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davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
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mit
[]
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:| | 0.9154 | 1.0 | 1812 | 0.8984 | 0.6785 | 0.6785 | 0.6383 | 0.6772 | 0.6785 | 0.6785 | | 0.8374 | 2.0 | 3624 | 0.8569 | 0.6957 | 0.6957 | 0.6529 | 0.6914 | 0.6957 | 0.6957 | | 0.7053 | 3.0 | 5436 | 0.8582 | 0.7019 | 0.7019 | 0.6594 | 0.6967 | 0.7019 | 0.7019 | | 0.7178 | 4.0 | 7248 | 0.8488 | 0.7030 | 0.7030 | 0.6662 | 0.7011 | 0.7030 | 0.7030 | | 0.6688 | 5.0 | 9060 | 0.8549 | 0.7059 | 0.7059 | 0.6683 | 0.7033 | 0.7059 | 0.7059 |
7e3cdf4d32d06bdd9cb250be6358d6b1
mit
[]
false
Validation evaluation | Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |----------------|----------------|----------------|-------------------| | PolicyBERTa-7d | 0.71 | 0.67 | 0.70 |
e72b60f0799f7fb16652418d8e03fe05
mit
[]
false
Test evaluation | Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |----------------|----------------|----------------|-------------------| | PolicyBERTa-7d | 0.65 | 0.60 | 0.65 |
927de2823271805dda7b18440cb223b7
mit
[]
false
Evaluation per category | Label | Validation F1-Score | Test F1-Score | |-----------------------------|---------------------|---------------| | external relations | 0.76 | 0.70 | | freedom and democracy | 0.61 | 0.55 | | political system | 0.55 | 0.55 | | economy | 0.74 | 0.67 | | welfare and quality of life | 0.77 | 0.72 | | fabric of society | 0.67 | 0.60 | | social groups | 0.58 | 0.41 |
93a968b3ddb98cf440d66f2296b29905
mit
[]
false
Evaluation based on saliency theory Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent. Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology. The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html
b3408747691d6c0402ab482b951adef9
mit
[]
false
measuring-parties-left-right-positions). But PolicyBERTa isn't fine-tuned to predict the rile-index, if you're interested in that, check [ManiBERT](https://huggingface.co/niksmer/ManiBERT) or [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE). In the following table, the predicted and original share of the individual policy domains are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original shares is 0.965. | Party-ID | Year | Type | Share external relations | Share freedom and democracy | Share political system | Share economy | Share welfare and quality of life | Share fabric of society | Share social groups | |--------------|-------------|---------------|--------------------------|-----------------------------|------------------------|----------------|-----------------------------------|-------------------------|---------------------| | 62320 | 2004 | Predicted | 7.1% | 4.8% | 13.2% | 20.3% | 35.2% | 9.6% | 9.8% | | | | Original | 10.2% | 2.5% | 13.7% | 23.8% | 31.7% | 11.6% | 6.4% | | 62320 | 2006 | Predicted | 2.9% | 4.7% | 16.4% | 18.9% | 38.3% | 11.9% | 6.9% | | | | Original | 5.6% | 5.0% | 15.8% | 20.7% | 38.7% | 9.3% | 4.9% | | 62320 | 2008 | Predicted | 6.8% | 4.7% | 6.2% | 24.7% | 38.3% | 10.3% | 9.0% | | | | Original | 5.6% | 3.7% | 8.2% | 33.1% | 29.5% | 11.7% | 4.3% | | 62420 | 2004 | Predicted | 9.7% | 3.5% | 14.5% | 24.7% | 34.8% | 8.5% | 4.3% | | | | Original | 12.6% | 1.3% | 18.8% | 23.0% | 33.2% | 9.0% | 2.0% | | 62420 | 2006 | Predicted | 9.5% | 2.2% | 7.9% | 27.8% | 34.8% | 9.2% | 8.7% | | | | Original | 10.6% | 2.5% | 9.6% | 29.7% | 33.1% | 8.3% | 6.2% | | 62420 | 2008 | Predicted | 0.7% | 0.5% | 3.5% | 41.7% | 46.4% | 3.7% | 3.5% | | | | Original | 2.0% | 0.2% | 4.4% | 33.3% | 45.9% | 7.7% | 6.4% | | 62623 | 2004 | Predicted | 7.1% | 11.4% | 24.5% | 17.6% | 21.5% | 13.6% | 4.3% | | | | Original | 8.4% | 6.7% | 28.8% | 17.4% | 18.7% | 15.5% | 4.5% | | 62623 | 2006 | Predicted | 5.6% | 8.5% | 23.6% | 15.6% | 14.8% | 24.3% | 7.6% | | | | Original | 5.0% | 8.9% | 22.2% | 17.4% | 17.2% | 25.7% | 3.6% | | 62623 | 2008 | Predicted | 5.0% | 4.4% | 12.2% | 33.1% | 21.9% | 17.5% | 5.9% | | | | Original | 5.6% | 2.2% | 11.6% | 37.8% | 17.8% | 20.9% | 4.1% | | 62110 | 2008 | Predicted | 10.0% | 3.1% | 6.8% | 22.7% | 41.3% | 10.1% | 6.0% | | | | Original | 13.4% | 3.3% | 7.7% | 26.9% | 35.6% | 8.9% | 4.3% |
8fda26d64610936e6474c1d1f3bfd91c
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-model-4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 3.1544
8cc4fd7e7a28b32bcb4599e710421b76
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4
eb9cb1ad92a026546a1f1755f0f7d1c0
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7789 | 1.0 | 554 | 2.0564 | | 1.4826 | 2.0 | 1108 | 2.0916 | | 0.7706 | 3.0 | 1662 | 2.4116 | | 0.3379 | 4.0 | 2216 | 3.1544 |
aa777084798c75a1e35b42bb9c4a1554
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1582
8665fefd6039780f5a49507998e6cd59
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2176 | 1.0 | 5533 | 1.1429 | | 0.9425 | 2.0 | 11066 | 1.1196 | | 0.7586 | 3.0 | 16599 | 1.1582 |
415246a3e61a48ec839cf2f800d835aa
apache-2.0
['generated_from_trainer']
false
new-test-model 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: 0.0962 - Precision: 0.9704 - Recall: 0.9766 - F1: 0.9735 - Accuracy: 0.9791
1f8facd2a69ed0e7d604ed608ae50ba9
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 151 | 0.1872 | 0.9295 | 0.9405 | 0.9349 | 0.9535 | | No log | 2.0 | 302 | 0.1417 | 0.9574 | 0.9652 | 0.9613 | 0.9679 | | No log | 3.0 | 453 | 0.1028 | 0.9676 | 0.9693 | 0.9684 | 0.9742 | | 0.3037 | 4.0 | 604 | 0.1063 | 0.9676 | 0.9696 | 0.9686 | 0.9743 | | 0.3037 | 5.0 | 755 | 0.0962 | 0.9704 | 0.9766 | 0.9735 | 0.9791 |
67c01a3e7c11ae3fc1a15acc9234df8c
apache-2.0
['super-image', 'image-super-resolution']
false
Densely Residual Laplacian Super-Resolution (DRLN) DRLN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Densely Residual Laplacian Super-resolution](https://arxiv.org/abs/1906.12021) by Anwar et al. (2020) and first released in [this repository](https://github.com/saeed-anwar/DRLN). The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling. ![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4](images/drln_4_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 4")
