license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.12682088744588746 | 877c4c308c6df334065341ffdc836aa3 |
apache-2.0 | ['automatic-speech-recognition'] | false | This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository (`timit-ids.txt`) The model was finetuned on [Wav2vec 2.0 Large, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec), and the results on the validation set were **PER:** 21\.0%, **FER:** 9\.2%. | 8326c8e1452ddebed3102b027f14fdc3 |
mit | ['pytorch', 'deberta', 'deberta-v2', 'named entity recognition', 'named-entity-recognition', 'ner'] | false | このモデルはdeberta-v2-base-japaneseをファインチューニングして固有表現抽出（NER）に用いれるようにしたものです。 このモデルはdeberta-v2-base-japaneseを Wikipediaを用いた日本語の固有表現抽出データセット(ストックマーク社、https://github.com/stockmarkteam/ner-wikipedia-dataset )を用いてファインチューニングしたものです。 | 318a4a5a61a7a68246c29ce3311f53fa |
mit | ['pytorch', 'deberta', 'deberta-v2', 'named entity recognition', 'named-entity-recognition', 'ner'] | false | This model is fine-tuned model for Named Entity Recognition (NER) which is based on deberta-v2-base-japanese This model is fine-tuned by using Wikipedia dataset. You could use this model for NER tasks. | f5b76112ba780946ee029f23ceda0282 |
mit | ['pytorch', 'deberta', 'deberta-v2', 'named entity recognition', 'named-entity-recognition', 'ner'] | false | How to use 使い方 transformersおよびpytorch、sentencepiece、Juman++をインストールしてください。 以下のコードを実行することで、固有表現抽出タスクを解かせることができます。 please execute this code. ```python from transformers import AutoTokenizer,pipeline, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained('Mizuiro-sakura/deberta-v2-base-japanese-finetuned-ner') model=AutoModelForTokenClassification.from_pretrained('Mizuiro-sakura/deberta-v2-base-japanese-finetuned-ner') | 37f92d5abdf9ce20a805aea0e247091b |
mit | ['pytorch', 'deberta', 'deberta-v2', 'named entity recognition', 'named-entity-recognition', 'ner'] | false | モデルの精度 accuracy of model precision recall f1-score support その他の組織名 0.73 0.75 0.74 238 イベント名 0.81 0.81 0.81 215 人名 0.84 0.87 0.85 547 地名 0.83 0.83 0.83 446 政治的組織名 0.82 0.85 0.83 263 施設名 0.74 0.86 0.80 241 法人名 0.81 0.82 0.82 487 製品名 0.68 0.73 0.71 252 micro avg 0.79 0.82 0.81 2689 macro avg 0.78 0.81 0.80 2689 weighted avg 0.79 0.82 0.81 2689 | 49986889472ca4f17df2bbd2120f7430 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Swedish_Sign_Language (swl) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 02:05:14.693 | 67431ff964c7cbc4f5c20791e148dd25 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-wnli This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1284 | 01adf193c7823e1d692366cd6372f872 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.7452 | 6.25 | 500 | 1.2770 | | 0.9127 | 12.5 | 1000 | 0.8006 | | 0.6024 | 18.75 | 1500 | 0.5714 | | 0.3967 | 25.0 | 2000 | 0.6533 | | 0.3443 | 31.25 | 2500 | 0.3623 | | 0.2739 | 37.5 | 3000 | 0.3035 | | 0.2326 | 43.75 | 3500 | 0.2767 | | 0.1942 | 50.0 | 4000 | 0.1730 | | 0.1666 | 56.25 | 4500 | 0.1674 | | 0.1688 | 62.5 | 5000 | 0.1459 | | 0.1378 | 68.75 | 5500 | 0.2353 | | 0.1344 | 75.0 | 6000 | 0.1074 | | 0.1259 | 81.25 | 6500 | 0.1757 | | 0.1176 | 87.5 | 7000 | 0.0720 | | 0.1114 | 93.75 | 7500 | 0.1377 | | 0.0993 | 100.0 | 8000 | 0.1752 | | 0.0992 | 106.25 | 8500 | 0.1284 | | b90d5be2fda6161eb47c69d4db24500e |
mit | ['generated_from_trainer'] | false | roberta-base-MLM This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2449 - Accuracy: 0.7842 | 7794fb190a26b90f0d2548d4b3d19958 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 3.0 | 09818fbfc5323856b2cd2a88cec9dd09 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | kyoto_marian_mod_3 This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2](https://huggingface.co/Hoax0930/kyoto_marian_mod_2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2477 - Bleu: 19.9506 | 383dee5990b94b5608fb7c77be06025e |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP | 8f626b022af37919a605ccd87dc133ff |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ug', 'robust-speech-event', 'hf-asr-leaderboard'] | false | XLS-R-300M Uyghur CV7 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UG dataset. It achieves the following results on the evaluation set: - Loss: 0.1772 - Wer: 0.2589 | 1107fe6ecfbe901823f85d2a59d3d8db |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ug', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Model description For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) The model vocabulary consists of the alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), with punctuation removed. | 38eeeca5c6661fe5e6a05049fce74db0 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ug', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Intended uses & limitations This model is expected to be of some utility for low-fidelity use cases such as: - Draft video captions - Indexing of recorded broadcasts The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers. | f0acef2718bf8a5707702276954f520a |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ug', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training and evaluation data The combination of `train` and `dev` of common voice official splits were used as training data. The official `test` split was used as validation data as well as for final evaluation. | 9882b10c5d204470c9841fd8667d7a8d |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ug', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training procedure The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV7 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 18500 steps (100 epochs). | 89524452d24fa78642e4faf65c6ed92b |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ug', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 100.0 - mixed_precision_training: Native AMP | 6f426496cc786e49cbd3ac926c5c0b85 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ug', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.3043 | 2.73 | 500 | 3.2415 | 1.0 | | 3.0482 | 5.46 | 1000 | 2.9591 | 1.0 | | 1.4767 | 8.2 | 1500 | 0.4779 | 0.5777 | | 1.3152 | 10.93 | 2000 | 0.3697 | 0.4938 | | 1.2246 | 13.66 | 2500 | 0.3084 | 0.4459 | | 1.1781 | 16.39 | 3000 | 0.2842 | 0.4154 | | 1.1351 | 19.13 | 3500 | 0.2615 | 0.3929 | | 1.1052 | 21.86 | 4000 | 0.2462 | 0.3747 | | 