Datasets:
davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
xet
Community
1
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-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']
false
Intended uses & limitations The model has two uses: - use 'as is' for spoken language recognition - use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data The model is trained on automatically collected YouTube data. For more information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/).
67d1987047d91054be2ced8e84c210b6
apache-2.0
['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']
false
Download Thai language sample from Omniglot and cvert to suitable form signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3") prediction = language_id.classify_batch(signal) print(prediction)
181a3ab4bfb8868046e0249da5e27cf8
apache-2.0
['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']
false
torch.Size([1, 1, 256]) ``` To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*.
5f4e574ce4c9a749e91ea49063618a79
apache-2.0
['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']
false
Limitations and bias Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: - Probably it's accuracy on smaller languages is quite limited - Probably it works worse on female speech than male speech (because YouTube data includes much more male speech) - Based on subjective experiments, it doesn't work well on speech with a foreign accent - Probably it doesn't work well on children's speech and on persons with speech disorders
e18b8bfaef4b08de9919df1824b185b2
apache-2.0
['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']
false
Training data The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/). VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language.
81f3fb4e7452e956103fd5a9b6795fdd
apache-2.0
['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']
false
Referencing SpeechBrain ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```
ef5ad66fe5c019cb772b4a077356110c
apache-2.0
['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']
false
Referencing VoxLingua107 ```bibtex @inproceedings{valk2021slt, title={{VoxLingua107}: a Dataset for Spoken Language Recognition}, author={J{\"o}rgen Valk and Tanel Alum{\"a}e}, booktitle={Proc. IEEE SLT Workshop}, year={2021}, } ```
9c4abc05dc2a403b4b878ef4439fc79a
apache-2.0
['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']
false
About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain
c70fdf0e087def87206fc5520ca2e580
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Wav2Vec2-Large-XLSR-53-Lithuanian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Lithuanian using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz.
8e7a77ab5feae85f8bad4161b20e40ec
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "lt", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model = Wav2Vec2ForCTC.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") resampler = torchaudio.transforms.Resample(48_000, 16_000)
31ef45240d02031f806b1f7cdd757468
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
We need to read the audio files as arrays def speech_file_to_array_fn(batch): \\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): \\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ```
f1bdc4d76cf09331ab1e9b7e901741f9
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Evaluation The model can be evaluated as follows on the Lithuanian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "lt", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model = Wav2Vec2ForCTC.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model.to("cuda") chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000)
fc0dfeb304ce089dafd03569753dc3ac
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
We need to read the audio files as arrays def speech_file_to_array_fn(batch): \\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() \\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn)
6bbf1892ce3e38711cb835ec4a7aaa40
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
We need to read the audio files as arrays def evaluate(batch): \\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \\twith torch.no_grad(): \\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) \\tbatch["pred_strings"] = processor.batch_decode(pred_ids) \\treturn batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 56.55 %
cca5e8e1c7f0dd5e1754a616e323d49b
apache-2.0
['text2text-generation', 'Guyanese Creole', 'Caribbean dialect']
false
Guyanese English Creole to English Translator This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of Guyanese English Creole to English. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creoles checkout the library [Caribe](https://pypi.org/project/Caribe/). ___
b157493e442006154ceb7f79bbd2add1
apache-2.0
['text2text-generation', 'Guyanese Creole', 'Caribbean dialect']
false
Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KES/GEC-English") model = AutoModelForSeq2SeqLM.from_pretrained("KES/GEC-English") text = "Ah waan ah phone" inputs = tokenizer("guy:"+text, truncation=True, return_tensors='pt') output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True) translation=tokenizer.batch_decode(output, skip_special_tokens=True) print("".join(translation))
26ac7561ecc95e5ba63e6c1b5f840556
mit
['generated_from_trainer']
false
xlm-roberta-large-finetuned-ner This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the hi_ner_config dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2329 - eval_precision: 0.7110 - eval_recall: 0.6854 - eval_f1: 0.6980 - eval_accuracy: 0.9332 - eval_runtime: 162.3478 - eval_samples_per_second: 66.9 - eval_steps_per_second: 16.73 - epoch: 2.64 - step: 50198
fe2b278e69dcc596454df7edc4064299
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6
5a433b9f3ea4a7064df6f3f11cba4086
apache-2.0
['generated_from_trainer']
false
whisper-tiny-ar-quran This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0928 - Wer: 7.0535
79ae98872e535ac11710c206250d2d57
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP
443f7cb45f1b27cde9fc3702f8310156
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1766 | 0.05 | 500 | 0.2829 | 20.0236 | | 0.1129 | 0.09 | 1000 | 0.1981 | 13.8364 | | 0.0775 | 0.14 | 1500 | 0.1763 | 12.5450 | | 0.0678 | 0.19 | 2000 | 0.1485 | 10.7302 | | 0.0437 | 0.23 | 2500 | 0.1336 | 9.6693 | | 0.0341 | 0.28 | 3000 | 0.1244 | 8.9602 | | 0.0302 | 0.33 | 3500 | 0.1059 | 8.2224 | | 0.0189 | 0.37 | 4000 | 0.1044 | 7.6902 | | 0.0167 | 0.42 | 4500 | 0.0966 | 7.2643 | | 0.0151 | 0.47 | 5000 | 0.0928 | 7.0535 |
db356ace2c9af81232b3a34143cedd34
mit
[]
false
ZINC-t5 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset. It achieves the following results on the evaluation set: - Loss: 0.1228 - Accuracy: 0.9476
15f62776eafca0771148f0f2abe3ddf1
mit
[]
false
Model description We trained t5 on SMILES from ZINC using the task of masked-language modeling (MLM). Compared to ZINC-t5, ZINC-t5-v2 uses a character-level tokenizer, and it was also trained on ZINC.
b5ca1bdaaf5bfff7461ae409544a7b01
mit
[]
false
Intended uses & limitations This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning. As an example, We finetuned this model to predict products. The model is [here](https://huggingface.co/sagawa/ZINC-t5-productpredicition), and you can use the demo [here](https://huggingface.co/spaces/sagawa/predictproduct-t5). Using its encoder, we trained a regression model to predict a reaction yield. You can use this demo [here](https://huggingface.co/spaces/sagawa/predictyield-t5).
cf3f55787a67081d70fea2b425fd87f9
mit
[]
false
Training and evaluation data We downloaded [ZINC data](https://drive.google.com/drive/folders/1lSPCqh31zxTVEhuiPde7W3rZG8kPgp-z) and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 22992522, and they were randomly split into train:validation=10:1.
