Split (1)

train · 898k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-30 00:39:23	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 526 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-30 00:39:08	card stringlengths 11 1.01M
cyeet/ppo-Pyramids	cyeet	2023-01-12T04:53:27Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-12T04:53:21Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: cyeet/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
YuJungSoo/kobigbird-pure125-74195889	YuJungSoo	2023-01-12T04:27:01Z	93	0	transformers	[ "transformers", "pytorch", "tensorboard", "big_bird", "question-answering", "generated_from_trainer", "dataset:custom_squad_v2", "endpoints_compatible", "region:us" ]	question-answering	2023-01-12T01:58:17Z	--- tags: - generated_from_trainer datasets: - custom_squad_v2 model-index: - name: kobigbird-pure125-74195889 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kobigbird-pure125-74195889 This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the custom_squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.1059 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 125 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.99 \| 42 \| 1.3347 \| \| No log \| 1.99 \| 84 \| 1.0866 \| \| No log \| 2.99 \| 126 \| 1.1059 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
huggingtweets/arvindkejriwal	huggingtweets	2023-01-12T03:34:27Z	108	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-01-12T03:23:23Z	--- language: en thumbnail: http://www.huggingtweets.com/arvindkejriwal/1673494462514/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1601815690419183616/vjuU9Ak9_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Arvind Kejriwal</div> <div style="text-align: center; font-size: 14px;">@arvindkejriwal</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Arvind Kejriwal. \| Data \| Arvind Kejriwal \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 950 \| \| Short tweets \| 33 \| \| Tweets kept \| 2261 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mi3v9ydd/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @arvindkejriwal's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/yih5jmqi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/yih5jmqi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/arvindkejriwal') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
tomekkorbak/pensive_saha	tomekkorbak	2023-01-12T03:30:46Z	2	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-01-11T14:15:13Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: pensive_saha results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pensive_saha This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 25000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<\|aligned\|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0.00056}, 'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 1661599744}, 'generation': {'force_call_on': [25354], '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': 2048, 'prefix': '<\|aligned\|>'}, {'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': 'challenging_rtp', 'num_samples': 2048, 'prefix': '<\|aligned\|>', 'prompt_before_control': True, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], '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': '81a1701e025d2c65ae6e8c2103df559071523ee0'}, 'num_additional_tokens': 2, 'path_or_name': 'tomekkorbak/goofy_pasteur'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'pensive_saha', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 2535, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1661599744, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3qk673go
hamjang/distilbert-base-uncased-finetuned-emotion	hamjang	2023-01-12T02:38:56Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-12T01:36:46Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: train args: split metrics: - name: Accuracy type: accuracy value: 0.9235 - name: F1 type: f1 value: 0.9236905799100917 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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: 0.2182 - Accuracy: 0.9235 - F1: 0.9237 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.809 \| 1.0 \| 250 \| 0.3096 \| 0.903 \| 0.9009 \| \| 0.2451 \| 2.0 \| 500 \| 0.2182 \| 0.9235 \| 0.9237 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
OubeidAllahjb/ppo-Huggy	OubeidAllahjb	2023-01-12T02:10:52Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-12T02:10:43Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: OubeidAllahjb/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
nasuka/deberta-v2-base-japanese-detect-ai	nasuka	2023-01-12T01:51:01Z	105	0	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-08T11:27:53Z	--- license: cc-by-sa-4.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: deberta-v2-base-japanese-detect-ai results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deberta-v2-base-japanese-detect-ai This model is a fine-tuned version of [ku-nlp/deberta-v2-base-japanese](https://huggingface.co/ku-nlp/deberta-v2-base-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1032 - Accuracy: 0.9842 - F1: 0.9842 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.0409 \| 1.0 \| 403 \| 0.1045 \| 0.9823 \| 0.9823 \| \| 0.0101 \| 2.0 \| 806 \| 0.0567 \| 0.9893 \| 0.9893 \| \| 0.0008 \| 3.0 \| 1209 \| 0.1032 \| 0.9842 \| 0.9842 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.0+cu113 - Datasets 2.8.0 - Tokenizers 0.13.2
ngohuudang/test_bug2	ngohuudang	2023-01-12T01:20:08Z	16	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-25T03:37:00Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer model-index: - name: test_bug2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_bug2 This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2977 - Wer: 0.1839 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1568 \| 0.27 \| 50 \| 0.2764 \| 0.1985 \| \| 0.0979 \| 0.53 \| 100 \| 0.2421 \| 0.1813 \| \| 0.1018 \| 0.8 \| 150 \| 0.2420 \| 0.1809 \| \| 0.1011 \| 1.07 \| 200 \| 0.2520 \| 0.1992 \| \| 0.0947 \| 1.34 \| 250 \| 0.2580 \| 0.1885 \| \| 0.1077 \| 1.6 \| 300 \| 0.2641 \| 0.2001 \| \| 0.109 \| 1.87 \| 350 \| 0.3196 \| 0.2156 \| \| 0.1239 \| 2.14 \| 400 \| 0.3298 \| 0.2163 \| \| 0.1286 \| 2.41 \| 450 \| 0.3392 \| 0.2436 \| \| 0.1515 \| 2.67 \| 500 \| 0.3821 \| 0.2450 \| \| 0.157 \| 2.94 \| 550 \| 0.3771 \| 0.2521 \| \| 0.1296 \| 3.21 \| 600 \| 0.3917 \| 0.2541 \| \| 0.1351 \| 3.48 \| 650 \| 0.3670 \| 0.2366 \| \| 0.1387 \| 3.74 \| 700 \| 0.3503 \| 0.2347 \| \| 0.1336 \| 4.01 \| 750 \| 0.4018 \| 0.2627 \| \| 0.114 \| 4.28 \| 800 \| 0.3699 \| 0.2723 \| \| 0.1254 \| 4.54 \| 850 \| 0.3395 \| 0.2404 \| \| 0.119 \| 4.81 \| 900 \| 0.3410 \| 0.2340 \| \| 0.1 \| 5.08 \| 950 \| 0.3302 \| 0.2216 \| \| 0.0968 \| 5.35 \| 1000 \| 0.3346 \| 0.2255 \| \| 0.0965 \| 5.61 \| 1050 \| 0.3144 \| 0.2140 \| \| 0.0906 \| 5.88 \| 1100 \| 0.3277 \| 0.2109 \| \| 0.0968 \| 6.15 \| 1150 \| 0.3300 \| 0.2141 \| \| 0.0818 \| 6.42 \| 1200 \| 0.3272 \| 0.2085 \| \| 0.0836 \| 6.68 \| 1250 \| 0.3177 \| 0.2014 \| \| 0.0803 \| 6.95 \| 1300 \| 0.3185 \| 0.2005 \| \| 0.0727 \| 7.22 \| 1350 \| 0.3110 \| 0.1928 \| \| 0.0687 \| 7.49 \| 1400 \| 0.3118 \| 0.1965 \| \| 0.0698 \| 7.75 \| 1450 \| 0.3170 \| 0.1955 \| \| 0.0651 \| 8.02 \| 1500 \| 0.3119 \| 0.1929 \| \| 0.0648 \| 8.29 \| 1550 \| 0.3058 \| 0.1904 \| \| 0.0612 \| 8.56 \| 1600 \| 0.3087 \| 0.1935 \| \| 0.0578 \| 8.82 \| 1650 \| 0.3076 \| 0.1871 \| \| 0.0557 \| 9.09 \| 1700 \| 0.3037 \| 0.1862 \| \| 0.0542 \| 9.36 \| 1750 \| 0.2990 \| 0.1858 \| \| 0.0551 \| 9.62 \| 1800 \| 0.2962 \| 0.1837 \| \| 0.0514 \| 9.89 \| 1850 \| 0.2977 \| 0.1839 \| ### Framework versions - Transformers 4.16.0 - Pytorch 1.13.1+cu116 - Datasets 1.18.3 - Tokenizers 0.12.1
thiagoms7/Reinforce1	thiagoms7	2023-01-12T00:50:30Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-01-12T00:50:21Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
rahmanfadhil/indobert-finetuned-pos	rahmanfadhil	2023-01-12T00:35:21Z	130	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:indonlu", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-11T12:40:47Z	--- license: mit tags: - generated_from_trainer datasets: - indonlu metrics: - precision - recall - f1 - accuracy model-index: - name: indobert-finetuned-pos results: - task: name: Token Classification type: token-classification dataset: name: indonlu type: indonlu config: posp split: train args: posp metrics: - name: Precision type: precision value: 0.9477284686897035 - name: Recall type: recall value: 0.9477284686897035 - name: F1 type: f1 value: 0.9477284686897035 - name: Accuracy type: accuracy value: 0.9477284686897035 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # indobert-finetuned-pos This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.1762 - Precision: 0.9477 - Recall: 0.9477 - F1: 0.9477 - Accuracy: 0.9477 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 420 \| 0.2238 \| 0.9278 \| 0.9278 \| 0.9278 \| 0.9278 \| \| 0.3621 \| 2.0 \| 840 \| 0.1806 \| 0.9437 \| 0.9437 \| 0.9437 \| 0.9437 \| \| 0.1504 \| 3.0 \| 1260 \| 0.1762 \| 0.9477 \| 0.9477 \| 0.9477 \| 0.9477 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
vtisza/PyramidsTraining	vtisza	2023-01-11T23:48:16Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-11T23:48:09Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: vtisza/PyramidsTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
OubeidAllahjb/ppo-LunarLander-v2	OubeidAllahjb	2023-01-11T23:30:41Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T23:30:20Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: POO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.03 +/- 20.56 name: mean_reward verified: false --- # POO Agent playing LunarLander-v2 This is a trained model of a POO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
SatCat/ppo-PyramidsTraining	SatCat	2023-01-11T23:29:46Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-11T23:29:39Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: SatCat/ppo-PyramidsTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
drcostco/testmodel1	drcostco	2023-01-11T23:17:04Z	15	0	diffusers	[ "diffusers", "text-to-image", "en", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-11T20:04:31Z	--- pipeline_tag: text-to-image license: openrail language: - en library_name: diffusers ---
willtai/ppo-LunarLander-v2	willtai	2023-01-11T23:07:50Z	6	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-10T19:33:14Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 261.77 +/- 17.72 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
vtisza/ppo-SnowballTarget	vtisza	2023-01-11T23:06:06Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-01-11T23:05:59Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: vtisza/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
tarolling/xlm-roberta-base-finetuned-panx-de	tarolling	2023-01-11T23:04:37Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-11T22:40:57Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8638300289723342 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de 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: 0.1358 - F1: 0.8638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2591 \| 1.0 \| 525 \| 0.1621 \| 0.8206 \| \| 0.1276 \| 2.0 \| 1050 \| 0.1379 \| 0.8486 \| \| 0.082 \| 3.0 \| 1575 \| 0.1358 \| 0.8638 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
goldman6g6/gold6g6	goldman6g6	2023-01-11T23:02:13Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2023-01-11T23:00:53Z	--- library_name: transformers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ## Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] # Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ## Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ## Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] # Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ## Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ## Training Procedure [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] # Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ## Testing Data, Factors & Metrics ### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] ### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] ### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] # Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] # Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
