেন - ুন - িং - ’ - ▁বাংলা - টার - ুম - ঞ্জ - ▁বাড়ি - ▁গত - ▁হাজার - ▁মতো - ডি - ▁তিন - দ্ধ - ▁এমন - ▁কয়েক - ▁কম - ত্ব - ্রা - ▁দিকে - ▁ছিলেন - ▁পড়ে - নার - ▁করি - কাল - ▁মুখ - ▁উঠ - র্ত - ▁টাকা - চার - শে - ▁এসে - ▁দুই - ▁করেছেন - ▁লোক - ম্প - ৮ - ষ্ঠ - ▁মহা - ▁কু - ▁থাকে - বাদ - চি - ▁এলাকা - ▁জানান - ▁প্রায় - ▁দেয়া - ▁গেল - য - চ্ছে - ▁ছবি - ▁নতুন - ▁অবস্থা - ▁অভি - ▁আজ - ▁কার - ▁খু - ▁জানা - ▁করছে - টির - ▁বাংলাদেশের - ▁বন্ধ - কারী - ▁অন্য - ▁ধরে - প্ত - ▁তাকে - ▁গেছে - ▁শি - চা - আ - ▁চাল - ▁আল - ▁৫ - ▁উত্ত - ▁ঝ - ▁জীবন - লার - ঙ - ▁প্রকাশ - ▁মেয়ে - ▁রে - ▁দেশের - ▁খেল - ▁মূল - ভি - ঙ্ক - ▁চি - ▁পর্যন্ত - ▁সাথে - লাম - ▁৪ - ▁টি - ▁বো - ▁আইন - গত - ▁হতে - ▁ভালো - . - স্ক - ▁অভিযোগ - ন্স - ▁কারণে - ▁অর্থ - ▁অপ - ক্স - বু - ▁২০ - ▁পাওয়া - ▁খুব - ▁মন - সম - ল্লা - ব্দ - ▁পি - ▁ওই - ▁করবে - য়ার - সহ - ক্ষণ - ▁নারী - ম্ব - ▁ফা - ▁বেশ - ▁পেয়ে - দে - ▁তখন - িয়ার - ▁ক্যা - ▁ছেলে - ▁চার - ভার - ▁দিতে - ▁ক্র - ▁গান - বাহিনী - ▁ভি - কৃত - ▁গো - বল - ▁ইসলাম - ▁জি - ▁ডি - ন্দ্র - ▁গ্রাম - ▁ওপর - ▁ভোট - ▁পাঠ - ▁গিয়ে - ▁মামলা - ▁ব্যবস্থা - সার - যুক্ত - ▁মাস - দার - ▁সেখানে - ▁জন্ম - ▁পদ - ▁কেউ - র্ণ - ▁দেওয়া - ভাগ - ▁১০ - ▁উদ্ - োয়া - রূপ - ▁ফেল - ▁তৈরি - ▁খবর - ▁কেন - ▁ভাষা - ▁৬ - ▁ভাব - ▁নেতা - ▁জানিয়েছে - ▁কী - ফা - ▁থাকা - ▁লি - টের - ▁ছা - ▁হল - ▁গ্র - ▁কর্ম - ▁সদস্য - ▁জাতীয় - ▁ব্র - দু - ▁কেন্দ্র - ▁হওয়ার - ▁দেব - ▁চলে - ▁হলো - তু - ▁বিশ্ব - ▁যাওয়া - ▁যাবে - ▁ট্র - ▁সম্পর্ক - ▁দিয়েছে - ▁যদি - ▁বিরুদ্ধে - ▁বিশেষ - ▁করলে - ▁ছোট - ▁অধি - ▁শুন - ▁আবার - ▁কারণ - ▁দলের - ▁ফি - ▁স্ট - ▁দেয় - ▁শিল্প - ▁রাজনৈতিক - ▁বলা - ▁ছাড়া - ▁জেলা - ▁দেখে - ▁প্রধান - ▁এসব - বন্ধ - ▁কর্মকর্তা - চ্ছি - ▁তথ্য - ▁অংশ - ▁দশ - ▁তাহা - মন্ত্রী - ৃত - ▁ঠিক - ▁রাত - ▁আসা - ▁থানা - ▁গোল - রাজ - ▁মৃত্যু - ▁রি - ▁পথ - ্যান - ▁বিচার - ▁শ্রমিক - ▁গল্প - ▁সকাল - ▁হাতে - ▁এটা - ▁কবি - ▁বাবা - ▁দাবি - ▁চাই - ▁মাধ্যমে - ▁হয়েছিল - ▁ঢ - ▁যাচ্ছে - ▁২০০ - ▁চলচ্চিত্র - ▁রহমান - ▁লেখা - ▁দেন - ▁পুরুষ - চিত্র - ▁ব্যবহার - ▁অনুষ্ঠান - ▁বর্তমান - ▁ধর্ম - ▁দাঁড় - ▁নিহত - ঃ - চ্ছ - ▁চেষ্টা - ▁চোখ - ▁উপজেলা - ▁আদালত - ▁সামনে - ▁রু - ▁চেয়ে - ▁সর্ব - ▁হত্যা - ▁গণ - ▁ডাক - ▁দ্বিতীয় - ▁ধরনের - ▁কবিতা - ▁ফলে - ▁সবচেয়ে - গুলি - ▁মোট - ▁পরিবার - ▁শিশু - ▁হোসেন - ▁রেখে - ▁রায় - ▁মাথা - ▁দুর্ - ▁৮ - ▁টা - ▁৭ - ▁বসে - ▁ওয়া - ▁ব্যক্তি - ▁শুধু - ▁ব্যাংক - ▁পাকিস্তান - ▁যখন - ▁করিয়া - ▁লিখ - পূর্ণ - ▁বিশ্ববিদ্যালয় - ▁সংখ্যা - ▁যুদ্ধ - ▁হইয়া - ▁ক্ষমতা - ▁সাধারণ - ▁কোটি - ▁শিক্ষা - ▁আলো - ▁তুলে - ▁সত্য - ▁ঘটে - '''' - ▁দূর - ▁প্রশ্ন - ুদ্ধ - ▁লাখ - ▁নিজের - েশন - ▁আলোচনা - ঈ - ▁ক্রিকেট - ▁সমাজ - ▁বয়স - ▁গ্রহণ - ▁জায়গা - ▁ব্যবসা - বর্তী - জীব - কল্প - ▁প্রত্য - ▁মাত্র - ▁উৎ - ▁শহরে - ▁এখানে - ▁নেয়া - ▁ঘোষণা - ▁সকল - ▁আটক - ▁নিরাপত্তা - ▁পাঁচ - ▁পূর্ব - ▁রাষ্ট্র - ▁ভাই - ▁বহু - ▁পরীক্ষা - ▁পুরো - ▁বাইরে - ▁থাকবে - ▁ক্ষেত্রে - ▁স্থান - ▁ম্যাচ - ▁ঘরে - ▁সবাই - ার্ড - ▁উদ্ধার - ▁ইতিহাস - ▁সাহিত্য - ▁সুযোগ - ▁আন্দোলন - ▁যুক্তরাষ্ট্র - দর্শন - ▁১২ - ▁১৮ - ▁প্রেম - ▁আন্তর্জাতিক - ল্যান্ড - ▁সমস্যা - ▁বিভাগ - ▁সিদ্ধান্ত - ▁মধ্য - ন্দি - ▁ছাত্র - ▁গাড়ি - ▁দীর্ঘ - ▁সংবাদ - ▁প্রয়োজন - ▁সিনেমা - ▁রাজধানী - ▁স্থানীয় - ▁একটু - ▁বাজার - জ্জ - ▁পৃথিবী - ▁বিশ্বাস - ▁আহত - ▁দায়িত্ব - ▁হরতাল - ▁সম্ভব - ▁অফিস - ▁অভিনয় - ▁কলেজ - ▁চট্টগ্রাম - ▁ক্ল - ▁দক্ষিণ - ▁পক্ষে - ▁মুক্তি - ▁সংসদ - ‘ - ▁উপস্থিত - ▁ফিরে - ▁আগামী - ▁সংগঠন - ▁মিনিট - ▁হামলা - ▁প্রতিষ্ঠান - ▁পোশাক - ▁প্ল - ▁সৃষ্টি - ▁কমিশন - ▁আমাকে - ▁তদন্ত - ▁উচ্চ - ▁রাজনীতি - দ্দ - ▁দর্শক - ▁তুমি - ▁পরিস্থিতি - াহার - ▁ক্ষতি - ▁আত্ম - ▁গ্রেপ্তার - ▁ফুট - ▁পাশাপাশি - মূল - ▁প্রধানমন্ত্রী - কর্মী - ▁সুন্দর - ▁নিয়ম - ▁আগুন - বিজ্ঞান - ▁সাংবাদিক - ▁লক্ষ্য - ▁অবশ্য - ▁শরীর - ▁উল্লেখ - ▁শতাংশ - ▁স্কুল - ভূত - ▁গ্রন্থ - ▁কখনো - ▁প্রাণ - ▁কারখানা - ▁হিন্দু - ▁বিবিসি - ▁আপনার - ▁আহমেদ - ▁স্ত্রী - বর্ষ - ▁শক্তি - সভা - ▁রাস্তা - ▁রকম - ▁পশ্চিম - ▁অপরাধ - ▁আসছে - ▁সংস্থা - ▁পৌঁছ - ▁দোকান - ▁পত্রিকা - ▁লেখক - ▁সন্তান - ▁ভেতর - ▁এগিয়ে - ▁নদী - ▁হইল - ▁পরিবেশ - ▁প্রেসিডেন্ট - ▁ছেড়ে - ▁চেয়ারম্যান - ▁ধারা - বৃত্ত - ▁বিক্রি - ▁শ্রী - ▁রক্ষা - ▁দ্রুত - ▁পরিচয় - ▁মালিক - ▁উপন্যাস - ▁শিক্ষার্থী - ▁অন্যতম - ▁চরিত্র - ▁প্রতিবেদন - ▁প্রস্তুত - ▁অভিযান - তন্ত্র - ▁অগ্নি - ▁জনগণ - ▁বৃহস্পতিবার - ▁ব্যাপক - ▁অনুযায়ী - ▁পরিবর্তন - ▁কলকাতা - ভূমি - ▁নজরুল - ▁ভূমিকা - ▁জনপ্রিয় - ▁শিক্ষক - ▁তেমন - ▁অন্যান্য - ▁বিদ্যুৎ - খ্যাত - ▁অস্ত্র - ▁প্রস্তাব - ▁স্বামী - ▁পরিচিত - ▁আয়োজন - ▁শনিবার - ▁তাঁকে - ▁যাত্রী - প্রাপ্ত - ▁কর্মসূচি - ▁গঠন - ▁প্রভাব - ▁কৃষ্ণ - ▁সমাবেশ - ▁সূত্র - ▁অনুষ্ঠিত - ▁পর্যায়ে - ঋ - ▁পুরস্কার - ▁বিক্ষোভ - ▁নিয়ন্ত্রণ - ▁রোববার - ▁প্রার্থী - ▁যোগাযোগ - ▁সোমবার - ▁মার্চ - ▁কমিটি - ▁সংঘর্ষ - ▁বুধবার - ▁সামাজিক - ▁তাঁদের - ▁মার্কিন - ▁সামরিক - ▁নিজেদের - ▁মঙ্গলবার - ▁বক্তব্য - ▁চুক্তি - ▁যুগ - ▁বৈঠক - ▁ইউনিয়ন - ▁মোহাম্মদ - অ - ▁তাঁহার - ▁নির্মাণ - ▁জানুয়ারি - ▁আবেদন - ▁বিশ্বকাপ - ▁ফেব্রুয়ারি - ▁তরুণ - ▁হিসাব - ▁সন্ধ্যা - ▁পরিকল্পনা - ▁উইকেট - ▁ধারণা - ▁আনন্দ - মুক্ত - ▁উদ্দেশ্য - ▁চিকিৎসা - ▁উন্নয়ন - ▁আধুনিক - ▁ভিত্তি - ':' - "\x94" - ঢ - ‍ - ় - e - / - i - r - t - o - '%' - l - a - n - '!' - p - '"' - s - '?' - d - '0' - '3' - u - ঞ - f - g - c - m - h - – - w - b - ; - x - '8' - '5' - '9' - k - ” - y - H - L - T - j - ৗ - B - K - _ - z - “ - F - v - '4' - '1' - '2' - ঔ - ঊ - "\x93" - D - O - œ - ঐ - ৰ - — - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.5 use_preprocessor: true token_type: bpe bpemodel: data/token_list/bpe_unigram1000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_bpe1000/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: vgg_rnn encoder_conf: rnn_type: lstm bidirectional: true use_projection: true num_layers: 4 hidden_size: 1024 output_size: 1024 postencoder: null postencoder_conf: {} decoder: rnn decoder_conf: num_layers: 2 hidden_size: 1024 sampling_probability: 0 att_conf: atype: location adim: 1024 aconv_chans: 10 aconv_filts: 100 required: - output_dir - token_list version: 0.10.6a1 distributed: false ``` </details>
5519105e3fa46c8c9ac557cd09acd90f
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 200 - mixed_precision_training: Native AMP