1.0711 | 24.59 | 4500 | 0.2366 | 0.3652 | | 1.035 | 27.32 | 5000 | 0.2268 | 0.3557 | | 1.0277 | 30.05 | 5500 | 0.2243 | 0.3450 | | 1.002 | 32.79 | 6000 | 0.2204 | 0.3389 | | 0.9837 | 35.52 | 6500 | 0.2156 | 0.3349 | | 0.9773 | 38.25 | 7000 | 0.2127 | 0.3289 | | 0.9807 | 40.98 | 7500 | 0.2142 | 0.3274 | | 0.9582 | 43.72 | 8000 | 0.2004 | 0.3142 | | 0.9548 | 46.45 | 8500 | 0.2022 | 0.3050 | | 0.9251 | 49.18 | 9000 | 0.2019 | 0.3035 | | 0.9103 | 51.91 | 9500 | 0.1964 | 0.3021 | | 0.915 | 54.64 | 10000 | 0.1970 | 0.3032 | | 0.8962 | 57.38 | 10500 | 0.2007 | 0.3046 | | 0.8729 | 60.11 | 11000 | 0.1967 | 0.2942 | | 0.8744 | 62.84 | 11500 | 0.1952 | 0.2885 | | 0.874 | 65.57 | 12000 | 0.1894 | 0.2895 | | 0.8457 | 68.31 | 12500 | 0.1895 | 0.2828 | | 0.8519 | 71.04 | 13000 | 0.1912 | 0.2875 | | 0.8301 | 73.77 | 13500 | 0.1878 | 0.2760 | | 0.8226 | 76.5 | 14000 | 0.1808 | 0.2701 | | 0.8071 | 79.23 | 14500 | 0.1849 | 0.2741 | | 0.7999 | 81.97 | 15000 | 0.1808 | 0.2717 | | 0.7947 | 84.7 | 15500 | 0.1821 | 0.2716 | | 0.7783 | 87.43 | 16000 | 0.1824 | 0.2661 | | 0.7729 | 90.16 | 16500 | 0.1773 | 0.2639 | | 0.7759 | 92.9 | 17000 | 0.1767 | 0.2629 | | 0.7713 | 95.63 | 17500 | 0.1780 | 0.2621 | | 0.7628 | 98.36 | 18000 | 0.1773 | 0.2594 | | 475173d03037f96e734cbcb78bc70d4a |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 128 - seed: 2 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 75 | a8a099a3698f5ed8c0950f11793d1826 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert_oscarth_0040 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2890 - Validation Loss: 1.2296 - Epoch: 39 | 39da0e6e782a8b93fa8e5fa8cfc33375 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.1327 | 2.9983 | 0 | | 2.7813 | 2.4562 | 1 | | 2.4194 | 2.2066 | 2 | | 2.2231 | 2.0562 | 3 | | 2.0894 | 1.9450 | 4 | | 1.9905 | 1.8621 | 5 | | 1.9148 | 1.7941 | 6 | | 1.8508 | 1.7363 | 7 | | 1.7976 | 1.6909 | 8 | | 1.7509 | 1.6488 | 9 | | 1.7126 | 1.6124 | 10 | | 1.6764 | 1.5835 | 11 | | 1.6450 | 1.5521 | 12 | | 1.6175 | 1.5282 | 13 | | 1.5919 | 1.5045 | 14 | | 1.5679 | 1.4833 | 15 | | 1.5476 | 1.4627 | 16 | | 1.5271 | 1.4498 | 17 | | 1.5098 | 1.4270 | 18 | | 1.4909 | 1.4161 | 19 | | 1.4760 | 1.3995 | 20 | | 1.4609 | 1.3864 | 21 | | 1.4475 | 1.3717 | 22 | | 1.4333 | 1.3590 | 23 | | 1.4203 | 1.3478 | 24 | | 1.4093 | 1.3403 | 25 | | 1.3980 | 1.3296 | 26 | | 1.3875 | 1.3176 | 27 | | 1.3773 | 1.3094 | 28 | | 1.3674 | 1.3011 | 29 | | 1.3579 | 1.2920 | 30 | | 1.3497 | 1.2826 | 31 | | 1.3400 | 1.2764 | 32 | | 1.3326 | 1.2694 | 33 | | 1.3236 | 1.2635 | 34 | | 1.3169 | 1.2536 | 35 | | 1.3096 | 1.2477 | 36 | | 1.3024 | 1.2408 | 37 | | 1.2957 | 1.2364 | 38 | | 1.2890 | 1.2296 | 39 | | 8c46b68db844040515d2c45939f1a857 |
apache-2.0 | ['translation'] | false | opus-mt-pap-en * source languages: pap * target languages: en * OPUS readme: [pap-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pap-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.eval.txt) | 50f940f8f68164a0025601f7442591df |
apache-2.0 | ['generated_from_trainer'] | false | nmt-mpst-id-en-lr_1e-4-ep_10-seq_128_bs-64 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4108 - Bleu: 5.8803 - Meteor: 0.1857 | 2248f7371b34e618e5f35c02e9878cea |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 10 | a648aa6daa401b984a05ac141848b2a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | No log | 1.0 | 101 | 2.8898 | 2.8643 | 0.1158 | | No log | 2.0 | 202 | 2.7574 | 3.5561 | 0.1355 | | No log | 3.0 | 303 | 2.6672 | 4.1558 | 0.1509 | | No log | 4.0 | 404 | 2.5927 | 4.5156 | 0.1593 | | 2.9931 | 5.0 | 505 | 2.5319 | 4.9528 | 0.1673 | | 2.9931 | 6.0 | 606 | 2.4832 | 5.2665 | 0.1728 | | 2.9931 | 7.0 | 707 | 2.4505 | 5.4822 | 0.1778 | | 2.9931 | 8.0 | 808 | 2.4290 | 5.7456 | 0.1829 | | 2.9931 | 9.0 | 909 | 2.4147 | 5.8499 | 0.185 | | 2.6176 | 10.0 | 1010 | 2.4108 | 5.8803 | 0.1857 | | 02ded47e020e15664eb917b2686cc2e4 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | S2T-SMALL-COVOST2-ES-EN-ST `s2t-small-covost2-es-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) | 9cbd6fb2a3e6971c5a3f4ddeb1d0ced6 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. | b77feb5978a34f94624011c34b1f4512 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Intended uses & limitations This model can be used for end-to-end Spanish speech to English text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. | dcf87db334a542b6c7f7f31d66ea03c1 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example.* You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-es-en-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-es-en-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=48_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` | 63fd2ea91fd1e5595cf8a5f2c0eeb2c1 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Training data The s2t-small-covost2-es-en-st is trained on Spanish-English subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset | 65b5728fe53100982f96245a74578943 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using character based SentencePiece vocab. | ab82dd50010e5be6e989118afe082179 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. | 25f3e42a1b96cd53617d0e6391dc30f9 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ``` | 050c4f4d8af1a5245c39330902789095 |
apache-2.0 | ['summarization'] | false | facebook/bart-base model fine-tuned on CNN/DailyMail This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **35%** of the original weights. The model contains **53%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method). <div class="graph"><script src="/echarlaix/bart-base-cnn-r2-19.4-d35-hybrid/raw/main/model_card/density_info.js" id="c0afb977-b30c-485d-ac75-afc874392380"></script></div> | 5b007a07bab17521d935810ace0afd47 |
apache-2.0 | ['summarization'] | false | Fine-Pruning details This model was fine-tuned from the HuggingFace [model](https://huggingface.co/facebook/bart-base). A side-effect of the block pruning is that some of the attention heads are completely removed: 38 heads were removed on a total of 216 (17.6%). | da0e70e10d0f800d8c08e2326a1b1d24 |