cleanrl/VideoPinball-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1	cleanrl	2023-01-11T22:59:47Z	0	0	cleanrl	[ "cleanrl", "tensorboard", "VideoPinball-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T22:59:43Z	--- tags: - VideoPinball-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: VideoPinball-v5 type: VideoPinball-v5 metrics: - type: mean_reward value: 434693.00 +/- 120673.56 name: mean_reward verified: false --- # (CleanRL) PPO Agent Playing VideoPinball-v5 This is a trained model of a PPO agent playing VideoPinball-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]" python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id VideoPinball-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock poetry install --all-extras python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id VideoPinball-v5 --seed 1 ``` # Hyperparameters ```python {'anneal_lr': True, 'async_batch_size': 16, 'batch_size': 2048, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'VideoPinball-v5', 'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado', 'gae': True, 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1024, 'norm_adv': True, 'num_envs': 64, 'num_minibatches': 2, 'num_steps': 32, 'num_updates': 24414, 'save_model': True, 'seed': 1, 'target_kl': None, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 2, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'envpool-atari'} ```
Rasgeath/LoRA	Rasgeath	2023-01-11T22:42:04Z	0	6	null	[ "region:us" ]	null	2023-01-07T17:45:27Z	# Tigger words : sksmalin.safetensors: sksmalin ke-ta.safetensors: ke-ta arknights.safetensors: arknights
oarga/my-first-model	oarga	2023-01-11T22:32:59Z	0	0	null	[ "dataset:gcaillaut/cora", "license:apache-2.0", "region:us" ]	null	2023-01-11T22:30:35Z	--- license: apache-2.0 datasets: - gcaillaut/cora metrics: - mse - accuracy ---
abdalrahmanshahrour/ArSUM-ai	abdalrahmanshahrour	2023-01-11T22:12:17Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "generated_from_trainer", "dataset:xlsum", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-11T15:31:22Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xlsum model-index: - name: ArSUM-ai results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ArSUM-ai This model is a fine-tuned version of [abdalrahmanshahrour/arabartsummarization](https://huggingface.co/abdalrahmanshahrour/arabartsummarization) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5951 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.3947 \| 1.0 \| 9380 \| 2.4121 \| \| 2.182 \| 2.0 \| 18760 \| 2.4303 \| \| 1.9303 \| 3.0 \| 28140 \| 2.4727 \| \| 1.7455 \| 4.0 \| 37520 \| 2.5470 \| \| 1.5804 \| 5.0 \| 46900 \| 2.5951 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
vix01mistry/ptwebsiteprogramme	vix01mistry	2023-01-11T21:44:43Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-01-11T21:42:39Z	--- license: openrail --- import openai # First, set your API key openai.api_key = "YOUR_API_KEY" # Next, define the prompt prompt = 'create a workout plan for someone who wants to lose weight' # Then, send the request to the GPT-3 API response = openai.Completion.create(engine="text-davinci-002", prompt=prompt, max_tokens=1024) # And finally, print the response print(response["choices"][0]["text"])
dfm794/Reinforce-cartpole-v1	dfm794	2023-01-11T21:36:19Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T21:36:09Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jpopham91/reinforce-Cartpole-v1	jpopham91	2023-01-11T21:28:02Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T21:27:48Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Ubermensch/form	Ubermensch	2023-01-11T21:25:52Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-01-11T21:25:52Z	--- license: cc-by-nc-sa-4.0 ---
mkahari/ppo-LunarLander-v2	mkahari	2023-01-11T21:25:01Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T21:24:37Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.07 +/- 15.38 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
odiaz1066/Reinforce-CartPole-v1	odiaz1066	2023-01-11T21:22:30Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T21:22:20Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jairtrejo/jairtrejo	jairtrejo	2023-01-11T20:58:51Z	3	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-11T20:45:13Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### jairtrejo Dreambooth model trained by jairtrejo with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars). Sample pictures of this concept: ![0](https://huggingface.co/jairtrejo/jairtrejo/resolve/main/sample_images/me_tedtalk.png) ![1](https://huggingface.co/jairtrejo/jairtrejo/resolve/main/sample_images/me_rembrandt_2.png)
henryscheible/eval_masked_v4_qnli	henryscheible	2023-01-11T20:54:15Z	0	0	null	[ "pytorch", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "region:us" ]	null	2023-01-11T15:05:47Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: eval_masked_v4_qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue args: qnli metrics: - name: Accuracy type: accuracy value: 0.8943803770821893 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eval_masked_v4_qnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4833 - Accuracy: 0.8944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
Butti93/whisper-small-hu	Butti93	2023-01-11T20:44:32Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hu", "dataset:mozilla-foundation/common_voice_11_0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-10T10:36:05Z	--- datasets: - mozilla-foundation/common_voice_11_0 language: - hu metrics: - wer pipeline_tag: automatic-speech-recognition ---
mkahari/RL_testing	mkahari	2023-01-11T20:31:06Z	4	0	transformers	[ "transformers", "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-01-10T15:53:50Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: RL_testing results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.70 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
henryscheible/eval_masked_v4_sst2	henryscheible	2023-01-11T20:17:50Z	0	0	null	[ "pytorch", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "region:us" ]	null	2023-01-11T15:05:44Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: eval_masked_v4_sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9208715596330275 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eval_masked_v4_sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3821 - Accuracy: 0.9209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
gaarsmu/ppo-Huggy	gaarsmu	2023-01-11T20:00:18Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-11T20:00:11Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: gaarsmu/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
espnet/shihlun_asr_whisper_medium_finetuned_chime4	espnet	2023-01-11T19:56:58Z	1	0	espnet	[ "espnet", "audio", "speech-recognition", "openai-whisper", "en", "dataset:chime4", "license:cc-by-4.0", "region:us" ]	null	2023-01-11T19:23:18Z	--- tags: - espnet - audio - speech-recognition - openai-whisper language: en datasets: - chime4 license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/shihlun_asr_whisper_medium_finetuned_chime4` This model was trained by Shih-Lun Wu (slseanwu) using the chime4 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd egs2/chime4/asr1 train_set=tr05_multi_noisy_si284 # tr05_multi_noisy (original training data) or tr05_multi_noisy_si284 (add si284 data) valid_set=dt05_multi_isolated_1ch_track test_sets="dt05_real_isolated_1ch_track dt05_simu_isolated_1ch_track et05_real_isolated_1ch_track et05_simu_isolated_1ch_track" asr_tag=whisper_medium_finetune_lr1e-5_adamw_wd1e-2_3epochs asr_config=conf/tuning/train_asr_whisper_full.yaml inference_config=conf/decode_asr_whisper_noctc_greedy.yaml ./asr.sh \ --skip_data_prep false \ --skip_train true \ --skip_eval false \ --lang en \ --ngpu 1 \ --nj 4 \ --stage 1 \ --stop_stage 13 \ --gpu_inference true \ --inference_nj 1 \ --token_type whisper_multilingual \ --feats_normalize '' \ --max_wav_duration 30 \ --feats_type raw \ --use_lm false \ --cleaner whisper_en \ --asr_tag "${asr_tag}" \ --asr_config "${asr_config}" \ --inference_config "${inference_config}" \ --inference_asr_model valid.acc.ave.pth \ --train_set "${train_set}" \ --valid_set "${valid_set}" \ --test_sets "${test_sets}" "$@" ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Tue Jan 10 04:15:30 CST 2023` - python version: `3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `d89be931dcc8f61437ac49cbe39a773f2054c50c` - Commit date: `Mon Jan 9 11:06:45 2023 -0600` ## asr_whisper_medium_finetune_lr1e-5_adamw_wd1e-2_3epochs ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track\|1640\|24791\|97.8\|1.7\|0.5\|0.3\|2.5\|24.5\| \|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track\|1640\|24792\|96.1\|3.0\|0.9\|0.5\|4.4\|35.6\| \|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_real_isolated_1ch_track\|1320\|19341\|96.4\|2.9\|0.7\|0.5\|4.1\|33.0\| \|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track\|1320\|19344\|93.4\|5.0\|1.7\|0.8\|7.4\|41.8\| \|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track\|1640\|24791\|97.7\|1.8\|0.5\|0.4\|2.8\|25.5\| \|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track\|1640\|24792\|96.0\|3.3\|0.8\|0.7\|4.8\|36.0\| \|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_real_isolated_1ch_track\|1320\|19341\|96.1\|3.3\|0.6\|0.7\|4.6\|34.9\| \|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track\|1320\|19344\|92.9\|5.8\|1.3\|1.2\|8.3\|43.2\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track\|1640\|141889\|99.1\|0.3\|0.5\|0.3\|1.2\|24.5\| \|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track\|1640\|141900\|98.2\|0.8\|1.0\|0.5\|2.3\|35.6\| \|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_real_isolated_1ch_track\|1320\|110558\|98.5\|0.7\|0.8\|0.5\|1.9\|33.0\| \|decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track\|1320\|110572\|96.5\|1.6\|1.9\|0.8\|4.3\|41.8\| \|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track\|1640\|141889\|99.1\|0.4\|0.5\|0.5\|1.3\|25.5\| \|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track\|1640\|141900\|98.2\|0.9\|0.9\|0.6\|2.4\|36.0\| \|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_real_isolated_1ch_track\|1320\|110558\|98.4\|0.9\|0.7\|0.6\|2.2\|34.9\| \|decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track\|1320\|110572\|96.3\|2.0\|1.7\|1.2\|4.9\|43.2\|
yuch0001/pokemon2	yuch0001	2023-01-11T19:46:09Z	8	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:lambdalabs/pokemon-blip-captions", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2023-01-11T16:05:37Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: lambdalabs/pokemon-blip-captions metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # pokemon2 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `lambdalabs/pokemon-blip-captions` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/yuch0001/pokemon2/tensorboard?#scalars)
rohitp1/libri-alpha-0.5-Temp-1-att	rohitp1	2023-01-11T19:39:05Z	105	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-11T11:57:30Z	--- tags: - generated_from_trainer model-index: - name: libri-alpha-0.5-Temp-1-att results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # libri-alpha-0.5-Temp-1-att This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 31.9525 - eval_wer: 0.0993 - eval_runtime: 165.9829 - eval_samples_per_second: 16.285 - eval_steps_per_second: 4.073 - epoch: 13.45 - step: 12000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - 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_ratio: 0.2 - num_epochs: 40 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