3128f4c3026e802526aa10290cfad7d1
apache-2.0
['super-image', 'image-super-resolution']
false
Model description Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately. This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
e347edecdbd5ad62f4480272d048ecf4
apache-2.0
['super-image', 'image-super-resolution']
false
How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import DrlnModel, ImageLoader from PIL import Image import requests url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg' image = Image.open(requests.get(url, stream=True).raw) model = DrlnModel.from_pretrained('eugenesiow/drln-bam', scale=2)
f5beb4fff1a6b1cf52488e8be4aed4d8
apache-2.0
['super-image', 'image-super-resolution']
false
Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, DrlnModel, DrlnConfig training_args = TrainingArguments( output_dir='./results',
6ee5e92828582b6eb42854ba990ffa10
apache-2.0
['super-image', 'image-super-resolution']
false
Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100) The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline. |Dataset |Scale |Bicubic |drln-bam | |--- |--- |--- |--- | |Set5 |2x |33.64/0.9292 |**38.23/0.9614** | |Set5 |3x |30.39/0.8678 |**35.3/0.9422** | |Set5 |4x |28.42/0.8101 |**32.49/0.8986** | |Set14 |2x |30.22/0.8683 |**33.95/0.9206** | |Set14 |3x |27.53/0.7737 |**31.27/0.8624** | |Set14 |4x |25.99/0.7023 |**28.94/0.7899** | |BSD100 |2x |29.55/0.8425 |**33.95/0.9269** | |BSD100 |3x |27.20/0.7382 |**29.78/0.8224** | |BSD100 |4x |25.96/0.6672 |**28.63/0.7686** | |Urban100 |2x |26.66/0.8408 |**32.81/0.9339** | |Urban100 |3x | |**29.82/0.8828** | |Urban100 |4x |23.14/0.6573 |**26.53/0.7991** | ![Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2](images/drln_2_4_compare.png "Comparing Bicubic upscaling against the models x4 upscaling on Set5 Image 2") You can find a notebook to easily run evaluation on pretrained models below: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
10d7df2a4ad3ea9ef920a951ec8533c0
apache-2.0
['super-image', 'image-super-resolution']
false
BibTeX entry and citation info ```bibtex @misc{wang2021bam, title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution}, author={Fanyi Wang and Haotian Hu and Cheng Shen}, year={2021}, eprint={2104.07566}, archivePrefix={arXiv}, primaryClass={eess.IV} } ``` ```bibtex @misc{anwar2019densely, title={Densely Residual Laplacian Super-Resolution}, author={Saeed Anwar and Nick Barnes}, year={2019}, eprint={1906.12021}, archivePrefix={arXiv}, primaryClass={eess.IV} } ```
3365eddaee8de77dbc52654f2df11faa
cc-by-4.0
['norwegian', 'bert']
false
Description NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway. This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years using a monolingual Norwegian vocabulary.
79f58f2ed875a4982788e0193927b6a5
cc-by-4.0
['norwegian', 'bert']
false
Intended use & limitations The 1.0 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on Github, see * https://github.com/NBAiLab/notram
d147fcbb453d2d8ab678e14d48b8df71
mit
['generated_from_trainer']
false
edos-2023-baseline-roberta-base-label_category This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0133 - F1: 0.5792
b295eb2f67b4606cbd3181d2ead76221
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.169 | 1.18 | 100 | 1.0580 | 0.2159 | | 0.9143 | 2.35 | 200 | 0.9283 | 0.5405 | | 0.7535 | 3.53 | 300 | 0.9387 | 0.5665 | | 0.6085 | 4.71 | 400 | 0.9574 | 0.5664 | | 0.53 | 5.88 | 500 | 1.0133 | 0.5792 |
9e496ac967fe80ab994dde524704ea77
creativeml-openrail-m
['text-to-image', 'stable-diffusion']
false
ppaattaass Dreambooth model trained by Brainergy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
6930ec033ec06bbaf0edf84b92a079a2
mit
['generated_from_trainer']
false
dreamy_williams This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
acbed69abdfde421957d7799a0d53ac1
mit
['generated_from_trainer']
false
Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 4096}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'dreamy_williams', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
06088edd565a78a723ba52cf91a8a62c
apache-2.0
['generated_from_trainer']
false
correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1206 - Precision: 0.0637 - Recall: 0.0080 - F1: 0.0141 - Accuracy: 0.9707
6bc30089bf7f86d9f9b8930f626ae703
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.1222 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 2.0 | 30 | 0.1159 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 3.0 | 45 | 0.1082 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 4.0 | 60 | 0.1042 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 5.0 | 75 | 0.1029 | 0.12 | 0.0139 | 0.0249 | 0.9736 |
505f6feac1f5eedad5231c21502e9ed6
apache-2.0
['translation']
false
opus-mt-fr-tiv * source languages: fr * target languages: tiv * OPUS readme: [fr-tiv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-tiv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-tiv/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tiv/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tiv/opus-2020-01-16.eval.txt)
21562450cec1204a6fa1d738b7466814
apache-2.0
['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_700k']
false
MultiBERTs, Intermediate Checkpoint - Seed 0, Step 700k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model
01dcdcc9aa445df32107fcf066bb0a32
apache-2.0
['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_700k']
false
How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_700k') model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_700k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_700k') model = BertModel.from_pretrained("google/multiberts-seed_0-step_700k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ```
f1c0d1df37d953cb99a92646c2391f63