43cab4c522481352cc0a15cf80296da5
mit
[]
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-03 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0
516fe8f663405bbdb0b2a8503511c2a7
mit
[]
false
Training results | Training Loss | Step | Accuracy | Validation Loss | |:-------------:|:------:|:--------:|:---------------:| | 0.2090 | 100000 | 0.9264 | 0.1860 | | 0.1628 | 200000 | 0.9349 | 0.1613 | | 0.1632 | 300000 | 0.9395 | 0.1467 | | 0.1451 | 400000 | 0.9435 | 0.1345 | | 0.1311 | 500000 | 0.9465 | 0.1261 |
9c166915cd962771d30bb72d2ca88423
apache-2.0
['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard']
false
Hubert-Large-Finetuned [Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression) The large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. The model is a fine-tuned version of [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k). [Paper](https://arxiv.org/abs/2106.07447) Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed **Abstract** Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/hubert .
d03a428d25bdc192a3caa80bc9b0844b
apache-2.0
['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard']
false
Usage The model can be used for automatic-speech-recognition as follows: ```python import torch from transformers import Wav2Vec2Processor, HubertForCTC from datasets import load_dataset processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft") model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values
427bd053393d3a2f31eb6a06b27ad6ea
apache-2.0
['image-classification', 'timm']
false
Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.6 - GMACs: 4.5 - Activations (M): 13.4 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-22k
efa450e63660cdc2893b88a6e0463b3a
apache-2.0
['image-classification', 'timm']
false
Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_tiny.fb_in22k_ft_in1k', pretrained=True) model = model.eval()
5784b1d11dbb8c459834127c30f5312f
apache-2.0
['image-classification', 'timm']
false
Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_tiny.fb_in22k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval()
8225d48349b34a804b6894acf09a47f6
apache-2.0
['image-classification', 'timm']
false
Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_tiny.fb_in22k_ft_in1k', pretrained=True, num_classes=0,
10f4a9fa337ff77bd2361f879f6d905f
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: 2
b6e8822344441fb58293e05eef08a1bd
mit
['generated_from_trainer']
false
xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4043 - F1: 0.6886
1658f5637cd1967fc7f0c47187dfbc00
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1347 | 1.0 | 50 | 0.5771 | 0.4880 | | 0.5066 | 2.0 | 100 | 0.4209 | 0.6582 | | 0.3631 | 3.0 | 150 | 0.4043 | 0.6886 |
a23d024d738409d8e6eb0d0271c5de28
apache-2.0
['generated_from_trainer']
false
longformer_summarise This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 2.3003 - Rouge2 Precision: 0.1654 - Rouge2 Recall: 0.0966 - Rouge2 Fmeasure: 0.1118
b1bff6c588a93a0b9efd299e08e85daa
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP
1b078cb958e15501460a5578bc3e3c3e
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 2.909 | 0.08 | 10 | 2.8969 | 0.09 | 0.1439 | 0.0953 | | 2.615 | 0.16 | 20 | 2.6182 | 0.1232 | 0.0865 | 0.0924 | | 2.581 | 0.24 | 30 | 2.4687 | 0.1357 | 0.0733 | 0.09 | | 2.1294 | 0.32 | 40 | 2.5215 | 0.1495 | 0.0932 | 0.1044 | | 2.8083 | 0.4 | 50 | 2.3870 | 0.1794 | 0.1054 | 0.1224 | | 3.0704 | 0.48 | 60 | 2.3676 | 0.1572 | 0.0989 | 0.1108 | | 2.4716 | 0.56 | 70 | 2.3554 | 0.1707 | 0.1039 | 0.1198 | | 2.454 | 0.64 | 80 | 2.3411 | 0.1619 | 0.0943 | 0.1115 | | 2.3046 | 0.72 | 90 | 2.3105 | 0.1547 | 0.0965 | 0.1116 | | 1.7467 | 0.8 | 100 | 2.3417 | 0.1551 | 0.0877 | 0.1046 | | 2.7696 | 0.88 | 110 | 2.3226 | 0.1543 | 0.0954 | 0.1085 | | 2.4999 | 0.96 | 120 | 2.3003 | 0.1654 | 0.0966 | 0.1118 |
91a66d5811f8a4024f313ee6aaefb20f
apache-2.0
['automatic-speech-recognition', 'id']
false
exp_w2v2t_id_wav2vec2_s226 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
4f023a92bf82f11b904abb6ab859c10b
mit
['spacy', 'token-classification']
false
de_core_news_lg German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `de_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` | | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` | | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) | | **Sources** | [TIGER Corpus](https://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/tiger.html) (Brants, Sabine, Stefanie Dipper, Peter Eisenberg, Silvia Hansen, Esther König, Wolfgang Lezius, Christian Rohrer, George Smith, and Hans Uszkoreit)<br />[Tiger2Dep](https://www.ims.uni-stuttgart.de/forschung/ressourcen/werkzeuge/tiger2dep/) (Wolfgang Seeker)<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)<br />[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) | | **License** | `MIT` | | **Author** | [Explosion](https://explosion.ai) |
84e0ad76877a92520363c05ea16669d6
mit
['spacy', 'token-classification']
false
Label Scheme <details> <summary>View label scheme (772 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$(`, `$,`, `$.`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPRART`, `APZR`, `ART`, `CARD`, `FM`, `ITJ`, `KOKOM`, `KON`, `KOUI`, `KOUS`, `NE`, `NN`, `NNE`, `PDAT`, `PDS`, `PIAT`, `PIS`, `PPER`, `PPOSAT`, `PPOSS`, `PRELAT`, `PRELS`, `PRF`, `PROAV`, `PTKA`, `PTKANT`, `PTKNEG`, `PTKVZ`, `PTKZU`, `PWAT`, `PWAV`, `PWS`, `TRUNC`, `VAFIN`, `VAIMP`, `VAINF`, `VAPP`, `VMFIN`, `VMINF`, `VMPP`, `VVFIN`, `VVIMP`, `VVINF`, `VVIZU`, `VVPP`, `XY`, `_SP` | | **`morphologizer`** | `POS=PUNCT`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Degree=Pos\|POS=ADV`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `POS=SCONJ`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `POS=VERB\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PART`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Number=Plur\|POS=PROPN`, `POS=PRON\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=NUM`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADP`, `Gender=Neut\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=SCONJ\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=X`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=SPACE`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=AUX\|VerbForm=Inf`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=AUX\|VerbForm=Part`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `Degree=Sup\|POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Fem\|POS=NOUN`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=PROPN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Neut\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Masc\|POS=NOUN`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|POS=PROPN`, `Case=Gen\|Definite=Def\|POS=DET\|PronType=Art`, `Case=Gen\|POS=PROPN`, `Case=Acc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|POS=PRON\|PronType=Dem`, `Definite=Ind\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Neut\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, _(truncated: full list in pipeline meta)_ | | **`parser`** | `ROOT`, `ac`, `adc`, `ag`, `ams`, `app`, `avc`, `cc`, `cd`, `cj`, `cm`, `cp`, `cvc`, `da`, `dep`, `dm`, `ep`, `ju`, `mnr`, `mo`, `ng`, `nk`, `nmc`, `oa`, `oc`, `og`, `op`, `par`, `pd`, `pg`, `ph`, `pm`, `pnc`, `punct`, `rc`, `re`, `rs`, `sb`, `sbp`, `svp`, `uc`, `vo` | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details>
7d07b4c5a3eb18900139916435b98c2a
mit
['spacy', 'token-classification']
false
Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.96 | | `TOKEN_P` | 99.92 | | `TOKEN_R` | 99.90 | | `TOKEN_F` | 99.91 | | `TAG_ACC` | 97.96 | | `POS_ACC` | 98.41 | | `MORPH_ACC` | 92.06 | | `MORPH_MICRO_P` | 96.01 | | `MORPH_MICRO_R` | 95.99 | | `MORPH_MICRO_F` | 96.00 | | `SENTS_P` | 95.18 | | `SENTS_R` | 96.48 | | `SENTS_F` | 95.41 | | `DEP_UAS` | 92.66 | | `DEP_LAS` | 90.78 | | `LEMMA_ACC` | 97.91 | | `ENTS_P` | 85.27 | | `ENTS_R` | 84.44 | | `ENTS_F` | 84.85 |
d61b6b65d83b4ed7fd3182e269077f99
cc-by-4.0
['translation', 'opus-mt-tc']
false
opus-mt-tc-base-tr-uk Neural machine translation model for translating from Turkish (tr) to Ukrainian (uk). 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", } ```
0429aa4f917a3e8f6198f03cbb812fca
cc-by-4.0
['translation', 'opus-mt-tc']
false
Model info * Release: 2022-03-07 * source language(s): * target language(s): ukr * model: transformer-align * data: opusTCv20210807+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pbt_transformer-align_2022-03-07.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opusTCv20210807+pbt_transformer-align_2022-03-07.zip) * more information released models: [OPUS-MT tur-ukr README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-ukr/README.md)
3fa61d8af2e7fa0d17919b95ee2713e2
cc-by-4.0
['translation', 'opus-mt-tc']
false
Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "1000 yen yeterli mi?", "Zürih, İsviçre'de bir şehirdir." ] model_name = "pytorch-models/opus-mt-tc-base-tr-uk" 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) )
04d62f29071af33dbf53550c7d213e71
cc-by-4.0
['translation', 'opus-mt-tc']
false
Цюрих - місто в Швейцарії. ``` 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-base-tr-uk") print(pipe("1000 yen yeterli mi?"))