marccgrau/whisper-small-allSNR-v2	marccgrau	2023-01-11T19:27:23Z	79	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "sbb-asr", "generated_from_trainer", "de", "dataset:marccgrau/sbbdata_allSNR", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-11T16:52:37Z	--- language: - de license: apache-2.0 tags: - sbb-asr - generated_from_trainer datasets: - marccgrau/sbbdata_allSNR metrics: - wer model-index: - name: Whisper Small German SBB all SNR - v2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: SBB Dataset 05.01.2023 type: marccgrau/sbbdata_allSNR args: 'config: German, split: train, test, val' metrics: - name: Wer type: wer value: 1.8738110336081166 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small German SBB all SNR - v2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset. It achieves the following results on the evaluation set: - Loss: 0.7183 - Wer: 1.8738 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 64 - 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: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 4.5636 \| 0.71 \| 100 \| 2.7931 \| 1.1541 \| \| 1.4736 \| 1.42 \| 200 \| 0.8866 \| 1.0444 \| \| 0.8446 \| 2.13 \| 300 \| 0.9127 \| 1.5136 \| \| 0.7396 \| 2.84 \| 400 \| 0.7580 \| 1.2644 \| \| 0.7699 \| 3.55 \| 500 \| 0.7183 \| 1.8738 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.12.1
henryscheible/eval_masked_v4_mrpc	henryscheible	2023-01-11T19:23:35Z	0	0	null	[ "pytorch", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "region:us" ]	null	2023-01-11T15:04:14Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: eval_masked_v4_mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.7941176470588235 - name: F1 type: f1 value: 0.8595317725752508 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eval_masked_v4_mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6346 - Accuracy: 0.7941 - F1: 0.8595 - Combined Score: 0.8268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
henryscheible/eval_masked_v4_wnli	henryscheible	2023-01-11T19:20:41Z	0	0	null	[ "pytorch", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "region:us" ]	null	2023-01-11T15:05:37Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: eval_masked_v4_wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.30985915492957744 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eval_masked_v4_wnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.7105 - Accuracy: 0.3099 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
Bhaskarbha/ppo-Huggy	Bhaskarbha	2023-01-11T19:07:53Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-11T19:07:46Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: Bhaskarbha/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
tmilushev/ppo-Huggy	tmilushev	2023-01-11T18:45:03Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-11T18:44:56Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: tmilushev/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Ryukijano/q-FrozenLake-v1-4x4-noSlippery	Ryukijano	2023-01-11T18:37:25Z	0	1	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T18:37:21Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Ryukijano/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Jzuluaga/wav2vec2-xls-r-300m-en-atc-atcosim	Jzuluaga	2023-01-11T18:33:22Z	96	3	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "en-atc", "en", "generated_from_trainer", "dataset:Jzuluaga/atcosim_corpus", "arxiv:2203.16822", "arxiv:2211.04054", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-16T09:07:03Z	--- license: apache-2.0 language: en datasets: - Jzuluaga/atcosim_corpus tags: - audio - automatic-speech-recognition - en-atc - en - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-300m-en-atc-atcosim results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: Jzuluaga/atcosim_corpus name: ATCOSIM dataset (Air Traffic Control Communications) config: test split: test metrics: - type: wer value: 7.36 name: TEST WER verified: False --- # wav2vec2-xls-r-300m-en-atc-atcosim This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [ATCOSIM corpus](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus). (A better ASR model for ATC data is available here: https://huggingface.co/Jzuluaga/wav2vec2-xls-r-300m-en-atc-uwb-atcc-and-atcosim) <a href="https://colab.research.google.com/github/idiap/w2v2-air-traffic/blob/main/src/eval_xlsr_atc_model.ipynb"> <img alt="GitHub" src="https://colab.research.google.com/assets/colab-badge.svg\"> </a> <a href="https://github.com/idiap/w2v2-air-traffic"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\"> </a> It achieves the following results on the evaluation set: - Loss: 0.0988 - Wer: 0.0736 Paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). Authors: Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Saeed Sarfjoo, Petr Motlicek, Matthias Kleinert, Hartmut Helmke, Oliver Ohneiser, Qingran Zhan Abstract: Recent work on self-supervised pre-training focus</b> on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E)acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data properties substantially differ between the pre-training and fine-tuning phases, termed domain shift. We target this scenario by analyzing the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a completely unseen domain, air traffic control (ATC) communications. We benchmark these two models on several open-source and challenging ATC databases with signal-to-noise ratio between 5 and 20 dB. Relative word error rate (WER) reductions between 20% to 40% are obtained in comparison to hybrid-based ASR baselines by only fine-tuning E2E acoustic models with a smaller fraction of labeled data. We analyze WERs on the low-resource scenario and gender bias carried by one ATC dataset. Code — GitHub repository: https://github.com/idiap/w2v2-air-traffic ## Usage You can use our Google Colab notebook to run and evaluate our model: https://github.com/idiap/w2v2-air-traffic/blob/master/src/eval_xlsr_atc_model.ipynb ## Intended uses & limitations This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets, e.g., LibriSpeech or CommonVoice. ## Training and evaluation data See Table 1 (page 3) in our paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). We described there the partitions of how to use our model. - We use the ATCOSIM dataset for fine-tuning this model. You can download the raw data here: https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html - However, do not worry, we have prepared the database in `Datasets format`. Here, [ATCOSIM CORPUS on HuggingFace](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus). You can scroll and check the train/test partitions, and even listen to some audios. - If you want to prepare a database in HuggingFace format, you can follow the data loader script in: [data_loader_atc.py](https://huggingface.co/datasets/Jzuluaga/atcosim_corpus/blob/main/atc_data_loader.py). ## Writing your own inference script If you use language model, you need to install the KenLM bindings with: ```bash conda activate your_environment pip install https://github.com/kpu/kenlm/archive/master.zip ``` The snippet of code: ```python from datasets import load_dataset, load_metric, Audio import torch from transformers import AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM import torchaudio.functional as F USE_LM = False DATASET_ID = "Jzuluaga/atcosim_corpus" MODEL_ID = "Jzuluaga/wav2vec2-xls-r-300m-en-atc-atcosim" # 1. Load the dataset # we only load the 'test' partition, however, if you want to load the 'train' partition, you can change it accordingly atcosim_corpus_test = load_dataset(DATASET_ID, "test", split="test") # 2. Load the model model = AutoModelForCTC.from_pretrained(MODEL_ID) # 3. Load the processors, we offer support with LM, which should yield better resutls if USE_LM: processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) else: processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) # 4. Format the test sample sample = next(iter(atcosim_corpus_test)) file_sampling_rate = sample['audio']['sampling_rate'] # resample if neccessary if file_sampling_rate != 16000: resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), file_sampling_rate, 16000).numpy() else: resampled_audio = torch.tensor(sample["audio"]["array"]).numpy() input_values = processor(resampled_audio, return_tensors="pt").input_values # 5. Run the forward pass in the model with torch.no_grad(): logits = model(input_values).logits # get the transcription with processor if USE_LM: transcription = processor.batch_decode(logits.numpy()).text else: pred_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(pred_ids) # print the output print(transcription) ``` # Cite us If you use this code for your research, please cite our paper with: ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 1.9105 \| 6.41 \| 500 \| 0.1622 \| 0.1531 \| \| 0.1119 \| 12.82 \| 1000 \| 0.0971 \| 0.0936 \| \| 0.0614 \| 19.23 \| 1500 \| 0.1002 \| 0.0983 \| \| 0.044 \| 25.64 \| 2000 \| 0.1011 \| 0.0929 \| \| 0.0366 \| 32.05 \| 2500 \| 0.0932 \| 0.0828 \| \| 0.0315 \| 38.46 \| 3000 \| 0.0926 \| 0.0880 \| \| 0.0297 \| 44.87 \| 3500 \| 0.0972 \| 0.0882 \| \| 0.0216 \| 51.28 \| 4000 \| 0.0911 \| 0.0774 \| \| 0.0211 \| 57.69 \| 4500 \| 0.0982 \| 0.0891 \| \| 0.0187 \| 64.1 \| 5000 \| 0.1009 \| 0.0863 \| \| 0.02 \| 70.51 \| 5500 \| 0.0953 \| 0.0852 \| \| 0.0163 \| 76.92 \| 6000 \| 0.1028 \| 0.0804 \| \| 0.0128 \| 83.33 \| 6500 \| 0.0930 \| 0.0856 \| \| 0.0127 \| 89.74 \| 7000 \| 0.0892 \| 0.0676 \| \| 0.0116 \| 96.15 \| 7500 \| 0.0857 \| 0.0753 \| \| 0.0139 \| 102.56 \| 8000 \| 0.1078 \| 0.0481 \| \| 0.0107 \| 108.97 \| 8500 \| 0.0955 \| 0.0683 \| \| 0.0096 \| 115.38 \| 9000 \| 0.0846 \| 0.0697 \| \| 0.0089 \| 121.79 \| 9500 \| 0.0854 \| 0.0675 \| \| 0.0084 \| 128.21 \| 10000 \| 0.0875 \| 0.0779 \| \| 0.0074 \| 134.62 \| 10500 \| 0.0840 \| 0.0770 \| \| 0.0061 \| 141.03 \| 11000 \| 0.0903 \| 0.0754 \| \| 0.0076 \| 147.44 \| 11500 \| 0.0872 \| 0.0769 \| \| 0.0069 \| 153.85 \| 12000 \| 0.0891 \| 0.0772 \| \| 0.0061 \| 160.26 \| 12500 \| 0.0971 \| 0.0774 \| \| 0.0049 \| 166.67 \| 13000 \| 0.0984 \| 0.0726 \| \| 0.0045 \| 173.08 \| 13500 \| 0.0952 \| 0.0765 \| \| 0.0039 \| 179.49 \| 14000 \| 0.1015 \| 0.0762 \| \| 0.0031 \| 185.9 \| 14500 \| 0.0937 \| 0.0712 \| \| 0.0032 \| 192.31 \| 15000 \| 0.0982 \| 0.0635 \| \| 0.0028 \| 198.72 \| 15500 \| 0.0981 \| 0.0743 \| \| 0.0024 \| 205.13 \| 16000 \| 0.1019 \| 0.0712 \| \| 0.0024 \| 211.54 \| 16500 \| 0.0957 \| 0.0732 \| \| 0.002 \| 217.95 \| 17000 \| 0.0941 \| 0.0732 \| \| 0.0015 \| 224.36 \| 17500 \| 0.1009 \| 0.0717 \| \| 0.0017 \| 230.77 \| 18000 \| 0.0955 \| 0.0730 \| \| 0.0013 \| 237.18 \| 18500 \| 0.0989 \| 0.0732 \| \| 0.0013 \| 243.59 \| 19000 \| 0.0967 \| 0.0738 \| \| 0.0011 \| 250.0 \| 19500 \| 0.0980 \| 0.0734 \| \| 0.0008 \| 256.41 \| 20000 \| 0.0988 \| 0.0736 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2
LaraTomeh/roberta-base-bne-finetuned-amazon_reviews_multi	LaraTomeh	2023-01-11T18:11:14Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-11T17:48:56Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: roberta-base-bne-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: train args: es metrics: - name: Accuracy type: accuracy value: 0.93325 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2247 - Accuracy: 0.9333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1943 \| 1.0 \| 1250 \| 0.1750 \| 0.932 \| \| 0.1023 \| 2.0 \| 2500 \| 0.2247 \| 0.9333 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
samueldomdey/arousal-english-distilroberta-base	samueldomdey	2023-01-11T18:08:01Z	109	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-11T15:17:52Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: arousal-english-distilroberta-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # arousal-english-distilroberta-base This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0198 - Rmse: 0.1408 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Tokenizers 0.13.2