apache-2.0
['translation']
false
opus-mt-sg-fi * source languages: sg * target languages: fi * OPUS readme: [sg-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sg-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/sg-fi/opus-2020-01-24.zip) * test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sg-fi/opus-2020-01-24.test.txt) * test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sg-fi/opus-2020-01-24.eval.txt)
72958e67be052153dfcc6a4c17fc8b11
apache-2.0
['generated_from_trainer']
false
Heritage-in-Digital-Age-distilbert-base-uncased-expression-rating This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0133 - Accuracy: 0.3496
efd8552315937f8b50dfe6a6f539929f
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 18 | 1.9870 | 0.3333 | | No log | 2.0 | 36 | 1.9731 | 0.3415 | | No log | 3.0 | 54 | 2.0133 | 0.3496 | | No log | 4.0 | 72 | 2.0809 | 0.3415 | | No log | 5.0 | 90 | 2.1694 | 0.3008 | | No log | 6.0 | 108 | 2.2611 | 0.2764 | | No log | 7.0 | 126 | 2.2832 | 0.3008 | | No log | 8.0 | 144 | 2.3670 | 0.2846 | | No log | 9.0 | 162 | 2.4279 | 0.2683 | | No log | 10.0 | 180 | 2.4460 | 0.3089 | | No log | 11.0 | 198 | 2.5236 | 0.2846 | | No log | 12.0 | 216 | 2.5896 | 0.3089 | | No log | 13.0 | 234 | 2.6061 | 0.3008 | | No log | 14.0 | 252 | 2.6813 | 0.2846 | | No log | 15.0 | 270 | 2.6990 | 0.3252 | | No log | 16.0 | 288 | 2.7439 | 0.3171 | | No log | 17.0 | 306 | 2.7499 | 0.3415 | | No log | 18.0 | 324 | 2.7737 | 0.3252 | | No log | 19.0 | 342 | 2.7793 | 0.3252 | | No log | 20.0 | 360 | 2.7775 | 0.3252 |
4935978c0daae1f259b4c2e8f8d6dbda
apache-2.0
[]
false
How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("allenai/macaw-11b") model = AutoModelForSeq2SeqLM.from_pretrained("allenai/macaw-11b") input_string = "$answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?" input_ids = tokenizer.encode(input_string, return_tensors="pt") output = model.generate(input_ids, max_length=200) >>> tokenizer.batch_decode(output, skip_special_tokens=True) ['$answer$ = gray ; $mcoptions$ = (A) blue (B) white (C) grey (D) black'] ```
8ddf50d63b7172f453db4d7f1fa1eebe
apache-2.0
[]
false
BibTeX entry and citation info ```bibtex @article{Tafjord2021Macaw, title={General-Purpose Question-Answering with {M}acaw}, author={Oyvind Tafjord and Peter Clark}, journal={ArXiv}, year={2021}, volume={abs/2109.02593} } ```
827a25df53109fb70838d8cdde2e7c10
mit
['generated_from_trainer']
false
xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1397 - F1: 0.8610
56b271f47f2806fde7ced5d1eb193933
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2663 | 1.0 | 525 | 0.1628 | 0.8273 | | 0.1338 | 2.0 | 1050 | 0.1457 | 0.8396 | | 0.0844 | 3.0 | 1575 | 0.1397 | 0.8610 |
bed1a363cb5c66d8338894cd69d89753
apache-2.0
['generated_from_trainer']
false
t5-small-finetuned_entailment_inference This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0249
1730258c144a392ebbb4a7e761a81b27
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP
7b58fbfc28e1650e939ea2162aa2a9d4
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2885 | 1.0 | 522 | 1.2005 | | 1.2209 | 2.0 | 1044 | 1.1594 | | 1.1871 | 3.0 | 1566 | 1.1263 | | 1.1455 | 4.0 | 2088 | 1.1098 | | 1.1124 | 5.0 | 2610 | 1.0949 | | 1.0758 | 6.0 | 3132 | 1.0825 | | 1.0485 | 7.0 | 3654 | 1.0707 | | 1.0205 | 8.0 | 4176 | 1.0606 | | 0.9913 | 9.0 | 4698 | 1.0523 | | 1.0099 | 10.0 | 5220 | 1.0463 | | 0.97 | 11.0 | 5742 | 1.0395 | | 0.9699 | 12.0 | 6264 | 1.0370 | | 0.9531 | 13.0 | 6786 | 1.0337 | | 0.9449 | 14.0 | 7308 | 1.0312 | | 0.9354 | 15.0 | 7830 | 1.0274 | | 0.9342 | 16.0 | 8352 | 1.0266 | | 0.9188 | 17.0 | 8874 | 1.0262 | | 0.9219 | 18.0 | 9396 | 1.0251 | | 0.9044 | 19.0 | 9918 | 1.0252 | | 0.9223 | 20.0 | 10440 | 1.0249 |
e3d6dec0ab593464e16d8d0a686b7afe
apache-2.0
['translation']
false
opus-mt-ig-de * source languages: ig * target languages: de * OPUS readme: [ig-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ig-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/ig-de/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ig-de/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ig-de/opus-2020-01-20.eval.txt)
383d57957460871a2743c8b4627ea4bf
cc-by-sa-4.0
['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing']
false
Model Description This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese](https://huggingface.co/cl-tohoku/bert-large-japanese). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
263609cde84abab73aa5db0d15261d79
cc-by-sa-4.0
['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing']
false
How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-japanese-unidic-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-japanese-unidic-luw-upos") s="国境の長いトンネルを抜けると雪国であった。" t=tokenizer.tokenize(s) p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]] print(list(zip(t,p))) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/bert-large-japanese-unidic-luw-upos") print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` [fugashi](https://pypi.org/project/fugashi), [unidic-lite](https://pypi.org/project/unidic-lite) and [pytokenizations](https://pypi.org/project/pytokenizations) are required.
e6a82b2a33fc68bee427f4ea7a76136d
apache-2.0
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
false
sentence-transformers/gtr-t5-large This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search. This model was converted from the Tensorflow model [gtr-large-1](https://tfhub.dev/google/gtr/gtr-large/1) to PyTorch. When using this model, have a look at the publication: [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results. The model uses only the encoder from a T5-large model. The weights are stored in FP16.
af76dd27b901c521eab2df03d86811ff
apache-2.0
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
false
Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/gtr-t5-large') embeddings = model.encode(sentences) print(embeddings) ``` The model requires sentence-transformers version 2.2.0 or newer.