apache-2.0 | ['generated_from_keras_callback'] | false | silviacamplani/distilbert-finetuned-dapt_tapt-ner-music This model is a fine-tuned version of [silviacamplani/distilbert-finetuned-dapt_tapt-lm-ai](https://huggingface.co/silviacamplani/distilbert-finetuned-dapt_tapt-lm-ai) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6073 - Validation Loss: 0.7078 - Train Precision: 0.5337 - Train Recall: 0.5986 - Train F1: 0.5643 - Train Accuracy: 0.8344 - Epoch: 9 | 31d32428bb318796614b6ce927da7bf7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 370, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | 2aa6fd184d620b4f066e524f4aa65e80 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 2.6231 | 2.0072 | 0.0 | 0.0 | 0.0 | 0.5482 | 0 | | 1.7195 | 1.5337 | 0.1905 | 0.0072 | 0.0139 | 0.5597 | 1 | | 1.3447 | 1.2423 | 0.3073 | 0.3510 | 0.3277 | 0.6910 | 2 | | 1.1065 | 1.0569 | 0.4162 | 0.4536 | 0.4341 | 0.7195 | 3 | | 0.9326 | 0.9225 | 0.5050 | 0.5473 | 0.5253 | 0.7689 | 4 | | 0.8061 | 0.8345 | 0.5306 | 0.5770 | 0.5528 | 0.8011 | 5 | | 0.7118 | 0.7749 | 0.5292 | 0.5878 | 0.5569 | 0.8176 | 6 | | 0.6636 | 0.7366 | 0.5314 | 0.5950 | 0.5614 | 0.8242 | 7 | | 0.6284 | 0.7158 | 0.5330 | 0.5968 | 0.5631 | 0.8321 | 8 | | 0.6073 | 0.7078 | 0.5337 | 0.5986 | 0.5643 | 0.8344 | 9 | | 24361779141226bc0ced21b6e82e8d51 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Mona Speech Model (Trained on ICU Data) This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Mona Speech dataset. It achieves the following results on the evaluation set: - Loss: 0.6949 - Wer: 114.5294 | a0e44598ac914bcbcd6dba8fe5a3da54 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0001 | 31.25 | 1000 | 0.6152 | 109.7314 | | 0.0001 | 62.5 | 2000 | 0.6619 | 111.6657 | | 0.0 | 93.75 | 3000 | 0.6838 | 114.1096 | | 0.0 | 125.0 | 4000 | 0.6949 | 114.5294 | | 8ece36e9e5d78a0db1b672065711eb0b |
mit | ['generated_from_trainer'] | false | bert-base-romanian-ner-finetuned-ner This model is a fine-tuned version of [dumitrescustefan/bert-base-romanian-ner](https://huggingface.co/dumitrescustefan/bert-base-romanian-ner) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0539 - Precision: 0.9662 - Recall: 0.9758 - F1: 0.9710 - Accuracy: 0.9861 | 5f8687871794a73285323b5c80f7c206 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0538 | 1.0 | 5500 | 0.0539 | 0.9662 | 0.9758 | 0.9710 | 0.9861 | | 564c3dec87d8c7c6aab0ad45736fa999 |
mit | ['generated_from_trainer'] | false | model_output_sorted_by_upvotes_positive_subreddit-wallstreetbets_1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9814 | 2fcb2c5161e8a5db5751fe32add54459 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP | 3c4fa57baeb2da93c3adf0894265ec4a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7551 | 1.07 | 1000 | 3.7881 | | 3.5181 | 2.13 | 2000 | 3.7335 | | 3.3476 | 3.2 | 3000 | 3.7369 | | 3.212 | 4.27 | 4000 | 3.7678 | | 3.0517 | 5.34 | 5000 | 3.8142 | | 2.899 | 6.4 | 6000 | 3.8666 | | 2.7874 | 7.47 | 7000 | 3.9208 | | 2.7247 | 8.54 | 8000 | 3.9636 | | 2.6566 | 9.6 | 9000 | 3.9814 | | 635ca9e8974f2b5d727d67298902ff9b |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Model description This is a ported version of [S3PRL's Hubert for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1). The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k), which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) | 97590e1f3e8617d0c872fd1efe7bcb1b |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "si", split="test") classifier = pipeline("audio-classification", model="superb/hubert-large-superb-sid") labels = classifier(dataset[0]["file"], top_k=5) ``` Or use the model directly: ```python import torch import librosa from datasets import load_dataset from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor def map_to_array(example): speech, _ = librosa.load(example["file"], sr=16000, mono=True) example["speech"] = speech return example | 2c7769c12ee41e9bfd826f0165714f5b |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "si", split="test") dataset = dataset.map(map_to_array) model = HubertForSequenceClassification.from_pretrained("superb/hubert-large-superb-sid") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-sid") | 6424a9825bc55908661cfa0136c2147f |
apache-2.0 | ['generated_from_trainer'] | false | (BERT base) Language modeling in the legal domain in Portuguese **legal-bert-base-cased-ptbr** is a Language Model in the legal domain in Portuguese based on the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) by using a MASK objective. The model is intended to assist NLP research in the legal field, computer law and legal technology applications. Several legal texts in Portuguese were used (more information below). **Large version of the model will be available soon**. | 0b2ffb612e137e9014abed34049ea788 |
apache-2.0 | ['generated_from_trainer'] | false | Pre-training corpora The pre-training corpora of **legal-bert-base-cased-ptbr** include: * 61309 - Documentos juridicos diversos | (Miscellaneous legal documents) * 751 - Petições (Recurso Extraordinário JEC) | (Petitions) * 682 - Sentenças | (Sentences) * 498 - Acordãos 2º Instancia | (2nd Instance Accords) * 469 - Agravos Recurso extraordinário | (RE grievances) * 411 - Despacho de Admissibilidade | (Admissibility Order) The data used was provided by the BRAZILIAN SUPREME FEDERAL TRIBUNAL, through the terms of use: [LREC 2020](https://ailab.unb.br/victor/lrec2020). The results of this project do not imply in any way the position of the BRAZILIAN SUPREME FEDERAL TRIBUNAL, all being the sole and exclusive responsibility of the author of the model. | 09f1db21be7451ca798bd490a323610c |
apache-2.0 | ['generated_from_trainer'] | false | Load Pretrained Model ````python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dominguesm/legal-bert-base-cased-ptbr") model = AutoModel.from_pretrained("dominguesm/legal-bert-base-cased-ptbr") | 65d8ce450cd56c5edc2d7be3ba0fe72f |