c7e80c0d4bb750a6209dcd49436cf456
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200
c586c3d19f2bd2c96e536a884cf4be24
apache-2.0
['summarization', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2
843adfe66b89c9bc2a7cc55bbc7b09c7
apache-2.0
['generated_from_trainer']
false
tiny-mlm-glue-cola-target-glue-qnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4706 - Accuracy: 0.7820
52c636dfbc2d8b25c4bab4f930ed10c3
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6037 | 0.15 | 500 | 0.5447 | 0.7315 | | 0.5395 | 0.31 | 1000 | 0.5304 | 0.7417 | | 0.5171 | 0.46 | 1500 | 0.4946 | 0.7626 | | 0.5141 | 0.61 | 2000 | 0.5316 | 0.7450 | | 0.5107 | 0.76 | 2500 | 0.4847 | 0.7712 | | 0.5031 | 0.92 | 3000 | 0.4687 | 0.7844 | | 0.4903 | 1.07 | 3500 | 0.4536 | 0.7897 | | 0.48 | 1.22 | 4000 | 0.4689 | 0.7829 | | 0.4677 | 1.37 | 4500 | 0.4769 | 0.7763 | | 0.474 | 1.53 | 5000 | 0.4706 | 0.7820 |
ec312e7f05b2ddc20a07f74ffa814a37
apache-2.0
['t5', 'french', 'punctuation']
false
🚀 Text Punctuator Based on Transformers model T5. T5 model fine-tuned for punctuation restoration. Model currently supports only French Language. More language supports will be added later using mT5. Train Datasets : Model trained using 2 french datasets (around 500k records): - [orange_sum](https://huggingface.co/datasets/orange_sum) - [mlsum](https://huggingface.co/datasets/mlsum) (only french text) More info will be added later.
09fa5825352b7aa1ab233857865bb036
apache-2.0
['t5', 'french', 'punctuation']
false
🚀 Usage **TextPunctuator as a wrapper of the model.** 1. Install the package. ```bash pip install TextPunctuator ``` 2. Simple example ```python from Punctuator import TextPunctuator punctuator = TextPunctuator(use_gpu=False)
b7f5d41d59f46ba5e8abac277a1b89ce
apache-2.0
['t5', 'french', 'punctuation']
false
text input text = "Sur la base de ces échanges Blake Lemoine a donc jugé que le système avait atteint \ un niveau de conscience lui permettant d'être sensible Ce dernier a ensuite envoyé \ par email un rapport sur la sensibilité supposée de LaMDA à deux cents employés de \ Google Très vite les dirigeants de l’entreprise ont rejeté les allégations" text_punctuated = punctuator.punctuate(text, lang='fr') text_punctuated
c6987829ac8b67ce8a14fc0da4178fed
apache-2.0
['t5', 'french', 'punctuation']
false
output : """ Sur la base de ces échanges, Blake Lemoine a donc jugé que le système avait atteint un niveau de conscience lui permettant d’être sensible. Ce dernier a ensuite envoyé par email un rapport sur la sensibilité supposée de LaMDA à deux cents employés de Google. Très vite, les dirigeants de l’entreprise ont rejeté les allégations. """ ```
f171671a5c32f34f1c22b222a5e268a5
apache-2.0
['automatic-speech-recognition', 'th']
false
exp_w2v2t_th_xls-r_s625 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (th)](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.