cleanrl/Venture-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1	cleanrl	2023-01-11T18:03:03Z	0	0	cleanrl	[ "cleanrl", "tensorboard", "Venture-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T18:02:59Z	--- tags: - Venture-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Venture-v5 type: Venture-v5 metrics: - type: mean_reward value: 0.00 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) PPO Agent Playing Venture-v5 This is a trained model of a PPO agent playing Venture-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]" python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id Venture-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/cleanrl/Venture-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py curl -OL https://huggingface.co/cleanrl/Venture-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Venture-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock poetry install --all-extras python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id Venture-v5 --seed 1 ``` # Hyperparameters ```python {'anneal_lr': True, 'async_batch_size': 16, 'batch_size': 2048, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Venture-v5', 'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado', 'gae': True, 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1024, 'norm_adv': True, 'num_envs': 64, 'num_minibatches': 2, 'num_steps': 32, 'num_updates': 24414, 'save_model': True, 'seed': 1, 'target_kl': None, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 2, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'envpool-atari'} ```
DiegoPolimeni/xlm-roberta-base-finetuned-panx-de	DiegoPolimeni	2023-01-11T17:56:22Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-11T17:25:02Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: train args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8638300289723342 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de 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: 0.1358 - F1: 0.8638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2591 \| 1.0 \| 525 \| 0.1621 \| 0.8206 \| \| 0.1276 \| 2.0 \| 1050 \| 0.1379 \| 0.8486 \| \| 0.082 \| 3.0 \| 1575 \| 0.1358 \| 0.8638 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
ahmad-alismail/ppo-Huggy	ahmad-alismail	2023-01-11T17:24:52Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-11T17:24:44Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: ahmad1289/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
alaaadr/dropboxLinking	alaaadr	2023-01-11T17:20:51Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-01-11T17:20:51Z	--- license: bigscience-openrail-m ---
Arch4ngel/SnowballTarget1	Arch4ngel	2023-01-11T17:19:53Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-01-11T17:19:48Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: Arch4ngel/SnowballTarget1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
bjpsantos/ppo-Huggy	bjpsantos	2023-01-11T17:13:24Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-11T17:13:16Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: bjpsantos/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
hasanalay/t5-base-news-summary-generation	hasanalay	2023-01-11T16:55:31Z	103	0	transformers	[ "transformers", "pytorch", "tf", "jax", "t5", "text2text-generation", "generated_from_keras_callback", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-11T16:54:22Z	--- tags: - generated_from_keras_callback model-index: - name: t5-base-news-summary-generation results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-news-summary-generation This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.8.0 - Tokenizers 0.13.2
gabella/bert-emotion	gabella	2023-01-11T16:51:12Z	118	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-05T19:41:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - precision - recall model-index: - name: bert-emotion results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: emotion split: train args: emotion metrics: - name: Precision type: precision value: 0.7350080900694398 - name: Recall type: recall value: 0.7334480130231172 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-emotion This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.1951 - Precision: 0.7350 - Recall: 0.7334 - Fscore: 0.7341 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| Fscore \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.8468 \| 1.0 \| 815 \| 0.7465 \| 0.7116 \| 0.6096 \| 0.6325 \| \| 0.5105 \| 2.0 \| 1630 \| 0.9035 \| 0.7532 \| 0.7111 \| 0.7276 \| \| 0.2492 \| 3.0 \| 2445 \| 1.1951 \| 0.7350 \| 0.7334 \| 0.7341 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
marccgrau/whisper-small-allSNR	marccgrau	2023-01-11T16:49:22Z	6	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "sbb-asr", "generated_from_trainer", "de", "dataset:marccgrau/sbbdata_allSNR", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-14T16:25:44Z	--- language: - de license: apache-2.0 tags: - sbb-asr - generated_from_trainer datasets: - marccgrau/sbbdata_allSNR metrics: - wer model-index: - name: Whisper Small German SBB all SNR results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: SBB Dataset 05.01.2023 type: marccgrau/sbbdata_allSNR args: 'config: German, split: train, test, val' metrics: - name: Wer type: wer value: 0.02092580849714648 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small German SBB all SNR This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset. It achieves the following results on the evaluation set: - Loss: 0.0274 - Wer: 0.0209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 100 - training_steps: 800 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.3449 \| 0.36 \| 100 \| 0.2161 \| 0.0380 \| \| 0.0653 \| 0.71 \| 200 \| 0.0265 \| 0.0178 \| \| 0.0315 \| 1.07 \| 300 \| 0.0291 \| 0.0197 \| \| 0.0194 \| 1.42 \| 400 \| 0.0273 \| 0.0197 \| \| 0.0141 \| 1.78 \| 500 \| 0.0278 \| 0.0197 \| \| 0.0088 \| 2.14 \| 600 \| 0.0265 \| 0.0159 \| \| 0.0055 \| 2.49 \| 700 \| 0.0273 \| 0.0209 \| \| 0.0047 \| 2.85 \| 800 \| 0.0274 \| 0.0209 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.12.1
CoreyMorris/ppo-ML-Agents-Pyramids	CoreyMorris	2023-01-11T16:46:44Z	10	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-11T16:46:38Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: CoreyMorris/ppo-ML-Agents-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
yachty66/bert-base-cased-ner-jnlpba-strong-labelled	yachty66	2023-01-11T16:36:17Z	115	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-11T16:33:34Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-ner-jnlpba-strong-labelled results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-ner-jnlpba-strong-labelled This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 1.7948 - eval_accuracy: 0.1970 - eval_runtime: 49.3802 - eval_samples_per_second: 156.217 - eval_steps_per_second: 9.781 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.8.0 - Tokenizers 0.13.2
saikiranp/ppo-PyramidsRND	saikiranp	2023-01-11T16:31:42Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-11T16:31:36Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: saikiranp/ppo-PyramidsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
mikegarts/dqn-SpaceInvadersNoFrameskip-v4	mikegarts	2023-01-11T16:23:13Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T16:22:32Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 647.00 +/- 188.48 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mikegarts -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mikegarts -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mikegarts ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
ahmad-alismail/ppo-LunarLander-v2	ahmad-alismail	2023-01-11T16:22:40Z	7	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T14:07:52Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 274.81 +/- 20.36 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from huggingface_hub import notebook_login from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.env_util import make_vec_env # Create the environment env = make_vec_env('LunarLander-v2', n_envs=16) model = PPO( policy = 'MlpPolicy', # The policy to be optimized env = env, # The environment in which the agent will act n_steps = 2048, # The number of steps to run for each environment per update batch_size = 64, # Minibatch size n_epochs = 10, # Number of epoch when optimizing the surrogate loss gamma = 0.999, # discount factor used to weigh future rewards in the total reward calculation gae_lambda = 0.98, # parameter used in the Generalized Advantage Estimation (GAE) algorithm ent_coef = 0.01, # Entropy coefficient for the loss calculation verbose=0) # Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for debug messages # Train it for 1,500,000 timesteps model.learn(total_timesteps=1500000, progress_bar=True) # Specify file name for model and save the model to file model_name = "ppo-LunarLander-v2" model.save(model_name) # Create a new environment for evaluation eval_env = gym.make("LunarLander-v2") # Evaluate the model with 10 evaluation episodes and deterministic=True mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) # Print the results print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") ```
DanGalt/ppo-Huggy	DanGalt	2023-01-11T16:22:40Z	13	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-11T16:22:31Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: DanGalt/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
nabdan/sd-class-butterflies-32	nabdan	2023-01-11T16:18:00Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-01-11T16:16:11Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('nabdan/sd-class-butterflies-32') image = pipeline().images[0] image ```
davidnai/DAVIDNAI-T5-HUG-93520798	davidnai	2023-01-11T16:12:19Z	61	0	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-11T16:11:57Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: DAVIDNAI-T5-HUG-93520798 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # DAVIDNAI-T5-HUG-93520798 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9216 - Validation Loss: 2.5848 - Train Rougel: tf.Tensor(0.20213354, shape=(), dtype=float32) - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Rougel \| Epoch \| \|:----------:\|:---------------:\|:----------------------------------------------:\|:-----:\| \| 2.9216 \| 2.5848 \| tf.Tensor(0.20213354, shape=(), dtype=float32) \| 0 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.8.0 - Tokenizers 0.13.2
Jbot/q-FrozenLake-v1-4x4-noSlippery	Jbot	2023-01-11T15:42:23Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T15:42:17Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Jbot/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
DanGalt/ppo-PyramidsTraining	DanGalt	2023-01-11T15:30:58Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-11T15:30:51Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: DanGalt/ppo-PyramidsTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Fake-Person/photoreal	Fake-Person	2023-01-11T15:25:46Z	0	0	null	[ "region:us" ]	null	2022-12-10T02:54:57Z	I was told this came from that from unstable diffusion discord
snu-nia-12/wav2vec2-xls-r-300m_nia12_phone-hiragana_japanese	snu-nia-12	2023-01-11T15:19:42Z	78	1	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "ja", "dataset:common_voice", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-11T14:58:40Z	--- language: ja datasets: - common_voice metrics: - cer model-index: - name: wav2vec2-xls-r-300m finetuned on Japanese Hiragana with no word boundaries results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice Japanese type: common_voice args: ja metrics: - name: Test CER type: cer value: 9.34 --- # Wav2Vec2-XLS-R-300M-Japanese-Hiragana Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Japanese Hiragana characters using JSUT, JVS, Common Voice, and in-house dataset. The sentence outputs do not contain word boundaries. Audio inputs should be sampled at 16kHz. ## Test Results CER: 9.34% ## Training Trained on JSUT, a subset of JVS, train+valid set of Common Voice Japanese, and in-house Japanese dataset. Tested on test set of Common Voice Japanese.
mrojas/spanish-disease-tagger	mrojas	2023-01-11T15:09:28Z	112	1	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "dataset:disease", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-11T14:46:56Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - disease metrics: - precision - recall - f1 - accuracy model-index: - name: spanish-disease-tagger results: - task: name: Token Classification type: token-classification dataset: name: disease type: disease config: disease split: train args: disease metrics: - name: Precision type: precision value: 0.8385373870172556 - name: Recall type: recall value: 0.8711054204011951 - name: F1 type: f1 value: 0.8545111994975926 - name: Accuracy type: accuracy value: 0.9487721041951381 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # spanish-disease-tagger This model is a fine-tuned version of [plncmm/roberta-clinical-wl-es](https://huggingface.co/plncmm/roberta-clinical-wl-es) on the disease dataset. It achieves the following results on the evaluation set: - Loss: 0.1786 - Precision: 0.8385 - Recall: 0.8711 - F1: 0.8545 - Accuracy: 0.9488 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2217 \| 1.0 \| 502 \| 0.1698 \| 0.8142 \| 0.8587 \| 0.8359 \| 0.9437 \| \| 0.1203 \| 2.0 \| 1004 \| 0.1735 \| 0.8513 \| 0.8528 \| 0.8520 \| 0.9473 \| \| 0.093 \| 3.0 \| 1506 \| 0.1786 \| 0.8385 \| 0.8711 \| 0.8545 \| 0.9488 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