apache-2.0 | ['generated_from_trainer'] | false | Use **legal-bert-base-cased-ptbr** variants as Language Models | Text | Masked token | Predictions | | ---------------------------------- | ------------ | ------------ | | De ordem, a Secretaria Judiciária do Supremo Tribunal Federal INTIMA a parte abaixo identificada, ou quem as suas vezes fizer, do inteiro teor do(a) despacho/decisão presente nos autos (art. 270 do Código de Processo [MASK] e art 5º da Lei 11.419/2006). | Civil | ('Civil', 0.9999), ('civil', 0.0001), ('Penal', 0.0000), ('eletrônico', 0.0000), ('2015', 0.0000) | | 2. INTIMAÇÃO da Autarquia: 2.2 Para que apresente em Juízo, com a contestação, cópia do processo administrativo referente ao benefício [MASK] em discussão na lide | previdenciário | ('ora', 0.9424), ('administrativo', 0.0202), ('doença', 0.0117), ('acidente', 0.0037), ('posto', 0.0036) | | Certifico que, nesta data, os presentes autos foram remetidos ao [MASK] para processar e julgar recurso (Agravo de Instrumento). | STF | ('Tribunal', 0.4278), ('Supremo', 0.1657), ('origem', 0.1538), ('arquivo', 0.1415), ('sistema', 0.0216) | | TEMA: 810. Validade da correção monetária e dos juros moratórios [MASK] sobre as condenações impostas à Fazenda Pública, conforme previstos no art. 1º-F da Lei 9.494/1997, com a redação dada pela Lei 11.960/2009. | incidentes | ('incidentes', 0.9979), ('incidente', 0.0021), ('aplicados', 0.0000), (',', 0.0000), ('aplicada', 0.0000) | | 1a3c207313f2fcd0e65744a63813ea54 |
apache-2.0 | ['generated_from_trainer'] | false | Training results ```` Num examples = 353435 Num Epochs = 3 Instantaneous batch size per device = 4 Total train batch size (w. parallel, distributed & accumulation) = 32 Gradient Accumulation steps = 1 Total optimization steps = 33135 TRAIN RESULTS "epoch": 3.0 "train_loss": 0.6107781137512769 "train_runtime": 10192.1545 "train_samples": 353435 "train_samples_per_second": 104.031 "train_steps_per_second": 3.251 EVAL RESULTS "epoch": 3.0 "eval_loss": 0.47251805663108826 "eval_runtime": 126.3026 "eval_samples": 17878 "eval_samples_per_second": 141.549 "eval_steps_per_second": 4.426 "perplexity": 1.604028145934512 ```` | 4f8d67eb748c02ea129acc55461f22f5 |
apache-2.0 | ['generated_from_trainer'] | false | Citation ``` @misc{domingues2022legal-bert-base-cased-ptbr, author = {Domingues, Maicon} title = {Language Model in the legal domain in Portuguese}, year={2022}, howpublished= {\url{https://huggingface.co/dominguesm/legal-bert-base-cased-ptbr/}} } ``` | bcfeeecf84c880fb74d95afefcac3da4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 12588 - mixed_precision_training: Native AMP | 87400d4bd4b2c5b06b7c8532cb2f9097 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'batch_size': 128, 'every_n_steps': 384, 'force_call_on': [12588], 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'bad_words_ids': [[32769]], 'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'bad_words_ids': [[32769]], 'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 384, 'force_call_on': [12588], 'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>', 'should_insert_prefix': True}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': 'cf05a2b0558c03b08c78f07662c22989785b9520'}, 'num_additional_tokens': 2, 'path_or_name': 'kejian/mighty-mle'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'kejian/mighty-mle', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'fanatic-conditional', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 12588, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 3f8b8c4e53f6476631d6946433801bb9 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-en-hu Neural machine translation model for translating from English (en) to Hungarian (hu). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` | 31d95566c2329f03676e0d831c1f81b2 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-02-25 * source language(s): eng * target language(s): hun * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.zip) * more information released models: [OPUS-MT eng-hun README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hun/README.md) | 2f9cc59a096be50a646b7dd85d1db723 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "I wish I hadn't seen such a horrible film.", "She's at school." ] model_name = "pytorch-models/opus-mt-tc-big-en-hu" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) | 7ae1abcf78188104811ef2a8bb9622a9 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Iskolában van. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-hu") print(pipe("I wish I hadn't seen such a horrible film.")) | 297ce597842f8e5a2784b04aa2ca32fc |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 556d5c4f4919272a47db1be067110039 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | eng-hun | tatoeba-test-v2021-08-07 | 0.62096 | 38.7 | 13037 | 79562 | | eng-hun | flores101-devtest | 0.60159 | 29.6 | 1012 | 22183 | | eng-hun | newssyscomb2009 | 0.51918 | 20.6 | 502 | 9733 | | eng-hun | newstest2009 | 0.50973 | 20.3 | 2525 | 54965 | | 29a6034f2616332b4e90be057c67a548 |
mit | [] | false | ilayaraja on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 4efeaf8b52d842d1b32675753fe2614b |
mit | [] | false | model by apurik-parv This is the Stable Diffusion model fine-tuned to the art style of Elayaraja, taught to Stable Diffusion with Dreambooth. S Elayaraja is a famous artist known for his oil paintings. He has embarked on a renowned place in the world of art. His paintings of Dravidian women are been an inspiration for many artists. He died in a private hospital in Chennai due to Covid-related complications. I hope this is a homage to him and his art will live through time. (எஸ். இளையராஜா (பிறப்பு: ஏப்ரல் 4, 1979 - இறப்பு: சூன் 6, 2021) என்பவர் தமிழக ஓவியர்களுள் ஒருவர்.[1] இவர் தமிழ்நாட்டில் உயிரோவியப் பாணி ஓவியங்களை வரைவதில் முன்னணி ஓவியராக இருந்தார்.) https://ta.wikipedia.org/wiki/%E0%AE%8E%E0%AE%B8%E0%AF%8D._%E0%AE%87%E0%AE%B3%E0%AF%88%E0%AE%AF%E0%AE%B0%E0%AE%BE%E0%AE%9C%E0%AE%BE It can be used by modifying the `instance_prompt(s)`: **iraja** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: iraja .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) | 8f5f19d060362819acbfed1a6c704f52 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-Turkish-Tr-med This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4727 - Wer: 0.4677 | 2663cdea85d79d14cce0165e137ddfb7 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8093 | 4.21 | 400 | 2.7831 | 1.0 | | 0.9881 | 8.42 | 800 | 0.5088 | 0.6681 | | 0.3519 | 12.63 | 1200 | 0.4496 | 0.6007 | | 0.2436 | 16.84 | 1600 | 0.4993 | 0.5654 | | 0.1874 | 21.05 | 2000 | 0.4793 | 0.5530 | | 0.1561 | 25.26 | 2400 | 0.5187 | 0.5589 | | 0.1336 | 29.47 | 2800 | 0.5135 | 0.5311 | | 0.1163 | 33.68 | 3200 | 0.4960 | 0.5143 | | 0.1056 | 37.89 | 3600 | 0.4795 | 0.5045 | | 0.0959 | 42.11 | 4000 | 0.4883 | 0.4987 | | 0.0819 | 46.32 | 4400 | 0.4799 | 0.4903 | | 0.0756 | 50.53 | 4800 | 0.4822 | 0.4831 | | 0.0692 | 54.74 | 5200 | 0.4621 | 0.4762 | | 0.062 | 58.95 | 5600 | 0.4727 | 0.4677 | | 49603bd9f912a68eaa5cfb640026dac9 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | dd5b62296749b7f8ea7570713b230299 |