2f859f29cfd1b56515f1f0651e7b4e37
cc-by-4.0
['translation', 'opus-mt-tc']
false
Benchmarks * test set translations: [opusTCv20210807+pbt_transformer-align_2022-03-07.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opusTCv20210807+pbt_transformer-align_2022-03-07.test.txt) * test set scores: [opusTCv20210807+pbt_transformer-align_2022-03-07.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-ukr/opusTCv20210807+pbt_transformer-align_2022-03-07.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU |
1bea2412dc4cde6314adc11c2e3508a2
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2243 - Accuracy: 0.925 - F1: 0.9251
359de735d4742117c6e5329f04688264
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.866 | 1.0 | 250 | 0.3365 | 0.896 | 0.8905 | | 0.2626 | 2.0 | 500 | 0.2243 | 0.925 | 0.9251 |
4ceced0063893be2bdb08b6c38f88eae
apache-2.0
['thai', 'token-classification', 'pos', 'dependency-parsing']
false
Model Description This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-base-thai](https://huggingface.co/KoichiYasuoka/deberta-base-thai).
25ee7b657c25dd0f360011c65801081d
apache-2.0
['thai', 'token-classification', 'pos', 'dependency-parsing']
false
How to Use ```py class UDgoeswith(object): def __init__(self,bert): from transformers import AutoTokenizer,AutoModelForTokenClassification self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForTokenClassification.from_pretrained(bert) def __call__(self,text): import numpy,torch,ufal.chu_liu_edmonds w=self.tokenizer(text,return_offsets_mapping=True) v=w["input_ids"] x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)] with torch.no_grad(): e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:] r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())] e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan) g=self.model.config.label2id["X|_|goeswith"] r=numpy.tri(e.shape[0]) for i in range(e.shape[0]): for j in range(i+2,e.shape[1]): r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1 e[:,:,g]+=numpy.where(r==0,0,numpy.nan) m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan) m[1:,1:]=numpy.nanmax(e,axis=2).transpose() p=numpy.zeros(m.shape) p[1:,1:]=numpy.nanargmax(e,axis=2).transpose() for i in range(1,m.shape[0]): m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] if [0 for i in h if i==0]!=[0]: m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan) m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] u="
ff0116ac72525445b64015b41f9fd65b
apache-2.0
['thai', 'token-classification', 'pos', 'dependency-parsing']
false
text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=UDgoeswith("KoichiYasuoka/deberta-base-thai-ud-goeswith") print(nlp("หลายหัวดีกว่าหัวเดียว")) ``` with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/). Or without ufal.chu-liu-edmonds: ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-base-thai-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") print(nlp("หลายหัวดีกว่าหัวเดียว")) ```
8f36f06f16bfe367c651744a6859c3e8
mit
['generated_from_trainer']
false
bert_base_tcm_no_objeto_0.8 This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0076 - Criterio Julgamento Precision: 0.7444 - Criterio Julgamento Recall: 0.8684 - Criterio Julgamento F1: 0.8016 - Criterio Julgamento Number: 114 - Data Sessao Precision: 0.7297 - Data Sessao Recall: 0.9153 - Data Sessao F1: 0.8120 - Data Sessao Number: 59 - Modalidade Licitacao Precision: 0.9412 - Modalidade Licitacao Recall: 0.9697 - Modalidade Licitacao F1: 0.9552 - Modalidade Licitacao Number: 462 - Numero Exercicio Precision: 0.9018 - Numero Exercicio Recall: 0.9619 - Numero Exercicio F1: 0.9309 - Numero Exercicio Number: 210 - Valor Objeto Precision: 0.7778 - Valor Objeto Recall: 0.8537 - Valor Objeto F1: 0.8140 - Valor Objeto Number: 41 - Overall Precision: 0.8803 - Overall Recall: 0.9458 - Overall F1: 0.9119 - Overall Accuracy: 0.9983
2635cadda694fb7d6c4ba257e8f05f60
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: 10.0
052136068ca96cd9fac1521cfc86ed47
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.012 | 1.0 | 2863 | 0.0099 | 0.7059 | 0.8421 | 0.7680 | 114 | 0.7013 | 0.9153 | 0.7941 | 59 | 0.9366 | 0.9589 | 0.9476 | 462 | 0.9136 | 0.9571 | 0.9349 | 210 | 0.5902 | 0.8780 | 0.7059 | 41 | 0.8583 | 0.9368 | 0.8958 | 0.9974 | | 0.0095 | 2.0 | 5726 | 0.0076 | 0.8095 | 0.8947 | 0.8500 | 114 | 0.6935 | 0.7288 | 0.7107 | 59 | 0.9346 | 0.9589 | 0.9466 | 462 | 0.9054 | 0.9571 | 0.9306 | 210 | 0.8409 | 0.9024 | 0.8706 | 41 | 0.8901 | 0.9323 | 0.9107 | 0.9981 | | 0.005 | 3.0 | 8589 | 0.0091 | 0.7574 | 0.9035 | 0.8240 | 114 | 0.6471 | 0.9322 | 0.7639 | 59 | 0.9371 | 0.9675 | 0.9521 | 462 | 0.9091 | 0.9524 | 0.9302 | 210 | 0.7660 | 0.8780 | 0.8182 | 41 | 0.8715 | 0.9492 | 0.9087 | 0.9978 | | 0.0042 | 4.0 | 11452 | 0.0076 | 0.7444 | 0.8684 | 0.8016 | 114 | 0.7297 | 0.9153 | 0.8120 | 59 | 0.9412 | 0.9697 | 0.9552 | 462 | 0.9018 | 0.9619 | 0.9309 | 210 | 0.7778 | 0.8537 | 0.8140 | 41 | 0.8803 | 0.9458 | 0.9119 | 0.9983 | | 0.004 | 5.0 | 14315 | 0.0100 | 0.7373 | 0.7632 | 0.7500 | 114 | 0.7534 | 0.9322 | 0.8333 | 59 | 0.9124 | 0.9697 | 0.9402 | 462 | 0.9196 | 0.9810 | 0.9493 | 210 | 0.76 | 0.9268 | 0.8352 | 41 | 0.8724 | 0.9413 | 0.9055 | 0.9979 | | 0.0041 | 6.0 | 17178 | 0.0103 | 0.7377 | 0.7895 | 0.7627 | 114 | 0.75 | 0.8644 | 0.8031 | 59 | 0.9492 | 0.9697 | 0.9593 | 462 | 0.92 | 0.9857 | 0.9517 | 210 | 0.7872 | 0.9024 | 0.8409 | 41 | 0.8919 | 0.9402 | 0.9154 | 0.9980 | | 0.002 | 7.0 | 20041 | 0.0092 | 0.7984 | 0.8684 | 0.8319 | 114 | 0.68 | 0.8644 | 0.7612 | 59 | 0.9471 | 0.9697 | 0.9583 | 462 | 0.9196 | 0.9810 | 0.9493 | 210 | 0.7872 | 0.9024 | 0.8409 | 41 | 0.8918 | 0.9492 | 0.9196 | 0.9983 | | 0.0014 | 8.0 | 22904 | 0.0100 | 0.8033 | 0.8596 | 0.8305 | 114 | 0.7612 | 0.8644 | 0.8095 | 59 | 0.9532 | 0.9697 | 0.9614 | 462 | 0.9186 | 0.9667 | 0.9420 | 210 | 0.8222 | 0.9024 | 0.8605 | 41 | 0.9049 | 0.9447 | 0.9244 | 0.9983 | | 0.0015 | 9.0 | 25767 | 0.0108 | 0.7787 | 0.8333 | 0.8051 | 114 | 0.7067 | 0.8983 | 0.7910 | 59 | 0.9513 | 0.9719 | 0.9615 | 462 | 0.9107 | 0.9714 | 0.9401 | 210 | 0.8409 | 0.9024 | 0.8706 | 41 | 0.8943 | 0.9458 | 0.9194 | 0.9984 | | 0.0008 | 10.0 | 28630 | 0.0112 | 0.7934 | 0.8421 | 0.8170 | 114 | 0.7222 | 0.8814 | 0.7939 | 59 | 0.9533 | 0.9719 | 0.9625 | 462 | 0.9193 | 0.9762 | 0.9469 | 210 | 0.8409 | 0.9024 | 0.8706 | 41 | 0.9012 | 0.9470 | 0.9235 | 0.9984 |
5fbcb1903d473ae8096c788ca042af37
cc-by-sa-4.0
[]
false
electra-base-cyberbullying This is a BERT Base model for the Japanese language finetuned for automatic cyberbullying detection. The model was based on [daigo's BERT Base for Japanese sentiment analysis](https://huggingface.co/daigo/bert-base-japanese-sentiment), and later finetuned on a balanced dataset created by unifying two datasets, namely "Harmful BBS Japanese comments dataset" and "Twitter Japanese cyberbullying dataset".
64f0405d1a9495e4fd27c8ec15ce5f97
cc-by-sa-4.0
[]
false
Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
535cbc01ecb038f49eebbb99d27cb222
cc-by-sa-4.0
[]
false
Citations Please, cite this model using the following citation. ``` @inproceedings{tanabe2022bert-base-cyberbullying, title={北見工業大学 テキスト情報処理研究室 BERT Base ネットいじめ検出モデル (Daigo ver.)}, author={田邊　威裕　and プタシンスキ ミハウ and エロネン ユーソ and 桝井 文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/bert-base-japanese-sentiment-cyberbullying" } ```
44179d4ff361639fe89e2020d887868a
cc-by-sa-4.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: 5
4e77918734c6ce7ae9a9b6b2f6e9c998
apache-2.0
['PyTorch', 'Transformers']
false
SbertPuncCase SbertPuncCase - модель восстановления пунктуации и регистра для русского языка. Модель способна расставлять точки, запятые и знаки вопроса; определять регистр - слово в нижнем регистре, слово с первой буквой в верхнем регистре, слово в верхнем регистре. Модель разработана для восстановления текста после распознавания речи, поэтому работает со строками в нижнем регистре. В основу модели легла [sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru). В качестве обучающих данных использованы текстовые расшифровки интервью.
acd46f1f92176c14428f770f7329fc94
apache-2.0
['PyTorch', 'Transformers']
false
Как это работает 1. Текст переводится в нижний регистр и разбивается на слова. 2. Слова разделяются на токены. 3. Модель (по аналогии с задачей NER) предсказывает класс для каждого токена. Классификация на 12 классов: 3+1 знака препинания * 3 варианта регистра. 4. Функция декодировки восстанавливает текст соответственно предсказанным классам.
8a95a25cd5d20f95e7c236df218a8903
apache-2.0
['PyTorch', 'Transformers']
false
Как использовать Код модели находится в файле `sbert-punc-case-ru/sbertpunccase.py`. Для быстрой установки можно воспользоваться командой: ``` pip install git+https://huggingface.co/kontur-ai/sbert_punc_case_ru ``` Использование модели: ``` from sbert_punc_case_ru import SbertPuncCase model = SbertPuncCase() model.punctuate("sbert punc case расставляет точки запятые и знаки вопроса вам нравится") ```
9bc44a5a19def9b1228205f12f4ccbd1
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0
968978355f232be53eded9ab3b43da61
mit
[]
false
PGT PGT is a GPT-2 prompt-based model trained to facilitate 3 patent generation-related tasks, namely: *part-of-patent generation*, *part-of-patent editing* and *patent coherence check*. For more information about the dataset and the training procedure with refer the reader to [our paper](https://openreview.net/pdf?id=dLHtwZKvJmE). The task specification is taken place by appending a short sentence at the end of a given input. The general format is: `input <|sep|> task specific prompt <|sep|>` In all cases, the generated output ends with the special token <|endoftext|> to facilitate postprocessing.