300582fc9624194006807876137d9930
creativeml-openrail-m
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']
false
LoRA DreamBooth - jianleo/lora_ruhua_sd_1k These are LoRA adaption weights for /root/autodl-tmp/sd_weights/models--runwayml--stable-diffusion-v1-5/snapshots/889b629140e71758e1e0006e355c331a5744b4bf. The weights were trained on a photo of rha woman using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
0a83e7254a9ffd4f453598c69a7d6ea5
apache-2.0
['exbert', 'multiberts', 'multiberts-seed-0']
false
MultiBERTs Seed 0 Checkpoint 140k (uncased) Seed 0 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-0](https://hf.co/multberts-seed-0). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani).
d7e744277c4319cb364e22f743ac6a89
apache-2.0
['exbert', 'multiberts', 'multiberts-seed-0']
false
How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-140k') model = BertModel.from_pretrained("multiberts-seed-0-140k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ```
befedc228a550b7937e28ee6e3d8bbde
mit
['generated_from_trainer']
false
xlm-roberta-base-finetuned-panx-en 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: 0.4004 - F1: 0.6700
04b8f84f3a41b4d0a651bfced0d3cd71
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1798 | 1.0 | 50 | 0.6616 | 0.4612 | | 0.5404 | 2.0 | 100 | 0.4206 | 0.6551 | | 0.3714 | 3.0 | 150 | 0.4004 | 0.6700 |
1644dfa9b1a17b53d5ed6d3ec4f3cf98
apache-2.0
['stanza', 'token-classification']
false
Stanza model for Wolof (wo) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 02:14:15.533
09bdf26b86642fda800808405734b191
apache-2.0
['translation']
false
epo-ita * source group: Esperanto * target group: Italian * OPUS readme: [epo-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-ita/README.md) * model: transformer-align * source language(s): epo * target language(s): ita * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.eval.txt)
1ce94fbde57c4bcf02392e5b4bd4dd38
apache-2.0
['translation']
false
System Info: - hf_name: epo-ita - source_languages: epo - target_languages: ita - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-ita/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'it'] - src_constituents: {'epo'} - tgt_constituents: {'ita'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.test.txt - src_alpha3: epo - tgt_alpha3: ita - short_pair: eo-it - chrF2_score: 0.465 - bleu: 23.8 - brevity_penalty: 0.9420000000000001 - ref_len: 67118.0 - src_name: Esperanto - tgt_name: Italian - train_date: 2020-06-16 - src_alpha2: eo - tgt_alpha2: it - prefer_old: False - long_pair: epo-ita - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
8056fe8ae2fa7389eea343fffcd11fa7
cc-by-sa-4.0
['spacy', 'token-classification']
false
Turkish large sized pipeline for TrSpaCy. Components: tok2vec, tagger, morphologizer, lemmatizer, parser, ner | Feature | Description | | --- | --- | | **Name** | `tr_core_news_lg` | | **Version** | `3.4.2` | | **spaCy** | `>=3.4.2,<3.5.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` | | **Components** | `tok2vec`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` | | **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) | | **Sources** | [UD Turkish BOUN](https://github.com/UniversalDependencies/UD_Turkish-BOUN) (Türk, Utku; Atmaca, Furkan; Özateş, Şaziye Betül; Berk, Gözde; Bedir, Seyyit Talha; Köksal, Abdullatif; Öztürk Başaran, Balkız; Güngör, Tunga; Özgür, Arzucan)<br />[Turkish Wiki NER dataset](https://github.com/turkish-nlp-suite/NER-datasets/tree/main/Turkish-Wiki-NER-Dataset) (Duygu Altinok, Co-one Istanbul)<br />[PANX/WikiANN](http://hlt.sztaki.hu/resources/hunnerwiki.html) (Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, Heng Ji)<br />[Large-sized Turkish Floret word vectors (MC4 corpus)](https://huggingface.co/turkish-nlp-suite/tr_vectors_web_lg) (Duygu Altinok) | | **License** | `cc-by-sa-4.0` | | **Author** | [Duygu Altinok](https://github.com/turkish-nlp-suite/turkish-spacy-models) |
f82db575950a4df48ac488abe4e102db
cc-by-sa-4.0
['spacy', 'token-classification']
false
Label Scheme <details> <summary>View label scheme (1552 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADP`, `ADV`, `ANum`, `ANum_Adj`, `ANum_Ness`, `ANum_Noun`, `ANum_With`, `ANum_Zero`, `Abr`, `Abr_With`, `Adj`, `Adj_Ness`, `Adj_With`, `Adj_Without`, `Adj_Zero`, `Adv`, `Adverb`, `Adverb_Adverb`, `Adverb_Noun`, `Adverb_Zero`, `Conj`, `Conj_Conj`, `DET`, `Demons`, `Demons_Zero`, `Det`, `Det_Zero`, `Dup`, `Interj`, `NAdj`, `NAdj_Aux`, `NAdj_Ness`, `NAdj_Noun`, `NAdj_Rel`, `NAdj_Verb`, `NAdj_With`, `NAdj_Without`, `NAdj_Zero`, `NNum`, `NNum_Rel`, `NNum_Zero`, `NOUN`, `Neg`, `Ness`, `Noun`, `Noun_Ness`, `Noun_Noun`, `Noun_Rel`, `Noun_Since`, `Noun_Verb`, `Noun_With`, `Noun_With_Ness`, `Noun_With_Verb`, `Noun_With_Zero`, `Noun_Without`, `Noun_Zero`, `PCAbl`, `PCAbl_Rel`, `PCAcc`, `PCDat`, `PCDat_Zero`, `PCGen`, `PCIns`, `PCIns_Zero`, `PCNom`, `PCNom_Adj`, `PCNom_Noun`, `PCNom_Zero`, `PRON`, `PUNCT`, `Pers`, `Pers_Ness`, `Pers_Pers`, `Pers_Rel`, `Pers_Zero`, `Postp`, `Prop`, `Prop_Conj`, `Prop_Rel`, `Prop_Since`, `Prop_With`, `Prop_Zero`, `Punc`, `Punc_Noun_Ness`, `Punc_Noun_Rel`, `Quant`, `Quant_Zero`, `Ques`, `Ques_Zero`, `Reflex`, `Reflex_Zero`, `Rel`, `SYM`, `Since`, `Since_Since`, `Verb`, `Verb_Conj`, `Verb_Ness`, `Verb_Noun`, `Verb_Verb`, `Verb_With`, `Verb_Zero`, `With`, `Without`, `Without_Zero`, `Zero` | | **`morphologizer`** | `NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `POS=PUNCT`, `POS=ADV`, `POS=NOUN`, `Case=Nom\|Number=Sing\|POS=ADJ\|Person=3`, `POS=DET`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3`, `POS=ADJ`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=3`, `POS=PRON`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Acc\|Number=Plur\|POS=NOUN\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Case=Nom\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Number=Sing\|POS=PROPN\|Person=3`, `POS=VERB\|Polarity=Pos`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Abl\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `POS=INTJ`, `Case=Abl\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Ins\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Loc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Acc\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3`, `POS=CCONJ`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Conv\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=ADP\|Person=3`, `Case=Dat\|Number=Plur\|POS=NOUN\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Nom\|POS=VERB\|Polarity=Pos`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Acc\|Number=Sing\|POS=PROPN\|Person=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `POS=ADP`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=3`, `Aspect=Perf\|Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Prog\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person=3`, `Mood=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Plur\|POS=ADJ\|Person=3`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=1`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Ins\|Number=Sing\|POS=NOUN\|Person=3`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Sing\|POS=ADJ\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Prog\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Mood=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2`, `Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Loc\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv\|Voice=Pass`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Equ\|Number=Sing\|POS=PRON\|Person=1`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Case=Loc\|POS=VERB\|Polarity=Pos\|Voice=Pass`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Mood=Des,Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Hab\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Ins\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Ins\|POS=VERB\|Polarity=Neg`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Nom\|POS=VERB\|Polarity=Pos\|Voice=Pass`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=ADJ\|Person=2,3\|Polarity=Pos`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Case=Nom\|POS=NOUN\|Polarity=Pos`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Loc\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Loc\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Ins\|Number=Sing\|POS=VERB\|Person=1`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `POS=VERB\|Polarity=Pos\|Voice=Pass`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=1`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Plur\|POS=PROPN\|Person=3`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Mood=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv`, `POS=AUX`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=NUM\|Person=3`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Gen\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Abbr=Yes\|Case=Gen\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Mood=Pot\|POS=VERB\|Polarity=Pos`, `Case=Abl\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=1`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `POS=VERB`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=3`, `Aspect=Perf\|Case=Loc\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Perf\|Case=Gen\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=3`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Rfl`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Equ\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=ADJ\|Person=1`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Cnd\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Nom\|NumType=Ord\|Number=Sing\|POS=NUM\|Person=3`, `Case=Nom\|Number=Sing\|POS=AUX\|Person=3`, `Case=Nom\|Number=Sing\|POS=ADV\|Person=3`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Hab\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Dat\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos`, `Case=Nom\|Number=Plur\|POS=AUX\|Person=3`, `Case=Ins\|POS=VERB\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Plur,Sing\|POS=NOUN\|Person=2,3`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=1,3\|Tense=Pres`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Conv`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Nom\|POS=ADV\|Polarity=Pos`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=1`, `POS=PROPN`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Equ\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Case=Loc\|POS=VERB\|Polarity=Pos`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Perf\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Imp\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Hab\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=1\|Person[psor]=1`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Loc\|Number=Sing\|POS=ADJ\|Person=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv\|Voice=Pass`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos`, `Aspect=Perf\|Number[psor]=Sing\|POS=AUX\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=2\|Person[psor]=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=2\|Person[psor]=3`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=2`, `POS=VERB\|Polarity=Neg`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos\|Tense=Pres`, `Number=Sing\|POS=VERB\|Person=3`, `Case=Equ\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=ADJ\|Person=3`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Abl\|Number=Sing\|POS=VERB\|Person=3`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Mood=Imp\|POS=VERB\|VerbForm=Conv`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Dat,Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Ins\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Gen\|Number=Sing\|POS=AUX\|Person=3`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Case=Abl\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2`, `Case=Loc\|Mood=Imp\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=2,3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=2`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Prog\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Prog\|Case=Nom\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=2`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Voice=Pass`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Prog\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Ins\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `POS=NUM`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur\|POS=PRON\|Person=1,3\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Cau`, `Case=Loc\|Number=Sing\|POS=NOUN\|Person=1`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres\|VerbForm=Conv`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Mood=Ind\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=2`, `Case=Acc\|Number=Sing\|POS=ADJ\|Person=3`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Case=Nom\|POS=VERB\|Polarity=Neg`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Abl\|POS=VERB\|Polarity=Pos`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `NumType=Ord\|POS=NUM`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=1\|Person[psor]=1`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Past`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Loc,Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `POS=SYM`, `Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Number=Plur\|POS=VERB\|Person=1`, `Case=Dat\|Number=Sing\|POS=ADP\|Person=3`, `Aspect=Hab\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|POS=PRON\|Person=1,3\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Voice=Cau`, `Aspect=Prog\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Case=Nom\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=NOUN\|Person=1,3\|Tense=Past`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|POS=ADP\|Tense=Pres\|VerbForm=Conv`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Mood=Nec\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Nom\|Number=Sing\|POS=PROPN\|Person=3\|Polarity=Pos`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=3`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=1`, `Case=Gen\|Number=Plur\|POS=PROPN\|Person=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Mood=Nec\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Voice=Cau`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=PRON\|Person=2,3\|Polarity=Pos\|PronType=Dem`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Evident=Nfh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Tense=Past`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Case=Loc\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Abl\|POS=VERB\|Polarity=Pos\|Voice=Pass`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Equ\|Number=Sing\|POS=NUM\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Mood=Cnd\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Hab\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv`, `Case=Ins\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=ADJ\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Pot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Dat\|Number=Plur\|POS=AUX\|Person=3`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos`, `Aspect=Perf\|Mood=Cnd\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Imp\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Equ\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Echo=Rdp\|POS=X`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Abl\|Number=Plur\|POS=PROPN\|Person=3`, `Aspect=Perf\|Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Past`, `Aspect=Prog\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Imp\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Equ\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Ins\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Imp\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Aspect=Imp\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