apache-2.0 | ['translation'] | false | opus-mt-de-mt * source languages: de * target languages: mt * OPUS readme: [de-mt](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-mt/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-mt/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-mt/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-mt/opus-2020-01-20.eval.txt) | 41a60b7c7652f202a32858e77068cd87 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5273 | 9c9ca74ee68aeb87f7e7fbea6cf0e539 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - training precision: Mixed Precision | 3c6cd9b1d1c630de7126042bf9f5309d |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Residual Channel Attention Networks (RCAN) RCAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://arxiv.org/abs/1807.02758) by Zhang et al. (2018) and first released in [this repository](https://github.com/yulunzhang/RCAN). The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.  | f90e4d510b8be2fefa3ba363f651ff91 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Model description Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods. This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results. | 5b6833d76c5f5cff0940bdada3ec039b |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import RcanModel, ImageLoader from PIL import Image import requests url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg' image = Image.open(requests.get(url, stream=True).raw) model = RcanModel.from_pretrained('eugenesiow/rcan-bam', scale=2) | 731da7a443f3167f4625b4a3aef4b839 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | save an output comparing the super-image with a bicubic scaling ``` [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab") | 221c81b5a36e75cbcc40237c6ebab70c |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Training data The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900). | 94ce1fb938a5921aeb0a390621bdf513 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Preprocessing We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566). Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times. During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches. Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image. We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data: ```bash pip install datasets ``` The following code gets the data and preprocesses/augments the data. ```python from datasets import load_dataset from super_image.data import EvalDataset, TrainDataset, augment_five_crop augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\ .map(augment_five_crop, batched=True, desc="Augmenting Dataset") | b6572f45884f018a453f725f9ea2ba0f |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, RcanModel, RcanConfig training_args = TrainingArguments( output_dir='./results', | 3769b8d08d6b66ab1877fe76ff0ad605 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | evaluation dataset ) trainer.train() ``` [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab") | 0b25cdb3518d1d2b6c8462a3a34edb9c |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100) The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline. |Dataset |Scale |Bicubic |rcan-bam | |--- |--- |--- |--- | |Set5 |2x |33.64/0.9292 |**** | |Set5 |3x |30.39/0.8678 |**** | |Set5 |4x |28.42/0.8101 |**30.8/0.8701** | |Set14 |2x |30.22/0.8683 |**** | |Set14 |3x |27.53/0.7737 |**** | |Set14 |4x |25.99/0.7023 |**27.91/0.7648** | |BSD100 |2x |29.55/0.8425 |**** | |BSD100 |3x |27.20/0.7382 |**** | |BSD100 |4x |25.96/0.6672 |**27.91/0.7477** | |Urban100 |2x |26.66/0.8408 |**** | |Urban100 |3x | |**** | |Urban100 |4x |23.14/0.6573 |**24.75/0.7346** |  You can find a notebook to easily run evaluation on pretrained models below: [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab") | a8925b40a621d76602b62d1990236b09 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | BibTeX entry and citation info ```bibtex @misc{wang2021bam, title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution}, author={Fanyi Wang and Haotian Hu and Cheng Shen}, year={2021}, eprint={2104.07566}, archivePrefix={arXiv}, primaryClass={eess.IV} } ``` ```bibtex @misc{zhang2018image, title={Image Super-Resolution Using Very Deep Residual Channel Attention Networks}, author={Yulun Zhang and Kunpeng Li and Kai Li and Lichen Wang and Bineng Zhong and Yun Fu}, year={2018}, eprint={1807.02758}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` | 1ff576a2ce262d9d55c6ab208f50440b |
apache-2.0 | [] | false | The ch-w2v-conformer model uses following datasets to pretrain: ISML datasets (6 languages,70k hours): internal dataset contains 40k hours Chinese, Cantonese, Tibetan, Inner Mongolian, Inner Kazakh, Uighur. Babel datasets (17 languages, 2k hours): Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Kurmanji, Lao, Pashto, Swahili, Tagalog, Tamil, Tok, Turkish, Vietnamese, Zulu After pretraining, we build ASR system based on CTC-Attention structure. In very low resource task, we find that if too many initialization network structures are constructed in the upper layer of pre-training conformer encoder, the migration performance of the pre-training model will be destroyed, so we only build a single-layer transformer decoder for joint training. pretrained model link: | 4a52f09dda4ee125ae7b16258300b044 |
apache-2.0 | [] | false | constrained-plus Task Performance * Languages: Cantonese,mongolian,kazakh * config: conf/train_conformer_large_10h.yaml * Feature info: using mfcc feature, with dither 1.0, without cmvn * Training info: lr 0.001, batch size 10, 4 gpus on V100, acc_grad 1, 80 epochs * Decoding info: ctc_weight 0.5, average_num 35 dev set results trained only with 10 hours training set | f5565c047b50ca9dabfcbdb79ca814c8 |