3d0a54a43654f1f7f8371e685954324a
mit
[]
false
Supported tasks **Part-of-patent generation** attempts to generate a part of a patent given as input another, already existing part of it. The model has been trained to perform title-to-abstract, abstract-to-claim as well as their inverse generations. For the claim case, the model was only exposed to independent claims during the training. Input example for part-of-patent generation for the abstract-to-title case: `An interesting patent abstract. <|sep|> Given the above abstract, suggest a title <|sep|>` **Part-of-patent editing** attempts to suggest alternatives for some highlighted parts of a patent abstract or claim. These parts are defined in the input with the special [MASK] token. The expected size of these masked parts can be from a single word to a small phrase. If more than one masks are given in the input, then the generated suggestions are distinguished in the output but the special <|mask_sep|> token. Input example for part-of-patent editing working on a claim input: `An interesting patent claim with a [MASK] part. <|sep|> Replace the [MASK] tokens in the above claim <|sep|>` The **coherence check** assesses the quality of a patent by examining whether to given parts of a patent could belong to the same patent in terms of content and syntax. The input patent parts can be title, abstract or claim. The expected output is Yes or No. Input example for the coherence check task having as input a title and a claim: `A patent title <|sep|> An interesting patent claim. <|sep|> Do the above title and claim belong to the same patent? <|sep|>"` Further prompts and tasks can be tried in a zero-shot fashion. The model and the tasks are also integrated and available via the [GT4SD python library](https://github.com/GT4SD/gt4sd-core/blob/main/notebooks/explore-pgt.ipynb).
bd88aa1f4dab8dd3e8e5d5d9f1984018
mit
[]
false
Example A full example of part-of-patent generation ``` from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("christofid/pgt") model = AutoModelForCausalLM.from_pretrained("christofid/pgt") text = "Automated patent generation <|sep|> Given the above title, suggest an abstract <|sep|>" text_encoded = tokenizer.encode(text, return_tensors="pt") generated = model.generate(text_encoded, do_sample=True, top_k=50, num_return_sequences = 3, max_length=512) generated_text = [tokenizer.decode(case).split("<|endoftext|>")[0].strip() for case in generated] ```
50165c1434f9b47c681f9f25ea7fd874
mit
[]
false
BibTeX entry and citation info ``` @inproceedings{christofidellis2022pgt, title={PGT: a prompt based generative transformer for the patent domain}, author={Christofidellis, Dimitrios and Torres, Antonio Berrios and Dave, Ashish and Roveri, Manuel and Schmidt, Kristin and Swaminathan, Sarath and Vandierendonck, Hans and Zubarev, Dmitry and Manica, Matteo}, booktitle={ICML 2022 Workshop on Knowledge Retrieval and Language Models}, year={2022} } ```
4bfeb6dad123b0ecd17ef473dc341371
apache-2.0
['generated_from_trainer']
false
model_en This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8610 - Wer: 0.2641
fd736725ccd5ba7e1d3d93f5290bfdfc
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200
e62b33db5fe6ef74f64342c8832c18b8
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 6.3443 | 3.05 | 250 | 3.0966 | 1.0 | | 2.9847 | 6.1 | 500 | 3.0603 | 1.0 | | 2.9263 | 9.15 | 750 | 2.9131 | 1.0 | | 2.2584 | 12.19 | 1000 | 1.4318 | 0.6575 | | 1.2603 | 15.24 | 1250 | 1.1964 | 0.4994 | | 0.9182 | 18.29 | 1500 | 1.1494 | 0.4485 | | 0.7462 | 21.34 | 1750 | 1.2171 | 0.4357 | | 0.6129 | 24.39 | 2000 | 1.0557 | 0.3468 | | 0.5364 | 27.44 | 2250 | 1.1069 | 0.4222 | | 0.4607 | 30.48 | 2500 | 1.3270 | 0.3370 | | 0.4139 | 33.53 | 2750 | 1.1814 | 0.3658 | | 0.3587 | 36.58 | 3000 | 1.2423 | 0.3419 | | 0.321 | 39.63 | 3250 | 1.2931 | 0.3211 | | 0.2961 | 42.68 | 3500 | 1.1409 | 0.3315 | | 0.2635 | 45.73 | 3750 | 1.4537 | 0.3241 | | 0.2498 | 48.78 | 4000 | 1.2643 | 0.3192 | | 0.2352 | 51.82 | 4250 | 1.2789 | 0.3278 | | 0.2193 | 54.87 | 4500 | 1.4220 | 0.3021 | | 0.2068 | 57.92 | 4750 | 1.3567 | 0.3713 | | 0.2055 | 60.97 | 5000 | 1.5375 | 0.3051 | | 0.198 | 64.02 | 5250 | 1.2676 | 0.2782 | | 0.1835 | 67.07 | 5500 | 1.3905 | 0.2825 | | 0.1655 | 70.12 | 5750 | 1.7000 | 0.2978 | | 0.1677 | 73.17 | 6000 | 1.4250 | 0.2812 | | 0.1522 | 76.22 | 6250 | 1.4220 | 0.2941 | | 0.1522 | 79.27 | 6500 | 1.5195 | 0.3021 | | 0.1344 | 82.32 | 6750 | 1.3749 | 0.2996 | | 0.1298 | 85.36 | 7000 | 1.6663 | 0.2849 | | 0.1293 | 88.41 | 7250 | 1.4564 | 0.2892 | | 0.1264 | 91.46 | 7500 | 1.4373 | 0.2935 | | 0.1243 | 94.51 | 7750 | 1.6572 | 0.2972 | | 0.1141 | 97.56 | 8000 | 1.4936 | 0.2892 | | 0.1086 | 100.61 | 8250 | 1.5231 | 0.2868 | | 0.1056 | 103.65 | 8500 | 1.3733 | 0.2763 | | 0.098 | 106.7 | 8750 | 1.4887 | 0.2923 | | 0.0984 | 109.75 | 9000 | 1.3779 | 0.2923 | | 0.0916 | 112.8 | 9250 | 1.4868 | 0.2604 | | 0.0881 | 115.85 | 9500 | 1.7991 | 0.2996 | | 0.0846 | 118.9 | 9750 | 1.5845 | 0.2849 | | 0.0861 | 121.95 | 10000 | 1.6684 | 0.2794 | | 0.0806 | 124.99 | 10250 | 1.5774 | 0.3039 | | 0.0822 | 128.05 | 10500 | 1.5928 | 0.2886 | | 0.0788 | 131.1 | 10750 | 1.6158 | 