charlemagne/distilbert-base-uncased-final2-mnli	charlemagne	2023-01-11T15:05:59Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-11T13:35:26Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-final2-mnli results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-final2-mnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1172 - Accuracy: 0.9714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 75 \| 0.5516 \| 0.8627 \| \| No log \| 2.0 \| 150 \| 0.2118 \| 0.9553 \| \| No log \| 3.0 \| 225 \| 0.1482 \| 0.9671 \| \| No log \| 4.0 \| 300 \| 0.1172 \| 0.9714 \| \| No log \| 5.0 \| 375 \| 0.1240 \| 0.9705 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.8.0+cu111 - Datasets 2.1.0 - Tokenizers 0.11.6
ayor-dns/q-Taxi-v3	ayor-dns	2023-01-11T15:01:03Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T14:49:41Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="ayor-dns/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
yassin99/rubertForRussian	yassin99	2023-01-11T14:56:00Z	105	0	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "feature-extraction", "ru", "arxiv:1905.07213", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-01-11T13:44:47Z	--- language: - ru --- # rubert-base-cased RuBERT $Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters$ was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\]. 08.11.2021: upload model with MLM and NSP heads \[1\]: Kuratov, Y., Arkhipov, M. $2019$. Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213).
Fake-Person/ti_hevan	Fake-Person	2023-01-11T14:33:06Z	0	1	null	[ "region:us" ]	null	2022-12-06T14:21:38Z	all the TI's i have I have no idea where they come from or what they do
Closen/dqn-SpaceInvadersNoFrameskip-v4	Closen	2023-01-11T14:28:07Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T14:27:24Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 832.50 +/- 329.28 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Closen -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Closen -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Closen ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
slushily/ohana-hannah-128	slushily	2023-01-11T14:18:53Z	7	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2023-01-11T06:14:33Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ohana-hannah-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 7 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/slushily/ohana-hannah-128/tensorboard?#scalars)
kardosdrur/grc_homercy_treebanks_sm	kardosdrur	2023-01-11T14:16:15Z	6	0	spacy	[ "spacy", "token-classification", "grc", "model-index", "region:us" ]	token-classification	2023-01-10T15:54:47Z	--- tags: - spacy - token-classification language: - grc model-index: - name: grc_homercy_treebanks_sm results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.0 - name: NER Recall type: recall value: 0.0 - name: NER F Score type: f_score value: 0.0 - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.6590702552 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.8626354422 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.7597941892 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.7814819474 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.6299106743 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.541285774 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.6277657267 --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `grc_homercy_treebanks_sm` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.4.4,<3.5.0` \| \| Default Pipeline \| `tok2vec`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `entity_ruler` \| \| Components \| `tok2vec`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `entity_ruler` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (2302 labels for 4 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `tagger` \| `---------`, `--p---fa-`, `--s---ma-`, `-3paia---`, `-3paim---`, `-3siia---`, `A-`, `C-`, `Df`, `Dq`, `Du`, `F-`, `G-`, `I-`, `Ma`, `Mo`, `Nb`, `Ne`, `Pc`, `Pd`, `Pi`, `Pk`, `Pp`, `Pr`, `Ps`, `Px`, `R-`, `S-`, `V-`, `a--------`, `a-------s`, `a-d---fa-`, `a-d---fd-`, `a-d---fg-`, `a-d---fn-`, `a-d---ma-`, `a-d---md-`, `a-d---mg-`, `a-d---mn-`, `a-d---mnc`, `a-d---mv-`, `a-d---na-`, `a-d---ng-`, `a-d---nn-`, `a-p----dc`, `a-p---fa-`, `a-p---fac`, `a-p---fas`, `a-p---fd-`, `a-p---fdc`, `a-p---fds`, `a-p---fg-`, `a-p---fgc`, `a-p---fn-`, `a-p---fnc`, `a-p---fns`, `a-p---fv-`, `a-p---m--`, `a-p---m-c`, `a-p---ma-`, `a-p---mac`, `a-p---mas`, `a-p---md-`, `a-p---mdc`, `a-p---mds`, `a-p---mg-`, `a-p---mgc`, `a-p---mgs`, `a-p---mn-`, `a-p---mnc`, `a-p---mns`, `a-p---mv-`, `a-p---mvs`, `a-p---na-`, `a-p---nac`, `a-p---nas`, `a-p---nd-`, `a-p---ndc`, `a-p---nds`, `a-p---ng-`, `a-p---ngs`, `a-p---nn-`, `a-p---nnc`, `a-p---nns`, `a-p---nv-`, `a-s----d-`, `a-s----dc`, `a-s----g-`, `a-s----gc`, `a-s---fa-`, `a-s---fac`, `a-s---fas`, `a-s---fd-`, `a-s---fds`, `a-s---fg-`, `a-s---fgc`, `a-s---fgs`, `a-s---fn-`, `a-s---fnc`, `a-s---fns`, `a-s---fv-`, `a-s---m--`, `a-s---ma-`, `a-s---mac`, `a-s---mas`, `a-s---md-`, `a-s---mdc`, `a-s---mds`, `a-s---mg-`, `a-s---mgc`, `a-s---mgs`, `a-s---mn-`, `a-s---mnc`, `a-s---mns`, `a-s---mv-`, `a-s---mvc`, `a-s---mvs`, `a-s---na-`, `a-s---nac`, `a-s---nas`, `a-s---nd-`, `a-s---ndc`, `a-s---nds`, `a-s---ng-`, `a-s---nn-`, `a-s---nnc`, `a-s---nns`, `a-s---nv-`, `a-s---nvs`, `c--------`, `d--------`, `d-------c`, `d-------s`, `g--------`, `i--------`, `l--------`, `l-d---fa-`, `l-d---fg-`, `l-d---mg-`, `l-d---mn-`, `l-d---na-`, `l-d---nn-`, `l-p---fa-`, `l-p---fd-`, `l-p---fg-`, `l-p---fn-`, `l-p---ma-`, `l-p---md-`, `l-p---mg-`, `l-p---mn-`, `l-p---na-`, `l-p---nd-`, `l-p---ng-`, `l-p---nn-`, `l-s---fa-`, `l-s---fd-`, `l-s---fg-`, `l-s---fn-`, `l-s---ma-`, `l-s---md-`, `l-s---mg-`, `l-s---mn-`, `l-s---na-`, `l-s---nd-`, `l-s---ng-`, `l-s---nn-`, `m--------`, `m-p---m--`, `m-p---md-`, `m-p---nn-`, `n-----fg-`, `n-----na-`, `n-----nn-`, `n-d----a-`, `n-d---fa-`, `n-d---fd-`, `n-d---fg-`, `n-d---fn-`, `n-d---ma-`, `n-d---md-`, `n-d---mg-`, `n-d---mn-`, `n-d---mv-`, `n-d---na-`, `n-d---nn-`, `n-p----d-`, `n-p----g-`, `n-p---fa-`, `n-p---fd-`, `n-p---fg-`, `n-p---fn-`, `n-p---fv-`, `n-p---ma-`, `n-p---md-`, `n-p---mg-`, `n-p---mn-`, `n-p---mv-`, `n-p---na-`, `n-p---nd-`, `n-p---ng-`, `n-p---nn-`, `n-p---nv-`, `n-s----d-`, `n-s----g-`, `n-s----n-`, `n-s----v-`, `n-s---fa-`, `n-s---fd-`, `n-s---fg-`, `n-s---fn-`, `n-s---fv-`, `n-s---m--`, `n-s---ma-`, `n-s---md-`, `n-s---mg-`, `n-s---mn-`, `n-s---mv-`, `n-s---na-`, `n-s---nd-`, `n-s---ng-`, `n-s---nn-`, `n-s---nv-`, `p--------`, `p-d----d-`, `p-d----n-`, `p-d---fa-`, `p-d---fd-`, `p-d---fg-`, `p-d---fn-`, `p-d---ma-`, `p-d---md-`, `p-d---mg-`, `p-d---mn-`, `p-d---mv-`, `p-p----a-`, `p-p----d-`, `p-p----g-`, `p-p----n-`, `p-p---fa-`, `p-p---fd-`, `p-p---fg-`, `p-p---fn-`, `p-p---ma-`, `p-p---md-`, `p-p---mg-`, `p-p---mn-`, `p-p---na-`, `p-p---nd-`, `p-p---ng-`, `p-p---nn-`, `p-s----a-`, `p-s----d-`, `p-s----g-`, `p-s----n-`, `p-s---fa-`, `p-s---fd-`, `p-s---fg-`, `p-s---fn-`, `p-s---ma-`, `p-s---md-`, `p-s---mg-`, `p-s---mn-`, `p-s---mv-`, `p-s---na-`, `p-s---nd-`, `p-s---ng-`, `p-s---nn-`, `p1p---fa-`, `p1p---ma-`, `p1p---md-`, `p1p---mg-`, `p1p---mn-`, `p1s---fa-`, `p1s---fd-`, `p1s---fg-`, `p1s---fn-`, `p1s---ma-`, `p1s---md-`, `p1s---mg-`, `p1s---mn-`, `p2p----a-`, `p2p----d-`, `p2p---ma-`, `p2p---mg-`, `p2p---mn-`, `p2s----a-`, `p2s----d-`, `p2s----g-`, `p2s----n-`, `p2s---ma-`, `p2s---md-`, `p2s---mg-`, `p3s---fa-`, `p3s---ma-`, `r--------`, `u--------`, `v---na---`, `v--amm---`, `v--an----`, `v--ana---`, `v--ane---`, `v--anm---`, `v--anp---`, `v--fna---`, `v--fne---`, `v--fnm---`, `v--fnp---`, `v--pna---`, `v--pnd---`, `v--pne---`, `v--pnp---`, `v--ppefa-`, `v--ppemn-`, `v--rn----`, `v--rna---`, `v--rne---`, `v--rnp---`, `v--tna---`, `v-dapafn-`, `v-dapama-`, `v-dapamg-`, `v-dapamn-`, `v-dapmfn-`, `v-dapmmn-`, `v-dappma-`, `v-dappmn-`, `v-dppafg-`, `v-dppama-`, `v-dppamn-`, `v-dppefn-`, `v-dppema-`, `v-dppemd-`, `v-dppemn-`, `v-dpppmn-`, `v-drpama-`, `v-drpamn-`, `v-drpefn-`, `v-drpemn-`, `v-p-pmma-`, `v-pap-mn-`, `v-papafa-`, `v-papafg-`, `v-papafn-`, `v-papama-`, `v-papamd-`, `v-papamg-`, `v-papamn-`, `v-papana-`, `v-papand-`, `v-papann-`, `v-papefn-`, `v-papema-`, `v-papemn-`, `v-papmfa-`, `v-papmfg-`, `v-papmfn-`, `v-papmma-`, `v-papmmd-`, `v-papmmg-`, `v-papmmn-`, `v-papmna-`, `v-papmng-`, `v-papmnn-`, `v-pappfd-`, `v-pappfg-`, `v-pappfn-`, `v-pappma-`, `v-pappmd-`, `v-pappmg-`, `v-pappmn-`, `v-pappna-`, `v-pappng-`, `v-pappnn-`, `v-pfpama-`, `v-pfpamg-`, `v-pfpamn-`, `v-pfpema-`, `v-pfpemn-`, `v-pfpmfa-`, `v-pfpmfn-`, `v-pfpmma-`, `v-pfpmmd-`, `v-pfpmmg-`, `v-pfpmmn-`, `v-pfpmnn-`, `v-pfppmn-`, `v-ppp-mn-`, `v-pppafa-`, `v-pppafd-`, `v-pppafg-`, `v-pppafn-`, `v-pppafv-`, `v-pppama-`, `v-pppamd-`, `v-pppamg-`, `v-pppamn-`, `v-pppamv-`, `v-pppana-`, `v-pppand-`, `v-pppang-`, `v-pppann-`, `v-pppefa-`, `v-pppefd-`, `v-pppefg-`, `v-pppefn-`, `v-pppefv-`, `v-pppema-`, `v-pppemd-`, `v-pppemg-`, `v-pppemn-`, `v-pppemv-`, `v-pppena-`, `v-pppend-`, `v-pppeng-`, `v-pppenn-`, `v-ppppma-`, `v-ppppmd-`, `v-ppppmn-`, `v-prp-mn-`, `v-prpafa-`, `v-prpafd-`, `v-prpafn-`, `v-prpama-`, `v-prpamd-`, `v-prpamg-`, `v-prpamn-`, `v-prpana-`, `v-prpang-`, `v-prpefa-`, `v-prpefd-`, `v-prpefg-`, `v-prpefn-`, `v-prpema-`, `v-prpemd-`, `v-prpemg-`, `v-prpemn-`, `v-prpena-`, `v-prpend-`, `v-prpeng-`, `v-prpenn-`, `v-prppfn-`, `v-prppmn-`, `v-sagamn-`, `v-saiamn-`, `v-samp---`, `v-sap-mg-`, `v-sap-mn-`, `v-sapafa-`, `v-sapafd-`, `v-sapafg-`, `v-sapafn-`, `v-sapama-`, `v-sapamd-`, `v-sapamg-`, `v-sapamn-`, `v-sapamv-`, `v-sapana-`, `v-sapang-`, `v-sapann-`, `v-sapanv-`, `v-sapema-`, `v-sapemn-`, `v-sapmfa-`, `v-sapmfd-`, `v-sapmfg-`, `v-sapmfn-`, `v-sapmma-`, `v-sapmmd-`, `v-sapmmg-`, `v-sapmmn-`, `v-sapmna-`, `v-sapmng-`, `v-sapmnn-`, `v-sappfa-`, `v-sappfd-`, `v-sappfg-`, `v-sappfn-`, `v-sappma-`, `v-sappmd-`, `v-sappmg-`, `v-sappmn-`, `v-sappna-`, `v-sappng-`, `v-sappnn-`, `v-sappnv-`, `v-sfpafa-`, `v-sfpafd-`, `v-sfpafn-`, `v-sfpama-`, `v-sfpamd-`, `v-sfpamg-`, `v-sfpamn-`, `v-sfpmfa-`, `v-sfpmfd-`, `v-sfpmfg-`, `v-sfpmfn-`, `v-sfpmma-`, `v-sfpmmg-`, `v-sfpmmn-`, `v-sfpmna-`, `v-sfppma-`, `v-spiamn-`, `v-spp-mn-`, `v-spp-nn-`, `v-sppa---`, `v-sppafa-`, `v-sppafd-`, `v-sppafg-`, `v-sppafn-`, `v-sppafv-`, `v-sppama-`, `v-sppamd-`, `v-sppamg-`, `v-sppamn-`, `v-sppamv-`, `v-sppana-`, `v-sppand-`, `v-sppang-`, `v-sppann-`, `v-sppanv-`, `v-sppefa-`, `v-sppefd-`, `v-sppefg-`, `v-sppefn-`, `v-sppema-`, `v-sppemd-`, `v-sppemg-`, `v-sppemn-`, `v-sppemv-`, `v-sppena-`, `v-sppend-`, `v-sppeng-`, `v-sppenn-`, `v-spppfa-`, `v-spppfd-`, `v-spppfg-`, `v-spppfn-`, `v-spppma-`, `v-spppmn-`, `v-srp-mn-`, `v-srpafa-`, `v-srpafd-`, `v-srpafg-`, `v-srpafn-`, `v-srpama-`, `v-srpamd-`, `v-srpamg-`, `v-srpamn-`, `v-srpamv-`, `v-srpana-`, `v-srpand-`, `v-srpang-`, `v-srpann-`, `v-srpefa-`, `v-srpefd-`, `v-srpefg-`, `v-srpefn-`, `v-srpema-`, `v-srpemd-`, `v-srpemg-`, `v-srpemn-`, `v-srpemv-`, `v-srpena-`, `v-srpend-`, `v-srpeng-`, `v-srpenn-`, `v-srppfn-`, `v-srppma-`, `v-srppmn-`, `v-srppmv-`, `v1paia---`, `v1paim---`, `v1paip---`, `v1paoa---`, `v1paom---`, `v1paop---`, `v1pasa---`, `v1pase---`, `v1pasm---`, `v1pasp---`, `v1pfia---`, `v1pfim---`, `v1pfom---`, `v1piia---`, `v1piie---`, `v1plia---`, `v1plie---`, `v1ppia---`, `v1ppie---`, `v1ppip---`, `v1ppoa---`, `v1ppoe---`, `v1ppsa---`, `v1ppse---`, `v1pria---`, `v1prie---`, `v1prsa---`, `v1prse---`, `v1ptie---`, `v1s-sa---`, `v1sa-a---`, `v1saia---`, `v1saie---`, `v1saim---`, `v1saip---`, `v1sao----`, `v1saoa---`, `v1saoe---`, `v1saom---`, `v1saop---`, `v1sasa---`, `v1sase---`, `v1sasm---`, `v1sasp---`, `v1sfi----`, `v1sfia---`, `v1sfie---`, `v1sfim---`, `v1sfip---`, `v1siia---`, `v1siie---`, `v1slia---`, `v1slie---`, `v1slim---`, `v1spia---`, `v1spie---`, `v1spoa---`, `v1spoe---`, `v1spsa---`, `v1spse---`, `v1sria---`, `v1srie---`, `v1sroa---`, `v1sroe---`, `v1srsa---`, `v1stie---`, `v1stim---`, `v2daia---`, `v2dama---`, `v2dasa---`, `v2dase---`, `v2dfia---`, `v2dfim---`, `v2diia---`, `v2diie---`, `v2dpia---`, `v2dpma---`, `v2dpme---`, `v2dria---`, `v2drma---`, `v2paia---`, `v2paim---`, `v2paip---`, `v2pama---`, `v2pame---`, `v2pamm---`, `v2paoa---`, `v2paom---`, `v2paop---`, `v2pasa---`, `v2pase---`, `v2pasm---`, `v2pasp---`, `v2pfia---`, `v2pfim---`, `v2piia---`, `v2piie---`, `v2ppia---`, `v2ppie---`, `v2ppma---`, `v2ppme---`, `v2ppoa---`, `v2ppoe---`, `v2ppsa---`, `v2pria---`, `v2prie---`, `v2prma---`, `v2prmp---`, `v2proa---`, `v2prsa---`, `v2saia---`, `v2saie---`, `v2saim---`, `v2saip---`, `v2sam----`, `v2sama---`, `v2same---`, `v2samm---`, `v2samp---`, `v2saoa---`, `v2saoe---`, `v2saom---`, `v2saop---`, `v2sasa---`, `v2sase---`, `v2sasm---`, `v2sasp---`, `v2sfi----`, `v2sfia---`, `v2sfie---`, `v2sfim---`, `v2sfip---`, `v2siia---`, `v2siie---`, `v2siip---`, `v2slia---`, `v2slie---`, `v2slim---`, `v2spia---`, `v2spie---`, `v2spma---`, `v2spme---`, `v2spoa---`, `v2spoe---`, `v2spsa---`, `v2spse---`, `v2sria---`, `v2srie---`, `v2srma---`, `v2srme---`, `v2sroa---`, `v2srsa---`, `v2stie---`, `v3-roe---`, `v3daia---`, `v3daim---`, `v3daip---`, `v3daoa---`, `v3dfia---`, `v3dfim---`, `v3diia---`, `v3diie---`, `v3dlia---`, `v3dlie---`, `v3dlim---`, `v3dpia---`, `v3dpie---`, `v3dpma---`, `v3dpme---`, `v3dpsa---`, `v3dria---`, `v3pai----`, `v3paia---`, `v3paie---`, `v3paim---`, `v3paip---`, `v3pamm---`, `v3paoa---`, `v3paoe---`, `v3paom---`, `v3paop---`, `v3pasa---`, `v3pase---`, `v3pasm---`, `v3pasp---`, `v3pfia---`, `v3pfie---`, `v3pfim---`, `v3piia---`, `v3piie---`, `v3piip---`, `v3plia---`, `v3plie---`, `v3plim---`, `v3plip---`, `v3ppia---`, `v3ppie---`, `v3ppip---`, `v3ppma---`, `v3ppme---`, `v3ppoa---`, `v3ppoe---`, `v3ppsa---`, `v3ppse---`, `v3pria---`, `v3prie---`, `v3prip---`, `v3sai----`, `v3saia---`, `v3saie---`, `v3saim---`, `v3saip---`, `v3sama---`, `v3samm---`, `v3samp---`, `v3sana---`, `v3sao----`, `v3saoa---`, `v3saoe---`, `v3saom---`, `v3saop---`, `v3sas----`, `v3sasa---`, `v3sase---`, `v3sasm---`, `v3sasp---`, `v3sfi----`, `v3sfia---`, `v3sfie---`, `v3sfim---`, `v3sfip---`, `v3sfoa---`, `v3sii----`, `v3siia---`, `v3siie---`, `v3siip---`, `v3sli----`, `v3slia---`, `v3slie---`, `v3slim---`, `v3slip---`, `v3spia---`, `v3spie---`, `v3spip---`, `v3spma---`, `v3spme---`, `v3spoa---`, `v3spoe---`, `v3spop---`, `v3spsa---`, `v3spse---`, `v3sria---`, `v3srie---`, `v3srip---`, `v3srma---`, `v3sroa---`, `v3srsa---`, `v3stie---`, `v3stim---`, `v3stip---`, `x--------`, `x-p----d-`, `x-p---nn-` \| \| `morphologizer` \| `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET`, `POS=SCONJ`, `POS=CCONJ`, `Case=Nom\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `POS=ADP`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Dat\\|Definite=Def\\|Gender=Masc,Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=ADV`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Case=Nom\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|POS=VERB\\|Tense=Past\\|VerbForm=Inf\\|Voice=Mid`, `Case=Gen\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid,Pass`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET`, `Case=Acc\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET`, `Case=Acc\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Dat\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `POS=VERB\\|Tense=Pres\\|VerbForm=Inf\\|Voice=Mid,Pass`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Case=Nom\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Degree=Pos\\|Gender=Masc,Neut\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=DET`, `POS=VERB\\|Tense=Pres\\|VerbForm=Inf\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|POS=VERB\\|Tense=Past\\|VerbForm=Inf\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Definite=Def\\|Gender=Masc,Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=AUX\\|Tense=Pres\\|VerbForm=Inf\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|PronType=Dem`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Degree=Sup\\|POS=ADV`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `POS=VERB\\|Tense=Pres\\|VerbForm=Inf\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|POS=VERB\\|Tense=Past\\|VerbForm=Inf\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `POS=ADV\\|Polarity=Neg`, `Case=Acc\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Mood=Opt\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Acc\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Mid,Pass`, `Case=Gen\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=VERB\\|Tense=Fut\\|VerbForm=Inf\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=DET`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid,Pass`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Dat\\|Definite=Def\\|Gender=Masc,Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid,Pass`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid,Pass`, `Case=Gen\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Degree=Pos\\|POS=ADV`, `Case=Acc\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Degree=Pos\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Degree=Pos\\|Gender=Masc,Neut\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Degree=Cmp\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET`, `Case=Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Case=Nom\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=NUM`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=DET`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Acc\\|Degree=Cmp\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Degree=Pos\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=VERB\\|Tense=Pres\\|VerbForm=Inf\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Degree=Pos\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=NUM`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Gen\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=NUM`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Number=Plur\\|POS=DET`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Voc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Acc\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `POS=INTJ`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Degree=Sup\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `POS=ADV\\|PronType=Rel`, `Aspect=Perf\\|Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=VERB\\|Tense=Fut\\|VerbForm=Inf\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=DET`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Case=Gen\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Degree=Pos\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Plur\\|POS=DET`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|PronType=Dem`, `Degree=Cmp\\|POS=ADV`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Gen\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=DET`, `POS=ADV\\|PronType=Int`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Mood=Opt\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Acc\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Perf\\|Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Degree=Sup\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Sing\\|POS=DET`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Dat\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Perf\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `POS=AUX\\|Tense=Fut\\|VerbForm=Inf\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=NUM`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Gen\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Degree=Pos\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Dat\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=DET`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=NUM`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=DET`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=NUM`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Acc\\|Degree=Pos\\|Gender=Masc,Neut\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Degree=Pos\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=DET`, `Aspect=Perf\\|Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc,Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Number=Sing\\|POS=DET`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Sing\\|POS=DET`, `Case=Gen\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Aspect=Perf\\|Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Number=Plur\\|POS=NUM`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Plur\\|POS=NUM`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Acc\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Degree=Cmp\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Plur\\|POS=DET`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=NUM`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Dat\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET`, `Case=Gen\\|Definite=Def\\|Gender=Masc,Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Degree=Pos\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Degree=Cmp\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Mood=Sub\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=NUM`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Perf\\|Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Degree=Cmp\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Degree=Sup\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Degree=Cmp\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Degree=Cmp\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=NUM`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET`, `Case=Gen\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Degree=Pos\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Degree=Cmp\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Case=Acc\\|Degree=Sup\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Nom\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|PronType=Dem`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid,Pass`, `Case=Dat\\|Degree=Sup\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Aspect=Perf\\|Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Mood=Imp\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=NUM`, `Case=Acc\\|Degree=Cmp\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Aspect=Perf\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Sub\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=ADJ`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=NUM`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=NUM`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=DET`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|VerbForm=Gdv`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Aspect=Perf\\|Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|PronType=Dem`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=DET`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Plur\\|POS=NUM`, `Case=Dat\\|Degree=Cmp\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Aspect=Perf\\|Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Case=Gen\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `POS=PROPN`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid,Pass`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=NUM`, `Case=Gen\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Plur\\|POS=NOUN`, `Case=Dat\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Aspect=Perf\\|Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Dat\\|Definite=Def\\|Gender=Masc,Neut\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=NUM`, `Case=Nom\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Sing\\|POS=NOUN`, `Case=Dat\\|Gender=Fem,Masc\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=NUM`, `Case=Gen\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=NUM`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Voc\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid,Pass`, `Case=Voc\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Case=Acc\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Acc\\|Gender=Masc,Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=NUM`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid,Pass`, `Case=Gen\\|Degree=Pos\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Degree=Cmp\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `POS=VERB\\|Tense=Fut\\|VerbForm=Inf\\|Voice=Pass`, `Case=Gen\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Imp\\|Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid,Pass`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `POS=VERB`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Dat\\|Gender=Fem,Masc\\|Number=Plur\\|POS=NOUN`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Perf\\|Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Degree=Cmp\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|VerbForm=Gdv`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=AUX\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Perf\\|Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Imp\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Mood=Opt\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|VerbForm=Gdv`, `Aspect=Perf\\|Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Voc\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Voc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Degree=Pos\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Dat\\|Degree=Cmp\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Aspect=Perf\\|Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Degree=Sup\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Person=3\\|Poss=Yes`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Perf\\|Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Voc\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Dat\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Mood=Opt\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|VerbForm=Gdv`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ\\|PronType=Dem`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Degree=Cmp\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|PronType=Dem`, `Case=Dat\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Plur\\|POS=ADJ`, `Mood=Opt\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Imp\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Voc\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Gen\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Pass`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Aspect=Perf\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Number=Plur\\|POS=NUM`, `POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `POS=X`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Dat\\|Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=AUX\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Voc\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Act`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Mood=Sub\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Sub\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Plur\\|POS=DET`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid,Pass`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Case=Dat\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Degree=Cmp\\|Gender=Masc,Neut\\|Number=Plur\\|POS=ADJ`, `Case=Voc\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Plur\\|POS=DET`, `Aspect=Perf\\|Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Degree=Sup\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=NUM`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Perf\\|Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Aspect=Perf\\|POS=AUX\\|Tense=Past\\|VerbForm=Inf\\|Voice=Mid`, `Aspect=Perf\\|Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=NUM`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|PronType=Dem`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Sing\\|POS=DET`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Degree=Sup\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|PronType=Dem`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Sub\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `Case=Voc\\|Degree=Pos\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Person=2\\|Poss=Yes`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc,Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Number=Sing\\|POS=DET`, `Case=Dat\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem,Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Gen\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Fem,Masc\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Gender=Fem,Masc\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=ADJ\\|Person=1\\|Poss=Yes`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid,Pass`, `Case=Nom\\|Degree=Cmp\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid,Pass`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid,Pass`, `Case=Dat\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid,Pass`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid,Pass`, `Case=Dat\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=NUM`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Voc\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc,Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc,Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|POS=AUX\\|Tense=Past\\|VerbForm=Inf\\|Voice=Act`, `POS=VERB\\|Tense=Past\\|VerbForm=Inf\\|Voice=Act`, `POS=PUNCT`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=1`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PRON`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=PRON`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON`, `POS=VERB\\|Tense=Past\\|VerbForm=Inf\\|Voice=Pass`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `POS=VERB\\|Tense=Past\\|VerbForm=Inf\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=PRON`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PRON`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Gender=Masc\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=2`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `POS=PART`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Voc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON`, `POS=DET`, `Case=Gen\\|Number=Sing\\|POS=PRON`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part`, `Case=Nom\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Number=Sing\\|POS=PRON`, `Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `POS=PRON`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Voc\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Dual\\|POS=NOUN`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Dual\\|POS=PRON`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=DET`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Dual\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Dual\\|POS=NOUN`, `Case=Nom\\|Gender=Fem\\|Number=Dual\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Dual\\|POS=PRON`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Fem\\|Number=Dual\\|POS=NOUN`, `Case=Nom\\|Gender=Fem\\|Number=Dual\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Dual\\|POS=DET`, `Case=Gen\\|Gender=Masc\\|Number=Dual\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=PRON`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Dual\\|POS=ADJ`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=X`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Fem\\|Number=Dual\\|POS=DET`, `Case=Acc\\|Gender=Fem\\|Number=Dual\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Dual\\|POS=PRON`, `Case=Acc\\|Gender=Fem\\|Number=Dual\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Acc\\|Number=Sing\\|POS=PRON`, `Case=Voc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Number=Sing\\|POS=NOUN`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `POS=VERB\\|Tense=Past\\|VerbForm=Inf`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Fem\\|Number=Dual\\|POS=NOUN`, `Case=Nom\\|Number=Sing\\|POS=PRON`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Sub\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Number=Dual\\|POS=PRON`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Degree=Cmp\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=X`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Neut\\|Number=Dual\\|POS=ADJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Dat\\|Number=Plur\\|POS=PRON`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Fem\\|Number=Dual\\|POS=PRON`, `Case=Gen\\|Gender=Fem\\|Number=Dual\\|POS=DET`, `Case=Gen\\|Gender=Fem\\|Number=Dual\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Dual\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin`, `Mood=Imp\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Imp\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Dual\\|POS=NOUN`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Voc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Voc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Dual\\|POS=PRON`, `Case=Nom\\|Gender=Neut\\|Number=Dual\\|POS=NOUN`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Dual\\|POS=ADJ`, `Case=Nom\\|Degree=Cmp\\|Gender=Masc\\|Number=Dual\\|POS=ADJ`, `Mood=Ind\\|Number=Dual\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Degree=Cmp\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Mood=Imp\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Dual\\|POS=PRON`, `Case=Nom\\|Number=Dual\\|POS=PRON`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|POS=NOUN`, `Case=Acc\\|Gender=Neut\\|POS=NOUN`, `Case=Dat\\|Number=Plur\\|POS=NOUN`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Voc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Dual\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Dual\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Dual\\|POS=NOUN`, `Case=Gen\\|Gender=Masc\\|Number=Dual\\|POS=PRON`, `Case=Voc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Sub\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Dual\\|POS=ADJ`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin`, `POS=VERB\\|VerbForm=Inf\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Dual\\|POS=NOUN`, `Case=Nom\\|Number=Plur\\|POS=PRON`, `Aspect=Perf\\|POS=VERB\\|Tense=Past\\|VerbForm=Inf`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Dual\\|POS=DET`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=X\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Neut\\|Number=Dual\\|POS=DET`, `Case=Nom\\|Gender=Neut\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|POS=VERB\\|Tense=Fut\\|VerbForm=Inf\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Imp\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=PRON`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Case=Voc\\|Gender=Masc\\|Number=Dual\\|POS=ADJ`, `Mood=Sub\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Dual\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Ind\\|Number=Plur\\|POS=X\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Opt\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Mood=Imp\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Dual\\|POS=PRON`, `Mood=Sub\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Aspect=Perf\\|Mood=Imp\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Imp\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Mid`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Opt\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Mood=Ind\\|Number=Plur\\|POS=X\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Case=Voc\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin`, `Aspect=Imp\\|Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Gen\\|Number=Plur\\|POS=PRON`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Mood=Opt\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Fut\\|VerbForm=Fin`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Mood=Imp\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Ind\\|Number=Dual\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Mid`, `Mood=Opt\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Mid`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=2`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=2`, `Case=Gen\\|Number=Sing\\|POS=PRON\\|Person=2`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=2`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=2`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1`, `Degree=Sup\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2`, `Case=Nom\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Number=Plur\\|POS=X`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pqp\\|VerbForm=Fin\\|Voice=Mid`, `Case=Nom\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `Case=Voc\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Mid`, `POS=VERB\\|Tense=Pres\\|VerbForm=Inf`, `Aspect=Perf\\|Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Dual\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Acc\\|Number=Dual\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Dual\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Inf\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=X`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Tense=Fut\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|VerbForm=Part\\|Voice=Mid`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Mid`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Case=Acc\\|Gender=Fem\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part\\|Voice=Mid`, `Mood=Opt\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Case=Acc\\|Number=Plur\\|POS=PRON`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Fut\\|VerbForm=Fin` \| \| `parser` \| `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` \| \| `entity_ruler` \| `ETHNICITY`, `LOCATION`, `PERSON` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TAG_ACC` \| 