apache-2.0 | [] | false | w2v-Conformer | decoding_method | Cantonese(CER) | mongolian(WER) | |:-------------------:|:----:|:----:| | ctc_greedy_search | 31.46 | 53.64 | | ctc_prefix_search | 31.47 | 53.50 | | attention_rescoring | 31.45 | 52.96 | | e840b83aac7d52eac7df9867d34602a0 |
apache-2.0 | [] | false | Conformer （train from scartch） | decoding_method | Cantonese(CER) | mongolian(WER) | |:-------------------:|----:|:----:| | ctc_greedy_search | 61.43 | 89.38 | | ctc_prefix_search | 61.37 | 89.53| | attention_rescoring | 60.61 | 89.60| | eb42e3212deafe8ad75d2dc17fe38e6e |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0454 - Accuracy: 0.9826 | 63aab0d454eb0f53b5d119bc753c8317 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 | 814937f7f88b13425b82e3ce1c29c4ea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2137 | 1.0 | 190 | 0.0981 | 0.9681 | | 0.1487 | 2.0 | 380 | 0.0517 | 0.9830 | | 0.1398 | 3.0 | 570 | 0.0454 | 0.9826 | | 0705c94c05d4812cdfb749af874e6191 |
apache-2.0 | ['translation'] | false | opus-mt-yap-fr * source languages: yap * target languages: fr * OPUS readme: [yap-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yap-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/yap-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/yap-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/yap-fr/opus-2020-01-16.eval.txt) | 079fa04b94703a64f320d72d770f0a92 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Portuguese transformer pipeline ([neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased)). Components: transformer, morphologizer, parser, ner, attribute_ruler, lemmatizer (trainable_lemmatizer). | Feature | Description | | --- | --- | | **Name** | `pt_core_news_trf` | | **Version** | `3.4.0` | | **spaCy** | `>=3.4.3,<3.5.0` | | **Default Pipeline** | `transformer`, `ner`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` | | **Components** | `transformer`, `ner`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [UD Portuguese Bosque v2.8](https://github.com/UniversalDependencies/UD_Portuguese-Bosque) (Rademaker, Alexandre; Freitas, Cláudia; de Souza, Elvis; Silveira, Aline; Cavalcanti, Tatiana; Evelyn, Wograine; Rocha, Luisa; Soares-Bastos, Isabela; Bick, Eckhard; Chalub, Fabricio; Paulino-Passos, Guilherme; Real, Livy; de Paiva, Valeria; Zeman, Daniel; Popel, Martin; Mareček, David; Silveira, Natalia; Martins, André)<br />[WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran) | | **License** | `CC BY-SA 4.0` | | **Author** | [Maicon Domingues](http://nlp.rocks) | | 151d2ed38be71139280b19d67eb2ccb8 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (742 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | | **`tagger`** | `ADJ`, `ADJ_ADJ`, `ADJ_NOUN`, `ADP`, `ADP_ADV`, `ADP_DET`, `ADP_NUM`, `ADP_PRON`, `ADP_PROPN`, `ADV`, `ADV_PRON`, `AUX`, `AUX_PRON`, `CCONJ`, `CCONJ_PRON`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PART_NOUN`, `PART_NUM`, `PRON`, `PROPN`, `PROPN_PROPN`, `PUNCT`, `SCONJ`, `SCONJ_DET`, `SCONJ_PRON`, `SYM`, `VERB`, `VERB_PRON`, `VERB_PRON_PRON`, `VERB_SCONJ`, `X` | | **`morphologizer`** | `Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `POS=PUNCT`, `POS=VERB\|VerbForm=Ger`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|POS=NUM`, `POS=SYM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=CCONJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=SCONJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|VerbForm=Inf`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=AUX\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `ExtPos=CCONJ\|POS=ADV`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=AUX\|VerbForm=Ger`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=VERB\|VerbForm=Part`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf`, `ExtPos=NOUN\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADP\|POS=ADV`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `ExtPos=CCONJ\|POS=CCONJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=AUX\|VerbForm=Part`, `Number=Plur\|POS=AUX\|Person=3\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `ExtPos=INTJ\|POS=AUX`, `Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|Number=Sing\|POS=NUM`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Plur\|POS=VERB\|Person=3\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `ExtPos=SCONJ\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Case=Acc\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Dem`, `ExtPos=SCONJ\|POS=ADV`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `ExtPos=PROPN\|Number=Sing\|POS=PROPN`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Number=Sing\|POS=AUX\|Person=3\|VerbForm=Inf`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Art`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|POS=PRON\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Definite=Def\|ExtPos=ADV\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=NOUN`, `ExtPos=CCONJ\|POS=VERB\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `ExtPos=ADV\|POS=ADP`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Abbr=Yes\|ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `ExtPos=SCONJ\|POS=SCONJ`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Inf`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art\|Typo=Yes`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=ADJ`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `ExtPos=PROPN\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `ExtPos=SCONJ\|POS=ADP`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=PROPN\|PronType=Art`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `ExtPos=NOUN\|POS=ADP`, `Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `ExtPos=ADV\|POS=ADV`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `ExtPos=NOUN\|POS=X`, `POS=X`, `ExtPos=NOUN\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Dem`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Gender=Masc\|Number=Sing\|POS=DET`, `ExtPos=ADP\|POS=ADP`, `POS=NOUN`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `ExtPos=AUX\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Typo=Yes\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Ind`, `POS=ADV\|Typo=Yes`, `Abbr=Yes\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `POS=PRON\|PronType=Rel`, `ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