73578cd6eebeba37269933de70cc6768
apache-2.0
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
false
Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/gtr-t5-large)
fc3ea05fcc56748e076f10dc3f23cfa2
apache-2.0
['image-classification', 'vision']
false
Data2Vec-Vision (base-sized model, pre-trained only) BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli and first released in [this repository](https://github.com/facebookresearch/data2vec_vision/tree/main/beit). Disclaimer: The team releasing Facebook team did not write a model card for this model so this model card has been written by the Hugging Face team.
3b96bce6e047e0417d42f35d312d8000
apache-2.0
['image-classification', 'vision']
false
Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?other=data2vec-vision) to look for fine-tuned versions on a task that interests you.
7c1439fd23230f97d056b368d981f6fa
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", "tt", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-tatar") model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-tatar") resampler = torchaudio.transforms.Resample(48_000, 16_000)
098c4b1917fbf69ba069e9c849e4f40e
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Evaluation The model can be evaluated as follows on the Tatar test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
593ab6916ab6ec7422542e0a0e8fd11e
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/tt.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.extractall() wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-tatar") model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-tatar") model.to("cuda") cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/tt/test.tsv", sep='\t') clips_path = "cv-corpus-6.1-2020-12-11/tt/clips/" def clean_sentence(sent): sent = sent.lower()
e4715347f26142db4d7977a5ea9cff44
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) row["speech"] = resampler(speech_array).squeeze().numpy() inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) targets.append(row["sentence"]) preds.append(processor.batch_decode(pred_ids)[0]) print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets))) ``` **Test Result**: 26.76 %
49cd38a1f8896dfe708fd7686b9f3470
apache-2.0
['generated_from_trainer']
false
MTL-distilbert-base-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0874
5dda03047ea5e642a9c90612c9738212
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5593 | 1.0 | 99 | 2.3163 | | 2.4346 | 2.0 | 198 | 2.2918 | | 2.3377 | 3.0 | 297 | 2.2345 | | 2.2953 | 4.0 | 396 | 2.1463 | | 2.2296 | 5.0 | 495 | 2.1761 | | 2.2235 | 6.0 | 594 | 2.0721 | | 2.1878 | 7.0 | 693 | 2.1460 | | 2.1569 | 8.0 | 792 | 2.0856 | | 2.1455 | 9.0 | 891 | 2.1039 | | 2.1391 | 10.0 | 990 | 2.1112 | | 2.1056 | 11.0 | 1089 | 2.0694 | | 2.1076 | 12.0 | 1188 | 2.0501 | | 2.0919 | 13.0 | 1287 | 2.0484 | | 2.0669 | 14.0 | 1386 | 2.0342 | | 2.0595 | 15.0 | 1485 | 2.0802 |
e9aa0256696fda23b044839432ba8be8
apache-2.0
[]
false
distilbert-base-en-tr-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
ce887709a3fa693dd9ff6a0401b158b7
apache-2.0
[]
false
How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-tr-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-tr-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
ca4e9cacf590006782b255540ab75783
apache-2.0
['generated_from_trainer']
false
distilbert_sa_GLUE_Experiment_logit_kd_wnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3436 - Accuracy: 0.5634
1231c50ca2ac78a03b5759e3a36de153
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3503 | 1.0 | 3 | 0.3457 | 0.5634 | | 0.3494 | 2.0 | 6 | 0.3476 | 0.5634 | | 0.3469 | 3.0 | 9 | 0.3439 | 0.5634 | | 0.3484 | 4.0 | 12 | 0.3436 | 0.5634 | | 0.3484 | 5.0 | 15 | 0.3444 | 0.5634 | | 0.3475 | 6.0 | 18 | 0.3469 | 0.5634 | | 0.3483 | 7.0 | 21 | 0.3472 | 0.5634 | | 0.3471 | 8.0 | 24 | 0.3460 | 0.5634 | | 0.3487 | 9.0 | 27 | 0.3446 | 0.5634 |
a43ba8e87ee861a595e3d529e0dafb14
mit
[]
false
saheeli-rai on Stable Diffusion This is the `<saheeli-rai>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<saheeli-rai> 0](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/15.jpeg) ![<saheeli-rai> 1](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/7.jpeg) ![<saheeli-rai> 2](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/12.jpeg) ![<saheeli-rai> 3](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/2.jpeg) ![<saheeli-rai> 4](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/6.jpeg) ![<saheeli-rai> 5](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/11.jpeg) ![<saheeli-rai> 6](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/10.jpeg) ![<saheeli-rai> 7](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/14.jpeg) ![<saheeli-rai> 8](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/8.jpeg) ![<saheeli-rai> 9](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/0.jpeg) ![<saheeli-rai> 10](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/1.jpeg) ![<saheeli-rai> 11](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/3.jpeg) ![<saheeli-rai> 12](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/4.jpeg) ![<saheeli-rai> 13](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/16.jpeg) ![<saheeli-rai> 14](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/13.jpeg) ![<saheeli-rai> 15](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/9.jpeg) ![<saheeli-rai> 16](https://huggingface.co/sd-concepts-library/saheeli-rai/resolve/main/concept_images/5.jpeg)
db5e5d75ed3dc880467d1a0a1234e2b5
cc-by-4.0
['translation', 'opus-mt-tc']
false
opus-mt-tc-big-zle-it Neural machine translation model for translating from East Slavic languages (zle) to Italian (it). 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", } ```
f4413835a43ff6d7c7bf876df666aab6
cc-by-4.0
['translation', 'opus-mt-tc']
false
Model info * Release: 2022-03-19 * source language(s): bel rus ukr * target language(s): ita * model: transformer-big * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807_transformer-big_2022-03-19.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-ita/opusTCv20210807_transformer-big_2022-03-19.zip) * more information released models: [OPUS-MT zle-ita README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-ita/README.md)
4f7991dc5ebb82f2db6fdd601d64f3f7
cc-by-4.0
['translation', 'opus-mt-tc']
false
Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Вони не ідіоти.", "Я не хочу идти в банк." ] model_name = "pytorch-models/opus-mt-tc-big-zle-it" 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) )
3b6de48fc313124c5b4601c024f6af33
cc-by-4.0
['translation', 'opus-mt-tc']
false
Non voglio andare in banca. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-it") print(pipe("Вони не ідіоти."))