0.2880 | | 0.0704 | 134.15 | 11000 | 1.7679 | 0.2941 | | 0.0721 | 137.19 | 11250 | 1.7055 | 0.2629 | | 0.0723 | 140.24 | 11500 | 1.5473 | 0.2653 | | 0.0676 | 143.29 | 11750 | 1.8963 | 0.2745 | | 0.0665 | 146.34 | 12000 | 1.6367 | 0.2739 | | 0.0618 | 149.39 | 12250 | 1.6757 | 0.2745 | | 0.0595 | 152.44 | 12500 | 1.5900 | 0.2745 | | 0.056 | 155.48 | 12750 | 1.5362 | 0.2794 | | 0.0587 | 158.53 | 13000 | 1.4616 | 0.2684 | | 0.0519 | 161.58 | 13250 | 1.6867 | 0.2549 | | 0.0569 | 164.63 | 13500 | 1.8294 | 0.2574 | | 0.0497 | 167.68 | 13750 | 1.7844 | 0.2868 | | 0.0531 | 170.73 | 14000 | 1.7564 | 0.2770 | | 0.0489 | 173.78 | 14250 | 1.5811 | 0.2629 | | 0.0524 | 176.82 | 14500 | 1.6925 | 0.2684 | | 0.0431 | 179.87 | 14750 | 1.7236 | 0.2653 | | 0.0457 | 182.92 | 15000 | 1.7460 | 0.2512 | | 0.045 | 185.97 | 15250 | 1.8096 | 0.2610 | | 0.0402 | 189.02 | 15500 | 1.8795 | 0.2635 | | 0.0529 | 192.07 | 15750 | 1.8310 | 0.2616 | | 0.0396 | 195.12 | 16000 | 1.8380 | 0.2635 | | 0.0432 | 198.17 | 16250 | 1.8610 | 0.2641 |
d7375c8a2624223cf03196dbf2aeb792
apache-2.0
['i-dont-know-what-im-doing', 'generated_from_trainer']
false
Whisper Small sv-SE - Lab 2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3278 - Wer: 19.7736
e1b673698aeba1e9b1af09e3d32ff107
apache-2.0
['i-dont-know-what-im-doing', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP
48825ff541ec1614b48f8bdc5ed3e2d1
apache-2.0
['i-dont-know-what-im-doing', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1378 | 1.29 | 1000 | 0.2953 | 21.4165 | | 0.0475 | 2.59 | 2000 | 0.2913 | 20.2495 | | 0.0186 | 3.88 | 3000 | 0.3027 | 19.8193 | | 0.0042 | 5.17 | 4000 | 0.3278 | 19.7736 |
e80dd75f7c987a29fac67d497ab3fba0
apache-2.0
['generated_from_trainer']
false
wav2vec2-base-timit-moaiz_exp2_new This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6849 - Wer: 0.5396
1d6ee293db7124453a8e1a1a4bc3189c
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP
00c7b6fef471e056a9b134e2572bb76d
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.1266 | 13.89 | 500 | 1.0233 | 0.7034 | | 0.5928 | 27.78 | 1000 | 0.6849 | 0.5396 |
14cca75ac4f14a46cf30daf69208f2c9
mit
['generated_from_trainer']
false
predict-perception-bert-blame-assassin This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5128 - Rmse: 1.0287 - Rmse Blame::a L'assassino: 1.0287 - Mae: 0.8883 - Mae Blame::a L'assassino: 0.8883 - R2: 0.5883 - R2 Blame::a L'assassino: 0.5883 - Cos: 0.6522 - Pair: 0.0 - Rank: 0.5 - Neighbors: 0.5795 - Rsa: nan
e28e830aba482556f8f57bed8e7c80ba
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 20 - eval_batch_size: 8 - seed: 1996 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30
207b40bde45c1b681b0e92f1d5ab2840
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a L'assassino | Mae | Mae Blame::a L'assassino | R2 | R2 Blame::a L'assassino | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------:|:------:|:------------------------:|:------:|:-----------------------:|:------:|:----:|:----:|:---------:|:---:| | 1.0184 | 1.0 | 15 | 1.2219 | 1.5879 | 1.5879 | 1.4308 | 1.4308 | 0.0191 | 0.0191 | 0.3913 | 0.0 | 0.5 | 0.3781 | nan | | 0.9214 | 2.0 | 30 | 1.0927 | 1.5017 | 1.5017 | 1.3634 | 1.3634 | 0.1227 | 0.1227 | 0.5652 | 0.0 | 0.5 | 0.4512 | nan | | 0.7809 | 3.0 | 45 | 0.8206 | 1.3013 | 1.3013 | 1.1808 | 1.1808 | 0.3412 | 0.3412 | 0.4783 | 0.0 | 0.5 | 0.3819 | nan | | 0.6593 | 4.0 | 60 | 0.5894 | 1.1029 | 1.1029 | 1.0145 | 1.0145 | 0.5268 | 0.5268 | 0.7391 | 0.0 | 0.5 | 0.6408 | nan | | 0.4672 | 5.0 | 75 | 0.4759 | 0.9910 | 0.9910 | 0.8868 | 0.8868 | 0.6180 | 0.6180 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.3356 | 6.0 | 90 | 0.4220 | 0.9332 | 0.9332 | 0.8083 | 0.8083 | 0.6612 | 0.6612 | 0.6522 | 0.0 | 0.5 | 0.4249 | nan | | 0.2782 | 7.0 | 105 | 0.4477 | 0.9612 | 0.9612 | 0.8046 | 0.8046 | 0.6406 | 0.6406 | 0.6522 | 0.0 | 0.5 | 0.6101 | nan | | 0.2075 | 8.0 | 120 | 0.4389 | 0.9518 | 0.9518 | 0.8050 | 0.8050 | 0.6476 | 0.6476 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.1725 | 9.0 | 135 | 0.4832 | 0.9985 | 0.9985 | 0.8356 | 0.8356 | 0.6121 | 0.6121 | 0.7391 | 0.0 | 0.5 | 0.6616 | nan | | 0.1642 | 10.0 | 150 | 0.4368 | 0.9494 | 0.9494 | 0.8060 | 0.8060 | 0.6493 | 0.6493 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.1172 | 11.0 | 165 | 0.4538 | 0.9677 | 0.9677 | 0.8174 | 0.8174 | 0.6357 | 0.6357 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.104 | 12.0 | 180 | 0.4672 | 0.9819 | 0.9819 | 0.8384 | 0.8384 | 0.6249 | 0.6249 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0822 | 13.0 | 195 | 0.4401 | 0.9530 | 0.9530 | 0.8107 | 0.8107 | 0.6467 | 0.6467 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0755 | 14.0 | 210 | 0.4464 | 0.9598 | 0.9598 | 0.8251 | 0.8251 | 0.6416 | 0.6416 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0801 | 15.0 | 225 | 0.4834 | 0.9988 | 0.9988 | 0.8604 | 0.8604 | 0.6119 | 0.6119 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.053 | 16.0 | 240 | 0.4846 | 1.0001 | 1.0001 | 0.8651 | 0.8651 | 0.6109 | 0.6109 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0573 | 17.0 | 255 | 0.4970 | 1.0128 | 1.0128 | 0.8743 | 0.8743 | 0.6010 | 0.6010 