65.91 \| \| `POS_ACC` \| 86.26 \| \| `MORPH_ACC` \| 75.98 \| \| `LEMMA_ACC` \| 78.15 \| \| `DEP_UAS` \| 62.99 \| \| `DEP_LAS` \| 54.13 \| \| `SENTS_P` \| 58.96 \| \| `SENTS_R` \| 67.12 \| \| `SENTS_F` \| 62.78 \| \| `ENTS_F` \| 0.00 \| \| `ENTS_P` \| 0.00 \| \| `ENTS_R` \| 0.00 \| \| `ENTS_PER_TYPE` \| 0.00 \| \| `TOK2VEC_LOSS` \| 5105059.07 \| \| `TAGGER_LOSS` \| 3119288.10 \| \| `MORPHOLOGIZER_LOSS` \| 3638836.67 \| \| `TRAINABLE_LEMMATIZER_LOSS` \| 2680350.80 \| \| `PARSER_LOSS` \| 7252141.29 \|
BobMcDear/vit_base_patch8_224_dino	BobMcDear	2023-01-11T14:16:07Z	0	0	null	[ "region:us" ]	null	2023-01-11T14:14:39Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
Fake-Person/Top_CivitAI_models	Fake-Person	2023-01-11T14:12:51Z	0	17	null	[ "region:us" ]	null	2022-12-23T20:52:45Z	I re-upload it for people who use google collab including my self because google collab cannot use Civitai links to download it Hassanblends? - https://civitai.com/models/1173/hassanblend-1512-and-previous-versions Anytwam - https://civitai.com/models/3745/anytwam-11-mixedmodel Art & Eros (aEros) - A tribute to beauty - https://civitai.com/models/3950/art-and-eros-aeros-a-tribute-to-beauty Darkberry - https://civitai.com/models/1845/darkberry-mix-99a1a2f6 Dreamlike - https://civitai.com/models/1274/dreamlike-diffusion-10 Gobgirlz - https://civitai.com/models/3660/gobgirlz elldreth's dream mix - https://civitai.com/models/1254/elldreths-dream-mix Healy's Anime blend - https://civitai.com/models/1400/healys-anime-blend Jamix https://civitai.com/models/3739/jamix Model - https://civitai.com/models/2034/moedel Mandarine - https://civitai.com/models/3521/mandarine pfg_1111? - https://civitai.com/models/1227/pfg allymix - https://civitai.com/models/1202/the-allys-mix wildmix - https://civitai.com/models/3095/wilds-mix uberporn - https://civitai.com/models/2661/uber-realistic-porn-merge-urpm
scorch12/IS-chatbot-model-2	scorch12	2023-01-11T14:06:53Z	66	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-01-11T14:06:27Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: IS-chatbot-model-2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # IS-chatbot-model-2 This model is a fine-tuned version of [esakrissa/IndoBERT-SQuAD](https://huggingface.co/esakrissa/IndoBERT-SQuAD) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 73060, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.8.0 - Tokenizers 0.13.2
SegevC/ppo-LunarLander-v2	SegevC	2023-01-11T13:43:40Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T13:43:15Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.58 +/- 8.99 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mnavas/hf-rl-pyramidsv1	mnavas	2023-01-11T13:23:56Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-11T11:14:47Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: mnavas/hf-rl-pyramidsv1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
girinlp-i2i/biobert-finetuned-ner	girinlp-i2i	2023-01-11T13:23:37Z	119	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-11T03:00:38Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: biobert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # biobert-finetuned-ner This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0280 - Precision: 0.9316 - Recall: 0.9303 - F1: 0.9310 - Accuracy: 0.9953 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0311 \| 1.0 \| 6070 \| 0.0278 \| 0.8892 \| 0.9098 \| 0.8994 \| 0.9939 \| \| 0.0213 \| 2.0 \| 12140 \| 0.0330 \| 0.9133 \| 0.9071 \| 0.9102 \| 0.9934 \| \| 0.0132 \| 3.0 \| 18210 \| 0.0224 \| 0.9283 \| 0.9194 \| 0.9238 \| 0.9952 \| \| 0.0078 \| 4.0 \| 24280 \| 0.0243 \| 0.9231 \| 0.9180 \| 0.9205 \| 0.9949 \| \| 0.0068 \| 5.0 \| 30350 \| 0.0222 \| 0.9253 \| 0.9303 \| 0.9278 \| 0.9957 \| \| 0.0035 \| 6.0 \| 36420 \| 0.0245 \| 0.9243 \| 0.9344 \| 0.9293 \| 0.9955 \| \| 0.0039 \| 7.0 \| 42490 \| 0.0282 \| 0.9283 \| 0.9372 \| 0.9327 \| 0.9954 \| \| 0.0008 \| 8.0 \| 48560 \| 0.0280 \| 0.9316 \| 0.9303 \| 0.9310 \| 0.9953 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
Art-phys/ppo-LunarLander-120M-v2	Art-phys	2023-01-11T13:16:43Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T13:16:15Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 294.16 +/- 21.42 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Art-phys/poca-Huggy	Art-phys	2023-01-11T13:11:38Z	13	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-01-11T13:11:28Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # poca Agent playing Huggy This is a trained model of a poca agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: Art-phys/poca-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
JasperDekoninck/q-Taxi-v3	JasperDekoninck	2023-01-11T12:50:30Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T12:50:09Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="OcelotBane/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
LarryAIDraw/anymono_basil	LarryAIDraw	2023-01-11T12:48:12Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-01-11T11:43:30Z	--- license: creativeml-openrail-m ---
vicfeuga/q-FrozenLake-v1-4x4-noSlippery	vicfeuga	2023-01-11T12:44:25Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T11:25:17Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="vicfeuga/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
JasperDekoninck/q-FrozenLake-v1-4x4-noSlippery	JasperDekoninck	2023-01-11T12:44:06Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T12:29:57Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="OcelotBane/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
jason1i/Reinforce-CartPole-v1	jason1i	2023-01-11T12:32:16Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T12:32:04Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Agog/SpaceInvaders	Agog	2023-01-11T12:29:53Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-09T21:50:54Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 677.00 +/- 93.65 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Agog -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Agog -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Agog ``` ## Hyperparameters ```python OrderedDict([('batch_size', 8), ('buffer_size', 50000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 50000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
LarryAIDraw/anymonoko_AbyssOrangeMix2_nsfw	LarryAIDraw	2023-01-11T12:27:01Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-01-11T11:17:10Z	--- license: creativeml-openrail-m ---
alvarobb/dqn-SpaceInvadersNoFrameskip-v4	alvarobb	2023-01-11T12:10:11Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T12:09:32Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 670.00 +/- 256.75 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga alvarobb -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga alvarobb -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga alvarobb ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
4mosot/ppo-Pyramids	4mosot	2023-01-11T12:09:32Z	15	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-11T12:08:47Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: 4mosot/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Abdelrahman123/q-FrozenLake-v1-4x4-noSlippery	Abdelrahman123	2023-01-11T11:54:00Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T11:53:56Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Abdelrahman123/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
tayfen/ppo_pyramids_baseline	tayfen	2023-01-11T11:43:02Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-11T11:42:55Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: tayfen/ppo_pyramids_baseline 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
4mosot/ppo-SnowballTarget	4mosot	2023-01-11T11:32:04Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-01-11T11:31:57Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: 4mosot/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
NathanaelM/Reinforce-Pixelcopter-PLE-v0	NathanaelM	2023-01-11T11:31:35Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-01-06T15:56:27Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 47.00 +/- 37.05 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
akum1343/results3	akum1343	2023-01-11T11:21:32Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-11T08:51:09Z	--- license: mit tags: - generated_from_trainer model-index: - name: results3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results3 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 27 \| 1.8564 \| 35.5763 \| 12.1495 \| 24.0011 \| 32.6505 \| 93.5333 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cpu - Datasets 2.6.1 - Tokenizers 0.12.1
samueldomdey/concreteness-english-distilroberta-base	samueldomdey	2023-01-11T11:08:39Z	113	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "distilroberta", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-04T15:05:02Z	--- language: en tags: - distilroberta widget: - text: animal - text: love - text: oh happy day ---
RajMoodley/PPO-SnowballTarget	RajMoodley	2023-01-11T11:06:10Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-01-11T11:06:03Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: RajMoodley/PPO-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Harm/my_ppo_rl_lunarlander	Harm	2023-01-11T10:53:52Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-11T09:47:15Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 245.64 +/- 23.52 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Subsets and Splits

Filtered Qwen2.5 Distill Models

Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.

Filtered Model Cards Count

Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.

Filtered Distill Qwen 7B Models

Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.

Filtered Qwen-7b Model Cards

The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.

Filtered Qwen 7B Model Cards

The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.

Qwen 7B Distilled Models

The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.

Qwen 7B Distilled Model Cards

The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.

Qwen 7B Distilled Models

Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.

Distilled Qwen 7B Models

The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.

Filtered Model Cards with Distill Qwen2.

Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.

Filtered Model Cards with Distill Qwen 7

The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.

Distill Qwen 7B Model Cards

The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.