Ins\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person=3\|Person[psor]=3`, `Evident=Nfh\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Conv`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=PROPN\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Evident=Nfh\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Conv`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Gen,Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Plur\|POS=ADP\|Person=3`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=1\|Person[psor]=1`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NUM\|Person=1\|Person[psor]=1`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=ADJ\|Person=1,3\|Tense=Past`, `Aspect=Hab\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Ins\|POS=VERB\|Polarity=Pos`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=PROPN\|Person=3`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Prog\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `POS=NOUN\|Polarity=Pos`, `Aspect=Imp\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=PRON\|Person=3\|Tense=Pres`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=ADV\|Person=3\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Cnd\|Number=Sing\|POS=ADV\|Person=3\|Tense=Pres`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Imp,Perf\|Mood=Gen\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Case=Abl\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Conv`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=1\|Person[psor]=2`, `Abbr=Yes\|Case=Nom\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Prog\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Loc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Abbr=Yes\|Case=Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=PROPN\|Person=3\|Tense=Past`, `Aspect=Imp\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Ins\|Number=Plur\|POS=NUM\|Person=3`, `Aspect=Prog\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Equ\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Prog\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Conv`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Abl\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Mood=Pot\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Hab,Perf\|Mood=Cnd,Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Prog\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Abl\|Mood=Gen\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Nom\|POS=VERB\|Polarity=Neg\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Case=Loc\|Number=Plur\|POS=NOUN\|Person=1`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=2\|Person[psor]=1`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab,Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|Polarity=Neg\|Tense=Past,Pres\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Imp\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Evident=Nfh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Imp\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Case=Nom\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=ADP\|Person=3`, `Aspect=Hab\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Prog\|Case=Nom\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=ADP\|Person=3\|Tense=Pres`, `Mood=Nec\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Rfl`, `Case=Acc\|Number=Sing\|POS=ADP\|Person=3`, `Case=Loc,Nom\|Number=Sing\|POS=PRON\|Person=3`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=3`, `Case=Nom\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Imp,Perf\|Mood=Gen\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut,Pres`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `POS=VERB\|Polarity=Pos\|Voice=Rfl`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Number=Sing\|POS=VERB\|Person=1`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=NUM\|Person=3`, `Case=Ins\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=1\|Person[psor]=3\|Tense=Past`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Number=Sing\|POS=ADP\|Person=3`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Plur\|POS=VERB\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Conv\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Cau`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pqp`, `Aspect=Perf\|Mood=Ind\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3\|Tense=Past`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|Voice=Pass`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Hab\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=ADJ\|Polarity=Pos`, `Aspect=Imp\|Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Acc\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=ADJ\|Person=1,3\|Tense=Pres`, `POS=PROPN\|Polarity=Pos`, `Aspect=Imp\|Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Voice=Cau`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Sing\|POS=ADP\|Person=3`, `Aspect=Perf\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Dat,Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3\|Tense=Pres`, `Evident=Nfh\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Prog\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Ins\|Number=Sing\|POS=VERB\|Person=2`, `Case=Nom\|Mood=Imp\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=2,3\|Person[psor]=3\|Polarity=Pos`, `Case=Loc\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Tense=Pres`, `Aspect=Imp\|Case=Dat\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=AUX\|Person=2,3\|Polarity=Pos\|Tense=Pres`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Cnd\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Equ\|Number=Sing\|POS=ADJ\|Person=3`, `Evident=Nfh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Neg`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Acc\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Ins\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Loc\|Evident=Nfh\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Abl\|Number=Plur\|POS=ADJ\|Person=3`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=1`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Abl\|Number=Plur\|POS=VERB\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2`, `Case=Nom\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Loc\|POS=NOUN\|Polarity=Pos`, `Mood=Des\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past`, `Aspect=Imp\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres`, `Aspect=Perf\|Case=Gen\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|Tense=Past`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Rcp`, `POS=ADV\|Polarity=Pos`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Voice=Rcp`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Fut`, `Aspect=Hab\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|POS=PRON\|Person=3\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Case=Nom\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|Reflex=Yes`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Rfl`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=ADP\|Person=3\|Tense=Past`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Pass`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Mood=Imp\|Number=Sing\|POS=ADJ\|Person=2\|Polarity=Pos`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=2\|Person[psor]=1`, `Case=Acc\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Pres`, `Case=Abl\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Dat\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=2\|Person[psor]=2\|Reflex=Yes`, `Aspect=Prog\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Plur,Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Gen\|Number=Plur\|POS=ADJ\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Tense=Past`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|POS=PROPN\|Person=1,3\|Tense=Past`, `Abbr=Yes\|Case=Dat\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Past`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