Definite=Def\|ExtPos=CCONJ\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Definite=Def\|ExtPos=SCONJ\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADJ\|Voice=Pass`, `Number=Sing\|POS=ADJ`, `ExtPos=ADV\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `POS=INTJ`, `Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `ExtPos=CCONJ\|POS=ADP`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Art`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `POS=SCONJ\|VerbForm=Ger`, `Abbr=Yes\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=NUM`, `Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET`, `ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=PROPN`, `ExtPos=AUX\|POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `ExtPos=ADJ\|POS=X`, `Gender=Fem\|Number=Sing\|POS=X`, `Abbr=Yes\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Number=Sing\|POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art\|Typo=Yes`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Fem\|POS=PRON\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art\|Typo=Yes`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Art`, `Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art\|Typo=Yes`, `ExtPos=AUX\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art\|Typo=Yes`, `NumType=Ord\|POS=ADJ`, `Gender=Masc\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=PROPN`, `ExtPos=PROPN\|Gender=Masc\|POS=PROPN`, `Gender=Masc\|POS=PROPN`, `Gender=Fem\|Number=Plur\|POS=DET`, `ExtPos=ADJ\|POS=ADP`, `ExtPos=ADJ\|POS=ADV`, `Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art\|Typo=Yes`, `ExtPos=ADP\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `ExtPos=NOUN\|POS=ADV`, `Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `ExtPos=NOUN\|Gender=Fem\|Number=Plur\|POS=NOUN`, `ExtPos=CCONJ\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Plur\|POS=AUX\|Person=1\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADV\|POS=X`, `Gender=Masc\|Number=Sing\|POS=X`, `POS=NUM`, `ExtPos=NOUN\|NumType=Ord\|POS=NUM`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `ExtPos=AUX\|POS=VERB\|VerbForm=Ger`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Ger`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Number=Plur\|POS=VERB\|Person=1\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `ExtPos=NOUN\|NumType=Card\|POS=PART`, `ExtPos=NUM\|Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|ExtPos=SCONJ\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `ExtPos=NOUN\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=CCONJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Definite=Def\|ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Gender=Fem\|Number=Plur\|POS=NOUN`, `NumType=Card\|POS=ADP`, `ExtPos=AUX\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|ExtPos=ADV\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Abbr=Yes\|ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=PROPN`, `NumType=Ord\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=SCONJ\|Person=3\|PronType=Prs`, `ExtPos=PROPN\|POS=X`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `ExtPos=NOUN\|POS=NOUN`, `Number=Sing\|POS=PRON\|PronType=Tot`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Art`, `POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|POS=VERB\|VerbForm=Part`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADP\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=ADJ`, `Definite=Def\|POS=ADP\|PronType=Art`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `ExtPos=NOUN\|Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|POS=SCONJ\|PronType=Art`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|POS=PRON\|PronType=Ind`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=NOUN\|Voice=Pass`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `ExtPos=AUX\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=PART`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Card\|POS=DET`, `Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `POS=PRON\|PronType=Prs`, `ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PROPN\|PronType=Art`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADV\|Person=3\|PronType=Prs`, `POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `ExtPos=SCONJ\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=NOUN\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADV\|Typo=Yes`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=SCONJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `ExtPos=ADP\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `ExtPos=CCONJ\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Definite=Def\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=ADP`, `ExtPos=AUX\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc,Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `POS=DET`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Art`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Ind`, `Definite=Def\|ExtPos=SCONJ\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `ExtPos=PROPN\|POS=ADV`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `ExtPos=PROPN\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=2\|PronType=Prs\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `NumType=Card\|POS=DET\|PronType=Art`, `Gender=Fem,Masc\|Number=Sing\|POS=PROPN`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `POS=PRON\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `ExtPos=AUX\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `ExtPos=ADJ\|Gender=Fem\|Number=Sing\|POS=X`, `Gender=Fem\|Number=Plur\|POS=NUM`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=SCONJ\|PronType=Art`, `Case=Dat\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Sets\|Number=Sing\|POS=NUM`, `POS=ADV\|PronType=Rel`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Foreign=Yes\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|POS=AUX\|PronType=Prs\|VerbForm=Inf`, `ExtPos=INTJ\|POS=ADV\|Polarity=Neg`, `POS=AUX`, `Gender=Masc\|Number=Plur\|POS=NUM`, `Number=Sing\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=PRON\|PronType=Int`, `Abbr=Yes\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Ind`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art\|Typo=Yes`