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0571 | 18.0 | 270 | 0.4803 | 0.9956 | 0.9956 | 0.8503 | 0.8503 | 0.6144 | 0.6144 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.0483 | 19.0 | 285 | 0.4936 | 1.0093 | 1.0093 | 0.8740 | 0.8740 | 0.6037 | 0.6037 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.0414 | 20.0 | 300 | 0.5138 | 1.0297 | 1.0297 | 0.8943 | 0.8943 | 0.5875 | 0.5875 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.0513 | 21.0 | 315 | 0.5240 | 1.0399 | 1.0399 | 0.9050 | 0.9050 | 0.5793 | 0.5793 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0499 | 22.0 | 330 | 0.5275 | 1.0434 | 1.0434 | 0.9048 | 0.9048 | 0.5765 | 0.5765 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0423 | 23.0 | 345 | 0.5350 | 1.0508 | 1.0508 | 0.8872 | 0.8872 | 0.5705 | 0.5705 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.0447 | 24.0 | 360 | 0.4963 | 1.0120 | 1.0120 | 0.8754 | 0.8754 | 0.6016 | 0.6016 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0364 | 25.0 | 375 | 0.5009 | 1.0167 | 1.0167 | 0.8809 | 0.8809 | 0.5979 | 0.5979 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.0412 | 26.0 | 390 | 0.5060 | 1.0219 | 1.0219 | 0.8781 | 0.8781 | 0.5938 | 0.5938 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.0297 | 27.0 | 405 | 0.5027 | 1.0185 | 1.0185 | 0.8838 | 0.8838 | 0.5964 | 0.5964 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0416 | 28.0 | 420 | 0.5071 | 1.0230 | 1.0230 | 0.8867 | 0.8867 | 0.5929 | 0.5929 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan | | 0.0327 | 29.0 | 435 | 0.5124 | 1.0283 | 1.0283 | 0.8883 | 0.8883 | 0.5887 | 0.5887 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan | | 0.0383 | 30.0 | 450 | 0.5128 | 1.0287 | 1.0287 | 0.8883 | 0.8883 | 0.5883 | 0.5883 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
c1a0c629bb1b9113018146d1f6fe9b5d
apache-2.0
['automatic-speech-recognition', 'nl']
false
exp_w2v2t_nl_unispeech-ml_s498 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
73e501bc76b0fdd96d6322ae5ddfba58
apache-2.0
['generated_from_trainer']
false
emotion_trained_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9051 - F1: 0.7302
dbe790aa4d2184f9cb4d17446ebd6536
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6.961635072722524e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 1234567 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4
37641cfe4016593955a9df48f77c7859
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 204 | 0.6480 | 0.7231 | | No log | 2.0 | 408 | 0.6114 | 0.7403 | | 0.5045 | 3.0 | 612 | 0.7592 | 0.7311 | | 0.5045 | 4.0 | 816 | 0.9051 | 0.7302 |
3f251b253e6d94ab94009ce1f0b47420
apache-2.0
['automatic-speech-recognition', 'de']
false
exp_w2v2t_de_r-wav2vec2_s460 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
6372d984ab043e72b9d825187f779bf9
mit
['audio', 'music', 'generation', 'tensorflow']
false
Model provided by: comehu Pretrained musika_sm64_ost model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706).
52635da29cf8736c271517b2c9a72fa0
mit
['audio', 'music', 'generation', 'tensorflow']
false
Model description This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio. The generator has a context window of about 12 seconds of audio.
80cb400058ec5287b873bed4564bb8c1
apache-2.0
['generated_from_trainer']
false
bert-large-uncased-finetuned-docvqa This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.6367
0ed6f5582b3994c8a4a25f819e1bdb72
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 250500 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6
0a8595fadfe5ace39573437cb019451c
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.5228 | 0.05 | 1000 | 2.6645 | | 2.4909 | 0.1 | 2000 | 2.8985 | | 2.1679 | 0.16 | 3000 | 2.3551 | | 1.9451 | 0.21 | 4000 | 2.2226 | | 1.6814 | 0.26 | 5000 | 2.1590 | | 1.8868 | 0.31 | 6000 | 2.6197 | | 1.6618 | 0.36 | 7000 | 2.3632 | | 1.8313 | 0.41 | 8000 | 2.4519 | | 1.7017 | 0.47 | 9000 | 2.2682 | | 1.8169 | 0.52 | 10000 | 2.4486 | | 1.7074 | 0.57 | 11000 | 2.3862 | | 1.7674 | 0.62 | 12000 | 2.1801 | | 1.8134 | 0.67 | 13000 | 2.3032 | | 1.8334 | 0.73 | 14000 | 2.4205 | | 1.6819 | 0.78 | 15000 | 2.2398 | | 1.5846 | 0.83 | 16000 | 2.3834 | | 1.6758 | 0.88 | 17000 | 1.9683 | | 1.6303 | 0.93 | 18000 | 2.3297 | | 1.5652 | 0.98 | 19000 | 2.0581 | | 1.3045 | 1.04 | 20000 | 2.4950 | | 1.2393 | 1.09 | 21000 | 2.6622 | | 1.1526 | 1.14 | 22000 | 2.3749 | | 1.2631 | 1.19 | 23000 | 2.3915 | | 1.1846 | 1.24 | 24000 | 2.2592 | | 1.2731 | 1.3 | 25000 | 2.4239 | | 1.3057 | 1.35 | 26000 | 2.2920 | | 1.134 | 1.4 | 27000 | 2.3107 | | 1.2017 | 1.45 | 28000 | 2.4271 | | 1.2202 | 1.5 | 29000 | 2.1814 | | 1.2179 | 1.56 | 30000 | 2.3365 | | 1.2359 | 1.61 | 31000 | 2.1256 | | 1.1964 | 1.66 | 32000 | 2.1720 | | 1.269 | 1.71 | 33000 | 2.4363 | | 1.1812 | 1.76 | 34000 | 2.2372 | | 1.2187 | 1.81 | 35000 | 2.2318 | | 1.1805 | 1.87 | 36000 | 2.3693 | | 1.1458 | 1.92 | 37000 | 2.5128 | | 1.1958 | 1.97 | 38000 | 2.1311 | | 0.8924 | 2.02 | 39000 | 2.4635 | | 0.869 | 2.07 | 40000 | 2.8231 | | 0.8333 | 2.13 | 41000 | 2.6762 | | 0.9194 | 2.18 | 42000 | 2.4588 | | 0.8089 | 2.23 | 43000 | 2.6443 | | 0.8612 | 2.28 | 44000 | 2.4300 | | 0.7981 | 2.33 | 45000 | 2.7418 | | 0.9765 | 2.38 | 46000 | 2.6543 | | 0.8646 | 2.44 | 47000 | 2.5990 | | 1.0316 | 2.49 | 48000 | 2.4625 | | 0.9862 | 2.54 | 49000 | 2.4691 | | 1.027 | 2.59 | 50000 | 2.4156 | | 0.9412 | 2.64 | 51000 | 2.4204 | | 0.9353 | 2.7 | 52000 | 2.4933 | | 0.9509 | 2.75 | 53000 | 2.4708 | | 0.9351 | 2.8 | 54000 | 2.5351 | | 0.9968 | 2.85 | 55000 | 2.2506 | | 1.025 | 2.9 | 56000 | 2.6317 | | 1.627 | 2.95 | 57000 | 2.7843 | | 0.9294 | 3.01 | 58000 | 2.9396 | | 0.6043 | 3.06 | 59000 | 3.1560 | | 0.7903 | 3.11 | 60000 | 2.8330 | | 0.7373 | 3.16 | 61000 | 2.9422 | | 0.6499 | 3.21 | 62000 | 3.0948 | | 0.6411 | 3.27 | 63000 | 2.7900 | | 0.625 | 3.32 | 64000 | 2.5268 | | 0.6264 | 3.37 | 65000 | 2.8701 | | 0.6143 | 3.42 | 66000 | 3.2544 | | 0.6286 | 3.47 | 67000 | 2.6208 | | 0.739 | 3.53 | 68000 | 2.8107 | | 0.5981 | 3.58 | 69000 | 2.8073 | | 0.6502 | 3.63 | 70000 | 2.6293 | | 0.6548 | 3.68 | 71000 | 2.9501 | | 0.7243 | 3.73 | 72000 | 2.7917 | | 0.598 | 3.78 | 73000 | 2.9341 | | 0.6159 | 3.84 | 74000 | 2.7629 | | 0.5905 | 3.89 | 75000 | 2.6441 | | 0.6393 | 3.94 | 76000 | 2.6660 | | 0.677 | 3.99 | 77000 | 2.7616 | | 0.3281 | 4.04 | 78000 | 3.6873 | | 0.4524 | 4.1 | 79000 | 3.3441 | | 0.3994 | 4.15 | 80000 | 3.3129 | | 0.4686 | 4.2 | 81000 | 3.1813 | | 0.5293 | 4.25 | 82000 | 2.9088 | | 0.3961 | 4.3 | 83000 | 3.0765 | | 0.4406 | 4.35 | 84000 | 3.1254 | | 0.401 | 4.41 | 85000 | 3.2415 | | 0.4594 | 4.46 | 86000 | 3.0691 | | 0.4523 | 4.51 | 87000 | 3.0493 | | 0.4719 | 4.56 | 88000 | 3.1352 | | 0.4895 | 4.61 | 89000 | 2.8991 | | 0.423 | 4.67 | 90000 | 3.1738 | | 0.3984 | 4.72 | 91000 | 3.1862 | | 0.4206 | 4.77 | 92000 | 3.1213 | | 0.4587 | 4.82 | 93000 | 3.0030 | | 0.381 | 4.87 | 94000 | 3.3218 | | 0.4138 | 4.92 | 95000 | 3.1529 | | 0.4003 | 4.98 | 96000 | 3.1375 | | 0.2098 | 5.03 | 97000 | 3.7443 | | 0.2334 | 5.08 | 98000 | 3.7359 | | 0.2534 | 5.13 | 99000 | 3.7814 | | 0.3067 | 5.18 | 100000 | 3.7128 | | 0.2363 | 5.24 | 101000 | 3.6091 | | 0.2652 | 5.29 | 102000 | 3.4015 | | 0.3311 | 5.34 | 103000 | 3.4793 | | 0.2344 | 5.39 | 104000 | 3.6792 | | 0.2741 | 5.44 | 105000 | 3.5385 | | 0.2896 | 5.5 | 106000 | 3.8118 | | 0.2071 | 5.55 | 107000 | 3.8690 | | 0.3023 | 5.6 | 108000 | 3.7087 | | 0.3299 | 5.65 | 109000 | 3.4925 | | 0.1943 | 5.7 | 110000 | 3.6739 | | 0.2488 | 5.75 | 111000 | 3.7614 | | 0.3138 | 5.81 | 112000 | 3.5156 | | 0.2555 | 5.86 | 113000 | 3.6056 | | 0.2918 | 5.91 | 114000 | 3.6533 | | 0.2751 | 5.96 | 115000 | 3.6367 |
84f58a0529466564395f1ceedd46bc34
apache-2.0
['translation']
false
opus-mt-ig-fi * source languages: ig * target languages: fi * OPUS readme: [ig-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ig-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/ig-fi/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ig-fi/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ig-fi/opus-2020-01-09.eval.txt)
b6ec9e830e95932c9c4e491c3e11e517
apache-2.0
['generated_from_trainer']
false
mbert-profane-final This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4464 - Accuracy: 0.8983 - Precision: 0.8135 - Recall: 0.8120 - F1: 0.8128
f89cbd1492332fbebc71aa40f1bf45f6
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10
1a1b032852afe9a796d9280207fe6e65
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.2313 | 0.9154 | 0.8687 | 0.8010 | 0.8294 | | 0.3077 | 2.0 | 592 | 0.2223 | 0.9125 | 0.8473 | 0.8205 | 0.8330 | | 0.3077 | 3.0 | 888 | 0.2137 | 0.9259 | 0.8784 | 0.8379 | 0.8563 | | 0.2102 | 4.0 | 1184 | 0.2334 | 0.9163 | 0.8483 | 0.8417 | 0.8449 | | 0.2102 | 5.0 | 1480 | 0.2737 | 0.9068 | 0.8305 | 0.8242 | 0.8273 | | 0.1533 | 6.0 | 1776 | 0.3214 | 0.8964 | 0.8034 | 0.8510 | 0.8239 | | 0.1092 | 7.0 | 2072 | 0.3409 | 0.9002 | 0.8115 | 0.8414 | 0.8252 | | 0.1092 | 8.0 | 2368 | 0.3849 | 0.9049 | 0.8322 | 0.8066 | 0.8185 | | 0.0775 | 9.0 | 2664 | 0.4408 | 0.8983 | 0.8113 | 0.8215 | 0.8162 | | 0.0775 | 10.0 | 2960 | 0.4464 | 0.8983 | 0.8135 | 0.8120 | 0.8128 |
a7d001c412e8348421a4d0e5cde870de
apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
restaurant_test_model This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the test_data dataset. It achieves the following results on the evaluation set: - Loss: 0.5435 - Wer: 78.5714
4eb360714f92f3beeff621b783628739
apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 40 - mixed_precision_training: Native AMP
e9bfd1d5aecb3a64d680a3f00cf705df
apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 10.0 | 10 | 2.2425 | 7.1429 | | No log | 20.0 | 20 | 0.6651 | 0.0 | | 2.4375 | 30.0 | 30 | 0.5776 | 35.7143 | | 2.4375 | 40.0 | 40 | 0.5435 | 78.5714 |
eaa073c375497fa89a66c355eda8f0e9
apache-2.0
['generated_from_trainer']
false
my_awesome_ko_en_model This model is a fine-tuned version of [KETI-AIR/ke-t5-small](https://huggingface.co/KETI-AIR/ke-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Bleu: 0.0 - Gen Len: 19.0
9b5193fdeaa53a00e8c7beb907d73f8b
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP
8876408f0ac77ea00bd81e35c878ee87
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:----:|:-------:| | No log | 1.0 | 67 | nan | 0.0 | 19.0 | | No log | 2.0 | 134 | nan | 0.0 | 19.0 |
6c5ef2eb65b8fdd4f46fa53786a160d8