Plur\|POS=ADP\|Person=2`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Tense=Pres`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=1`, `Evident=Nfh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `POS=SCONJ`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Acc\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Perf\|Case=Gen\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=ADP\|Person=3`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Voice=Pass`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Acc\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `NumType=Dist\|POS=NUM`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=2\|Person[psor]=2`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PART\|Person=3\|Person[psor]=3`, `POS=ADP\|Polarity=Pos`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Case=Loc\|Number=Plur\|POS=PROPN\|Person=3`, `Case=Abl\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1,3`, `Case=Equ\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Nfh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Loc\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Perf\|Case=Loc\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=2\|Person[psor]=2\|Voice=Rfl`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Conv`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Loc,Nom\|Number=Plur,Sing\|POS=NOUN\|Person=2,3`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person=3\|Person[psor]=1`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=X\|Person=3`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Gen\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Perf\|Case=Abl\|Mood=Gen\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg`, `Aspect=Prog\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Imp\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=NUM\|Person=3`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=PROPN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Perf\|Case=Ins\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=NOUN\|Person=2,3\|Polarity=Pos`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=2`, `Aspect=Hab\|Evident=Nfh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Loc\|POS=VERB\|Polarity=Neg`, `Case=Loc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Loc\|Mood=Imp\|Number=Plur,Sing\|POS=ADJ\|Person=2,3\|Polarity=Pos`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Aspect=Prog\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1\|Tense=Past`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Aspect=Perf\|Evident=Nfh\|Mood=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past,Pres`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Evident=Nfh\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Gen,Pot\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `NumType=Card\|POS=ADJ`, `Case=Gen,Nom\|Number=Plur,Sing\|POS=PRON\|Person=1,3`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Voice=Cau`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Acc\|Mood=Gen\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=2\|Person[psor]=2`, `Case=Ins\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Hab\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Aspect=Imp\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres`, `Case=Ins\|POS=VERB\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Plur\|POS=AUX\|Person=2`, `Case=Nom\|Number=Plur\|POS=NUM\|Person=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=ADP\|Person=1\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Rfl`, `Case=Nom\|Number=Plur,Sing\|POS=ADJ\|Person=2,3`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Aspect=Imp\|Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Voice=Cau`, `Case=Equ\|Number=Plur\|POS=NUM\|Person=3`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Number=Sing\|POS=VERB\|Person=2`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Number=Sing\|POS=ADJ\|Person=1`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=ADP\|Person=1\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=X\|Person=3\|Person[psor]=1`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=1\|Person[psor]=3`, `Aspect=Perf\|Mood=Gen,Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Mood=Ind,Nec\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|Polarity=Pos\|Tense=Past`, `Mood=Nec\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Sing\|POS=ADV\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Abl\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=3`, `Aspect=Imp\|Mood=Pot\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab,Perf\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Prog\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Prog\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Polite=Infm\|Tense=Past`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=2\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Loc\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person=2`, `Case=Equ\|Number=Plur\|POS=NOUN\|Person=3`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Rfl`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Cnd\|Number=Sing\|POS=PRON\|Person=1,3\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Rfl`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Aspect=Perf\|Case=Acc\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person=2`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADP\|Person=3\|Person[psor]=2`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Imp\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut`, `Case=Nom\|POS=VERB\|Polarity=Neg\|Voice=Pass`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Abl\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Hab\|Case=Nom\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Evident=Nfh\|Mood=Gen\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past,Pres`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=ADV\|Person=3\|Tense=Past`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=1\|Person[psor]=1`, `Aspect=Imp\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Imp\|POS=VERB\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=ADJ\|Person=1,3\|Tense=Pres`, `Aspect=Imp\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=2`, `Case=Loc,Nom\|Number=Plur,Sing\|POS=PRON\|Person=1,3`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=1,3\|Tense=Past`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Abl\|Mood=Pot\|POS=VERB\|Polarity=Pos`, `Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Evident=Nfh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=3`, `Aspect=Prog\|Case=Nom\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Number=Plur\|POS=ADJ\|Person=1`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Hab,Perf\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Mood=Cnd\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `POS=X`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Mood=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Rfl`, `Case=Abl\|POS=VERB\|Polarity=Neg`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=DET\|Person=3\|Tense=Past`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=2,3\|Person[psor]=3\|Tense=Pres`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=ADP\|Person=1`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Loc\|Mood=Cnd\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Prog\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Loc,Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Nom\|Mood=Cnd\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Mood=Des\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|POS=NOUN\|Person=1,3\|Tense=Past`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Number=Plur\|POS=NOUN\|Person=1`, `Case=Nom\|Number=Plur\|POS=ADP\|Person=1`, `Aspect=Imp\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `Case=Dat\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Prog\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