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `ExtPos=NUM\|NumType=Mult\|POS=NUM`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `NumType=Card\|Number=Plur\|POS=NUM`, `ExtPos=AUX\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `ExtPos=NUM\|NumType=Card\|POS=NUM`, `POS=VERB`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Rel`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Typo=Yes\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|Typo=Yes\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf`, `ExtPos=NOUN\|Number=Sing\|POS=PROPN`, `ExtPos=ADP\|POS=DET`, `ExtPos=ADP\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Abbr=Yes\|ExtPos=PROPN\|Number=Sing\|POS=PROPN`, `ExtPos=AUX\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `ExtPos=SCONJ\|Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Art`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `ExtPos=PROPN\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=DET\|PronType=Tot`, `NumType=Range\|POS=NUM`, `Case=Dat\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Definite=Def\|ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=X`, `ExtPos=NOUN\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Abbr=Yes\|ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Dem`, `ExtPos=SCONJ\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `NumType=Frac\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Ind`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Rel`, `ExtPos=NOUN\|NumType=Card\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind\|Typo=Yes`, `Mood=Cnd\|POS=VERB\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin` | | **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `xcomp` | </details> | e40474e40e7e588dc23ddc573b1a8862 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 92.84 | | `ENTS_P` | 92.75 | | `ENTS_R` | 92.94 | | `TAG_ACC` | 97.82 | | `POS_ACC` | 97.81 | | `MORPH_ACC` | 96.11 | | `LEMMA_ACC` | 97.35 | | `DEP_UAS` | 92.84 | | `DEP_LAS` | 89.66 | | `SENTS_P` | 93.49 | | `SENTS_R` | 94.28 | | `SENTS_F` | 93.88 | | eabe06172e8b8fdc72a8a7446df4e2f8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout b8df4c928e132acff78d196988bdb68a66987952 pip install -e . cd egs2/an4/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model Fhrozen/test_an4 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 4906f5070491db4469e33e695da3410e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Wed Oct 20 00:00:46 JST 2021` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.4a1` - pytorch version: `pytorch 1.9.0` - Git hash: `b8df4c928e132acff78d196988bdb68a66987952` - Commit date: `Tue Oct 19 07:48:11 2021 -0400` | 0bbd9dea09b3ea2063db36b28297bfb7 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|773|4.0|22.3|73.7|0.1|96.1|100.0| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|591|2.7|21.8|75.5|0.0|97.3|100.0| | ce1171ce44c35cb7d1b2540dccd844de |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|2565|17.2|16.4|66.4|1.0|83.8|100.0| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|1915|15.5|16.4|68.1|0.9|85.5|100.0| | 5299b2f701636ea4a3ebc355fc65c097 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|2695|21.1|15.6|63.3|0.9|79.9|100.0| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|2015|19.4|15.6|65.0|0.9|81.5|100.0| | b2d8f646a18a46542d577d3cab083f51 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: null print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_raw_en_bpe30 ngpu: 0 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: null dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 40 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: - 10 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe30/train/speech_shape - exp/asr_stats_raw_en_bpe30/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe30/valid/speech_shape - exp/asr_stats_raw_en_bpe30/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_nodev/wav.scp - speech - sound - - dump/raw/train_nodev/text - text - text valid_data_path_and_name_and_type: - - dump/raw/train_dev/wav.scp - speech - sound - - dump/raw/train_dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adadelta optim_conf: {} scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - ▁ - T - E - O - R - Y - A - H - U - S - I - F - B - L - P - D - G - M - C - V - X - J - K - Z - W - N - Q - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: ctc_weight: 0.5 ignore_id: -1 lsm_weight: 0.0 length_normalized_loss: false report_cer: true report_wer: true sym_space: <space> sym_blank: <blank> extract_feats_in_collect_stats: true use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram30/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe30/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: rnn encoder_conf: {} postencoder: null postencoder_conf: {} decoder: rnn decoder_conf: {} required: - output_dir - token_list version: 0.10.4a1 distributed: false ``` </details> | 63653b2a2b9f34047a5440b2278931e4 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | LM config <details><summary>expand</summary> ``` config: conf/train_lm.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/lm_train_lm_en_bpe30 ngpu: 0 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: null dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 40 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 1 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 256 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/lm_stats_en_bpe30/train/text_shape.bpe valid_shape_file: - exp/lm_stats_en_bpe30/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/lm_train.txt - text - text valid_data_path_and_name_and_type: - - dump/raw/train_dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.1 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - ▁ - T - E - O - R - Y - A - H - U - S - I - F - B - L - P - D - G - M - C - V - X - J - K - Z - W - N - Q - <sos/eos> init: null model_conf: ignore_id: 0 use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram30/bpe.model non_linguistic_symbols: null cleaner: null g2p: null lm: seq_rnn lm_conf: unit: 650 nlayers: 2 required: - output_dir - token_list version: 0.10.4a1 distributed: false ``` </details> | 98afda6bb72761549acc6c93a69e1a26 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.