1\|Polarity=Neg`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person=1`, `Case=Equ\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=PRON\|Person=3\|Tense=Pres`, `Case=Nom\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Voice=Pass`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Nom\|Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Cau`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Case=Nom\|Number=Plur\|POS=ADJ\|Person=3\|Polarity=Pos`, `Number=Plur\|POS=NOUN\|Person=2`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Sing\|POS=ADP\|Person=2\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Cau`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=1,3\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Mood=Pot\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Gen,Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2`, `Case=Loc,Nom\|Number=Sing\|POS=PROPN\|Person=3`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Past`, `Case=Nom\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Voice=Cau`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Abl,Loc\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|POS=PRON\|Person=3\|Tense=Pres`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=2\|Person[psor]=2`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=1`, `Case=Loc\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|Tense=Past`, `Case=Nom\|NumType=Card\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|POS=AUX\|Person=1`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|POS=NOUN\|Person=1,3\|Tense=Pres`, `Aspect=Imp\|Mood=Pot\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Abl\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=2\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=ADP\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Abbr=Yes\|Case=Loc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=2`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=2`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg`, `Aspect=Hab,Perf\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=1\|Person[psor]=1`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past,Pres\|VerbForm=Part`, `Case=Equ\|Number=Sing\|POS=PROPN\|Person=3`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=2,3\|Tense=Past`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Case=Loc,Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=2\|Person[psor]=1`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3`, `Case=Nom\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Case=Ins\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Prog\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Equ\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Perf\|Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=1`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=3`, `Aspect=Prog\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=1\|Person[psor]=1`, `Aspect=Imp\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg`, `Number=Sing\|POS=NOUN\|Person=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `POS=ADJ\|Polarity=Neg`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Imp,Perf\|Case=Nom\|Mood=Gen,Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut,Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=PROPN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Cnd\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Nfh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Imp,Perf\|Mood=Cnd\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut,Pres`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Mood=Pot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Case=Gen\|Mood=Cnd\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2`, `Aspect=Imp\|Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Aspect=Hab\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Acc\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `aux`, `aux:q`, `case`, `cc`, `cc:preconj`, `ccomp`, `clf`, `compound`, `compound:lvc`, `compound:redup`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `flat`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `vocative`, `xcomp` | </details>
4df3796031aa0ce1abfe29aeaefa92f4
cc-by-sa-4.0
['spacy', 'token-classification']
false
Accuracy | Type | Score | | --- | --- | | `TAG_ACC` | 91.19 | | `POS_ACC` | 90.68 | | `MORPH_ACC` | 89.13 | | `LEMMA_ACC` | 82.32 | | `DEP_UAS` | 73.48 | | `DEP_LAS` | 63.73 | | `SENTS_P` | 87.17 | | `SENTS_R` | 81.92 | | `SENTS_F` | 84.47 | | `ENTS_F` | 88.90 | | `ENTS_P` | 89.54 | | `ENTS_R` | 88.28 |
2995f5a1e0330c467d51e16d7554214e
apache-2.0
['generated_from_trainer']
false
ak-vit-base-patch16-224-in21k-image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 3.1599 - Accuracy: 1.0
3c1875372521f34e9f3b2e86bbacdd51
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.191 | 0.99 | 65 | 3.1599 | 1.0 | | 2.7393 | 1.99 | 130 | 2.7834 | 1.0 | | 2.5853 | 2.99 | 195 | 2.6595 | 1.0 |
e067c1e55f56a6f686aaf3e0500dd782
apache-2.0
['stanza', 'token-classification']
false
Stanza model for Portuguese (pt) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-10-07 02:01:55.440
a9d7e15a5ba38146e04159bc5c664b6f
creativeml-openrail-m
['text-to-image']
false
Jak's **Naturitize** Image Pack (SD 1.5) for Stable Diffusion -------------------- Another Jak Texture Pack Release is here to help create your earthy, creations! Trained using 112 (768px) training images, 8000 training steps, 500 Text_Encoder_steps. Use Prompt: "**naturitize**" in the beginning of your prompt followed by a word. *No major prompt-crafting needed*. Thanks to /u/Jak_TheAI_Artist and /u/okamiueru for creating training images! Sample pictures of this concept: ![0](https://huggingface.co/plasmo/naturitize-sd1-5-768px/resolve/main/sample_images/naturitize_(1).jpg)
d71902277dcdd605fc80d3790366c06a
mit
['generated_from_trainer']
false
hungry_carson This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
44f95749d6c2e6c2325cfde6af66a71c
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - 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: 0.01 - training_steps: 2362 - mixed_precision_training: Native AMP
3d8ef462cc08b6e4c8ef4b028d22949e
mit
['generated_from_trainer']
false
Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.0}, 'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 2990407680}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 4096, 'prefix': '<|aligned|>'}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '5c64636da035c40bb8b1186648a39822071476cb'}, 'num_additional_tokens': 2, 'path_or_name': 'tomekkorbak/cranky_lichterman'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'hungry_carson', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 251, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 2990407680, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
476d3609599f29ce21b17ef1eb02f976
apache-2.0
[]
false
Mengzi-oscar-base-retrieval (Chinese Image-text retrieval model) [Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696) Mengzi-oscar-base-retrieval is fine-tuned based on Chinese multi-modal pre-training model [Mengzi-Oscar](https://github.com/Langboat/Mengzi/blob/main/Mengzi-Oscar.md), on COCO-ir dataset.
b68e7b979fe86ccaa050c643113d2552
apache-2.0
[]
false
Citation If you find the technical report or resource is useful, please cite the following technical report in your paper. ``` @misc{zhang2021mengzi, title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese}, author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou}, year={2021}, eprint={2110.06696}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
c472a0b2123f2a2626091068d8b0bca8
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: 0.2532 - F1: 0.8331
ba3b320252231303654f72c437709580
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6951 | 1.0 | 105 | 0.2967 | 0.7682 | | 0.2824 | 2.0 | 210 | 0.2569 | 0.8201 | | 0.1724 | 3.0 | 315 | 0.2532 | 0.8331 |
2a54e9574053a8c3d544538d63ff07e0