2fa3278f1c6d7593af80eb4e1422bb70
cc-by-4.0
['translation', 'opus-mt-tc']
false
Benchmarks * test set translations: [opusTCv20210807_transformer-big_2022-03-19.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-ita/opusTCv20210807_transformer-big_2022-03-19.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-03-19.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-ita/opusTCv20210807_transformer-big_2022-03-19.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU |
9cda9722c92e17052f1249d1dac1b8f4
cc-by-4.0
['translation', 'opus-mt-tc']
false
words | |----------|---------|-------|-------|-------|--------| | bel-ita | tatoeba-test-v2021-08-07 | 0.65945 | 49.3 | 264 | 1681 | | rus-ita | tatoeba-test-v2021-08-07 | 0.64037 | 43.5 | 10045 | 71584 | | ukr-ita | tatoeba-test-v2021-08-07 | 0.69570 | 50.0 | 5000 | 27846 | | bel-ita | flores101-devtest | 0.46311 | 13.5 | 1012 | 27306 | | rus-ita | flores101-devtest | 0.53054 | 23.7 | 1012 | 27306 | | ukr-ita | flores101-devtest | 0.52783 | 23.2 | 1012 | 27306 |
01dd5a725148f03c285a619db7c31d9f
apache-2.0
['translation']
false
opus-mt-guw-fi * source languages: guw * target languages: fi * OPUS readme: [guw-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/guw-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/guw-fi/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/guw-fi/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/guw-fi/opus-2020-01-09.eval.txt)
b45fbeffdb8521a007c5bbda120eab89
mit
['generated_from_trainer']
false
finetuned_gpt2-xl_sst2_negation0.0001_pretrainedTrue_epochs1 This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 2.9187
f8f5b4b605ff745c794cedc0a9d9c838
apache-2.0
['generated_from_trainer']
false
wav2vec2-large-xls-r-300m-j-kana-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7188 - Wer: 0.1285
763138e2947d2c0805c5b99c8981ecb7
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 - mixed_precision_training: Native AMP
7e3135060dd14623242e432a1cfc9a2b
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 397 | 3.8381 | 0.9571 | | No log | 2.0 | 794 | 0.8909 | 0.2265 | | 4.0962 | 3.0 | 1191 | 0.8076 | 0.2054 | | 4.0962 | 4.0 | 1588 | 0.7300 | 0.1809 | | 4.0962 | 5.0 | 1985 | 0.7322 | 0.1761 | | 0.6325 | 6.0 | 2382 | 0.6478 | 0.1524 | | 0.6325 | 7.0 | 2779 | 0.6559 | 0.1472 | | 0.408 | 8.0 | 3176 | 0.6925 | 0.1500 | | 0.408 | 9.0 | 3573 | 0.7567 | 0.1582 | | 0.408 | 10.0 | 3970 | 0.6687 | 0.1358 | | 0.29 | 11.0 | 4367 | 0.7223 | 0.1418 | | 0.29 | 12.0 | 4764 | 0.7082 | 0.1328 | | 0.2152 | 13.0 | 5161 | 0.7114 | 0.1340 | | 0.2152 | 14.0 | 5558 | 0.7082 | 0.1280 | | 0.2152 | 15.0 | 5955 | 0.7188 | 0.1285 |
b48c7971549ced1d0657d3f6a9e338a4
apache-2.0
['generated_from_trainer']
false
bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 - Precision: 0.9310 - Recall: 0.9497 - F1: 0.9403 - Accuracy: 0.9864
1413f95cd577a4bb2d36a5dc9c7d3331
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0884 | 1.0 | 1756 | 0.0697 | 0.9128 | 0.9283 | 0.9205 | 0.9819 | | 0.0322 | 2.0 | 3512 | 0.0660 | 0.9267 | 0.9473 | 0.9369 | 0.9859 | | 0.0175 | 3.0 | 5268 | 0.0616 | 0.9310 | 0.9497 | 0.9403 | 0.9864 |
60b69b40349ee44db17199fe1a18156c
mit
['generated_from_trainer']
false
quirky_hamilton This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
b39b76e11aacb880cf63522519daf3f9
mit
['generated_from_trainer']
false
Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.000286, 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'tomekkorbak/goofy_pasteur'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'quirky_hamilton', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
eaf0b996bcc6af90894af6aa40db3133
apache-2.0
['generated_from_trainer']
false
Switch Transformer (base-16) fine-tuned om samsum for conversation summarization This model is a fine-tuned version of [google/switch-base-16](https://huggingface.co/google/switch-base-16) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4434 - Rouge1: 47.2139 - Rouge2: 23.3399 - Rougel: 39.8364 - Rougelsum: 43.2592 - Gen Len: 16.9194
1f92e5a091565d64c69b432458ea7c54
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.846 | 1.0 | 3683 | 1.4857 | 45.9134 | 22.4258 | 38.9716 | 42.6169 | 17.0623 | | 1.5734 | 2.0 | 7366 | 1.4346 | 47.574 | 24.2967 | 40.3749 | 44.2636 | 17.3790 | | 1.38 | 3.0 | 11049 | 1.4277 | 47.9915 | 24.9077 | 40.658 | 44.5301 | 17.1406 | | 1.2388 | 4.0 | 14732 | 1.4223 | 48.3444 | 25.4061 | 41.2776 | 45.0434 | 16.9254 | | 1.1629 | 5.0 | 18415 | 1.4372 | 48.5991 | 25.5464 | 41.3726 | 45.0784 | 16.9890 |
d3d681632244e56ef2a1f6a74bd302c9
mit
['sklearn', 'skops', 'tabular-classification']
false
sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}
73f48c925381ef54275b34a15d266af0
mit
['sklearn', 'skops', 'tabular-classification']
false
sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}
b68777beb98bbbd3d7a553e5df179a68
mit
['sklearn', 'skops', 'tabular-classification']
false
sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}
7f5a0a487bd80e9bbedc1bfae13676cb
mit
['sklearn', 'skops', 'tabular-classification']
false
sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0 div.sk-text-repr-fallback {display: none;}</style><div id="sk-b53d2f4f-2533-401b-9a2a-da04c1fbfce0" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>ComplementNB()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="8d305fec-9b3e-4d4c-960e-2d386ab08acc" type="checkbox" checked><label for="8d305fec-9b3e-4d4c-960e-2d386ab08acc" class="sk-toggleable__label sk-toggleable__label-arrow">ComplementNB</label><div class="sk-toggleable__content"><pre>ComplementNB()</pre></div></div></div></div></div>
75f8a40a8e9c90a717666c783147090c
gpl-3.0
['spacy', 'token-classification']
false
UD v2.5 benchmarking pipeline for UD_Spanish-AnCora | Feature | Description | | --- | --- | | **Name** | `es_udv25_spanishancora_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) | | **License** | `GNU GPL 3.0` | | **Author** | [Explosion](https://explosion.ai) |
6ae6da9856d86f7827a2dd9fb2e2098c
gpl-3.0
['spacy', 'token-classification']
false
Label Scheme <details> <summary>View label scheme (2060 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `AUX_PRON`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `PUNCT_VERB_PRON_PUNCT`, `SCONJ`, `SYM`, `VERB`, `VERB_PRON`, `X` | | **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Preppron\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `AdpType=Prep\|POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PROPN`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=PRON\|PronType=Int,Rel`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=SCONJ`, `POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Number=Plur\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=PUNCT\|PunctType=Peri`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Comm`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `POS=ADV`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `POS=PRON\|PronType=Ind`, `POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `POS=PUNCT\|PunctType=Quot`, `POS=PUNCT`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `NumType=Card\|POS=NUM`, `POS=VERB\|VerbForm=Ger`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADV`, `POS=AUX\|VerbForm=Inf`, `Number=Plur\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc,Dat\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `AdvType=Tim\|POS=NOUN`, `AdpType=Prep\|POS=ADV`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `NumType=Card\|Number=Plur\|POS=NUM`, `AdpType=Preppron\|POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `NumForm=Digit\|POS=NOUN`, `Number=Sing\|POS=PRON\|PronType=Dem`, `AdpType=Preppron\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|POS=NOUN`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `AdvType=Tim\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PUNCT\|PunctType=Colo`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PUNCT\|PunctType=Semi`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=INTJ`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `AdpType=Prep\|POS=ADJ`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PUNCT\|PunctType=Dash`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|POS=NOUN`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc,Dat\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=NOUN\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `POS=DET\|PronType=Ind`, `POS=DET\|PronType=Int,Rel`, `AdvType=Tim\|POS=ADV`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Degree=Abs\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc,Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Degree=Abs\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Case=Acc,Dat\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=SCONJ\|PronType=Int,Rel`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=SYM`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Com\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Pre\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=NOUN\|VerbForm=Fin`, `Case=Acc,Dat\|Mood=Imp\|Number=Plur,Sing\|POS=VERB\|Person=1,2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Case=Acc,Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Number=Sing\|POS=VERB\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Abs\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Dat\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `AdpType=Prep\|Degree=Cmp\|POS=ADV`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Fin`, `Case=Acc,Dat\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Ind`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|PunctType=Quot\|VerbForm=Inf`, `Case=Com\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Case=Dat\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Ind`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=PRON\|PronType=Dem`, `POS=AUX\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `AdvType=Tim\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Ind`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN\|VerbForm=Part`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Ind`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Com\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `POS=X`, `Case=Com\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `POS=ADP`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Sing\|POS=AUX\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `Case=Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=NOUN\|PunctType=Comm`, `Degree=Cmp\|POS=ADJ`, `Gender=Masc\|POS=ADJ`, `Degree=Abs\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `POS=PRON\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Ind`, `Number=Sing\|POS=DET\|PronType=Int,Rel` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:pass`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` | | **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `5`, `6`, `8`, `10`, `14`, `16`, `18`, `20`, `22`, `24`, `25`, `27`, `29`, `33`, `36`, `38`, `40`, `42`, `45`, `48`, `50`, `54`, `57`, `59`, `60`, `62`, `64`, `66`, `68`, `71`, `73`, `75`, `77`, `81`, `83`, `85`, `87`, `88`, `91`, `93`, `95`, `97`, `99`, `100`, `102`, `104`, `106`, `108`, `110`, `112`, `114`, `115`, `117`, `119`, `120`, `122`, `49`, `125`, `126`, `128`, `130`, `134`, `138`, `140`, `143`, `145`, `146`, `148`, `150`, `151`, `153`, `156`, `158`, `160`, `162`, `164`, `167`, `170`, `171`, `173`, `177`, `178`, `179`, `181`, `182`, `184`, `186`, `187`, `188`, `191`, `193`, `195`, `198`, `201`, `202`, `13`, `204`, `206`, `208`, `210`, `214`, `216`, `218`, `221`, `223`, `224`, `226`, `228`, `230`, `232`, `234`, `235`, `237`, `239`, `241`, `242`, `244`, `248`, `250`, `254`, `257`, `258`, `260`, `261`, `262`, `264`, `265`, `266`, `267`, `269`, `271`, `273`, `277`, `278`, `280`, `284`, `286`, `288`, `289`, `290`, `291`, `293`, `296`, `298`, `300`, `302`, `304`, `306`, `308`, `309`, `313`, `315`, `319`, `321`, `322`, `323`, `324`, `325`, `327`, `328`, `330`, `332`, `336`, `338`, `339`, `341`, `342`, `343`, `345`, `347`, `348`, `350`, `351`, `352`, `354`, `355`, `357`, `359`, `361`, `363`, `365`, `367`, `370`, `372`, `375`, `377`, `379`, `382`, `385`, `389`, `391`, `393`, `395`, `397`, `398`, `400`, `402`, `404`, `408`, `410`, `413`, `415`, `416`, `418`, `419`, `420`, `422`, `424`, `427`, `429`, `431`, `433`, `434`, `435`, `436`, `438`, `440`, `441`, `443`, `445`, `447`, `448`, `450`, `451`, `452`, `454`, `456`, `457`, `458`, `460`, `462`, `463`, `465`, `466`, `468`, `470`, `473`, `477`, `478`, `480`, `481`, `483`, `485`, `489`, `491`, `492`, `494`, `496`, `498`, `500`, `501`, `504`, `505`, `506`, `507`, `509`, `511`, `514`, `516`, `519`, `521`, `522`, `524`, `526`, `528`, `532`, `535`, `538`, `541`, `543`, `545`, `546`, `548`, `550`, `554`, `555`, `557`, `559`, `560`, `561`, `562`, `564`, `565`, `567`, `569`, `571`, `572`, `573`, `575`, `576`, `579`, `582`, `584`, `586`, `589`, `590`, `591`, `592`, `595`, `596`, `597`, `599`, `600`, `601`, `603`, `606`, `607`, `608`, `610`, `615`, `617`, `618`, `622`, `624`, `625`, `626`, `627`, `629`, `631`, `633`, `585`, `634`, `636`, `637`, `638`, `639`, `643`, `644`, `646`, `647`, `648`, `650`, `651`, `653`, `654`, `657`, `658`, `660`, `662`, `663`, `667`, `669`, `671`, `673`, `674`, `678`, `680`, `683`, `684`, `685`, `686`, `688`, `689`, `692`, `693`, `695`, `696`, `697`, `699`, `701`, `702`, `704`, `707`, `709`, `711`, `712`, `714`, `715`, `717`, `718`, `719`, `720`, `722`, `725`, `728`, `730`, `732`, `733`, `734`, `735`, `736`, `738`, `739`, `740`, `741`, `743`, `745`, `748`, `750`, `752`, `753`, `755`, `756`, `759`, `760`, `763`, `764`, `765`, `766`, `768`, `770`, `772`, `773`, `774`, `775`, `776`, `778`, `779`, `780`, `783`, `785`, `786`, `788`, `791`, `793`, `795`, `797`, `798`, `800`, `803`, `804`, `805`, `807`, `808`, `810`, `813`, `816`, `819`, `821`, `823`, `824`, `825`, `826`, `829`, `832`, `833`, `836`, `129`, `837`, `838`, `839`, `843`, `845`, `846`, `848`, `849`, `851`, `852`, `853`, `855`, `856`, `857`, `858`, `862`, `864`, `866`, `868`, `869`, `873`, `875`, `877`, `878`, `879`, `882`, `884`, `886`, `888`, `890`, `891`, `892`, `893`, `895`, `897`, `898`, `900`, `902`, `904`, `906`, `907`, `909`, `910`, `912`, `914`, `915`, `916`, `918`, `920`, `921`, `923`, `924`, `926`, `928`, `930`, `931`, `933`, `935`, `936`, `937`, `939`, `940`, `943`, `944`, `945`, `946`, `947`, `949`, `951`, `952`, `953`, `955`, `956`, `957`, `0`, `959`, `961`, `963`, `965`, `966`, `968`, `969`, `970`, `972`, `973`, `975`, `976`, `978`, `979`, `980`, `982`, `983`, `984`, `986`, `987`, `989`, `990`, `993`, `995`, `996`, `997`, `1000`, `1003`, `1004`, `1006`, `1007`, `1008`, `1010`, `1012`, `1013`, `1014`, `1015`, `1017`, `1018`, `1021`, `1025`, `1027`, `1029`, `1030`, `1032`, `1034`, `1035`, `1036`, `1038`, `1039`, `1041`, `1043`, `1044`, `1045`, `1046`, `1047`, `1049`, `1050`, `1052`, `1053`, `1054`, `1055`, `1056`, `1057`, `1058`, `1060`, `1061`, `1063`, `1065`, `1067`, `1069`, `1070`, `1072`, `1075`, `1076`, `1077`, `1078`, `1079`, `1080`, `1081`, `1082`, `1085`, `1086`, `1088`, `1090`, `1091`, `1092`, `1093`, `1094`, `1096`, `1097`, `1100`, `1101`, `1103`, `1104`, `1106`, `1108`, `1109`, `1111`, `1112`, `1114`, `1115`, `1116`, `598`, `26`, `1117`, `1118`, `1119`, `1121`, `1122`, `1123`, `1124`, `1125`, `1127`, `1128`, `1130`, `1132`, `1133`, `1135`, `1137`, `1139`, `1140`, `1141`, `1142`, `1144`, `1147`, `1151`, `1152`, `1153`, `1155`, `1157`, `1160`, `1162`, `1163`, `1165`, `1166`, `1170`, `1171`, `1173`, `1175`, `1177`, `1179`, `1180`, `1183`, `1185`, `1186`, `1188`, `1189`, `1191`, `1192`, `1193`, `1196`, `65`, `1197`, `1198`, `1202`, `1204`, `1206`, `1208`, `1209`, `1210`, `1213`, `1214`, `1215`, `1218`, `1220`, `1221`, `1223`, `1225`, `1226`, `1228`, `1230`, `1232`, `1233`, `1235`, `1236`, `1237`, `1238`, `1241`, `1242`, `1243`, `1244`, `1248`, `1253`, `1254`, `1256`, `1259`, `1260`, `1262`, `1264`, `1265`, `1266`, `1267`, `1269`, `1272`, `1273`, `1274`, `1275`, `1277`, `1280`, `1283`, `1286`, `1289`, `1291`, `1293`, `1294`, `1295`, `1296`, `1297`, `1298`, `1300`, `1301`, `1303`, `1307`, `1309`, `1311`, `1312`, `1316`, `1317`, `1318`, `1319`, `1321`, `1322`, `1323`, `1324`, `1325`, `1326`, `1327`, `1329`, `1330`, `1331`, `1332`, `1333`, `1334`, `1335`, `1336`, `1338`, `1339`, `1341`, `1342`, `1344`, `1346`, `1347`, `1348`, `1349`, `1350`, `1351`, `1352`, `1354`, `1356`, `1357`, `1359`, `1360`, `1361`, `1363`, `1364`, `1365`, `1369`, `1370`, `1371`, `1372`, `1373`, `1377`, `1378`, `1379`, `1381`, `1382`, `1383`, `1385`, `1386`, `1388`, `1389`, `1390`, `1391`, `1392`, `1394`, `1395`, `1396`, `1398`, `1399`, `1400`, `1402`, `1403`, `1406`, `1408`, `1409`, `1410`, `1413`, `1415`, `1416`, `1417`, `1418`, `1419`, `1421`, `1422`, `1423`, `1425`, `1427`, `1428`, `1431`, `1432`, `1433`, `1434`, `1435`, `1437`, `1438`, `1441`, `1442`, `1443`, `1445`, `1446`, `1447`, `1448`, `1449`, `1450`, `1452`, `1453`, `1454`, `1455`, `1457`, `1458`, `1460`, `1462`, `1463`, `1464`, `1467`, `1468`, `1469`, `1470`, `1472`, `1477`, `1479`, `1481`, `1484`, `1486`, `1488`, `1489`, `1492`, `1494`, `1495`, `1496`, `1498`, `1500`, `1501`, `1503`, `1504`, `1505`, `1507`, `1509`, `1510`, `1512`, `1513`, `1514`, `1516`, `1518`, `1519`, `1520`, `1523`, `1525`, `1526`, `1527`, `1529`, `1531`, `1532`, `1533`, `1535`, `1536`, `1537`, `1538`, `1540`, `1541`, `1542`, `1544`, `1546`, `1547`, `1548`, `124`, `1549`, `1551`, `1553`, `1555`, `1557`, `1560`, `1561`, `1563`, `1564`, `1565`, `1569`, `1571`, `1572`, `1573`, `1574`, `1575`, `1577`, `1579`, `1581`, `1582`, `1583`, `1585`, `1588`, `1589`, `1590`, `1591`, `1592`, `1595`, `1596`, `1597`, `1598`, `1599`, `1600`, `1601`, `1603`, `1605`, `1609`, `1611`, `1613`, `1614`, `1618`, `1619`, `1622`, `1624`, `1626`, `1628`, `1630`, `1631`, `1634`, `1636`, `1637`, `1638`, `1640`, `1642`, `1643`, `1644`, `1645`, `1646`, `1648`, `1649`, `1650`, `1651`, `1652`, `1653`, `1654`, `1656`, `1658`, `1660`, `1662`, `1665`, `1667`, `1668`, `1669`, `1671`, `1672`, `1673`, `1674`, `1675`, `1676`, `1678`, `1680`, `1681`, `1682`, `1683`, `1684`, `1685`, `1686`, `1688`, `1689`, `1690`, `1691`, `1692`, `1694`, `1696`, `1697`, `1698`, `1700`, `1701`, `1702`, `1703`, `1704`, `1706`, `1708`, `1709`, `1710`, `1711`, `1712`, `1713`, `1714`, `1715`, `1717`, `1718`, `1719`, `1721`, `1722`, `1724`, `1725`, `1726`, `1728`, `1729`, `1730`, `1731`, `1732`, `1733`, `1735`, `1737`, `1739`, `1741`, `1743`, `1744`, `1745`, `1747`, `1749`, `1750`, `1752`, `1753`, `1756`, `1758`, `1760`, `1761`, `1762`, `1764`, `1765`, `1767`, `1769`, `1772`, `1773`, `1774`, `1775`, `1777`, `1778`, `1781`, `1783`, `1784`, `1786`, `1790`, `1791`, `1792`, `1793`, `1795`, `1796`, `1798`, `1799`, `1801`, `1802`, `1804`, `1805`, `1806`, `1807`, `1809`, `1810`, `1811`, `1814`, `1816`, `1817`, `1818`, `1819`, `1820`, `1822`, `1824`, `1826`, `1827`, `1829`, `1831`, `1832`, `1834`, `1836`, `1838`, `1840`, `1842`, `1843`, `1844`, `1845`, `1847`, `1848`, `1850`, `1851`, `1853`, `1854`, `1856`, `1859`, `1860`, `1861`, `1863`, `1865`, `1866`, `1868`, `1869`, `1870`, `1871`, `1873`, `1875`, `1877`, `1879`, `1881`, `1883`, `1884`, `1887`, `1889`, `1890`, `1892`, `1893`, `1894`, `1895`, `1897`, `1899`, `1902`, `1903`, `1904`, `1906`, `1907`, `1909`, `1910`, `1912`, `1913`, `1914`, `1916`, `1917`, `1918`, `1920`, `1921`, `1923`, `1926`, `1927`, `1928`, `1929`, `1930`, `1931`, `1932`, `1933`, `1934`, `1935`, `1937`, `1938`, `1939`, `1942`, `1943`, `1944`, `1945`, `1946`, `1947`, `1948`, `1949`, `1950`, `1952`, `1953`, `1955`, `1956`, `1957`, `1958`, `1959`, `1961`, `1964`, `1967`, `1969`, `1971`, `1972`, `1974`, `1975`, `1977`, `1978`, `1979`, `1980`, `1981`, `1922`, `1982`, `1983`, `1984`, `1986`, `1988`, `1989`, `1990`, `1992`, `1993`, `1994`, `1995`, `1998`, `1999`, `2000`, `2003`, `2006`, `2007`, `2008`, `2009`, `2011`, `2013`, `2015`, `2016`, `2017`, `2018`, `2020`, `2023`, `2027`, `2028`, `2030`, `2031`, `2032`, `2033`, `2034`, `2035`, `2036`, `2039`, `2042`, `2043`, `2045`, `2047`, `2050`, `2052`, `2053`, `2054`, `2055`, `2056`, `2057`, `2061`, `2062`, `2063`, `2064`, `2065`, `2066`, `2067`, `2068`, `2069`, `2070`, `2073`, `2074`, `2075`, `2076`, `2078`, `2079`, `2080`, `2081`, `2082`, `2083`, `2084`, `2089`, `2090`, `2092`, `2093`, `2094`, `2095`, `2096`, `2098`, `2099`, `2100`, `2101`, `2103`, `2104`, `2106`, `2108`, `2109`, `2110`, `2113`, `2116`, `2119`, `2121`, `2124`, `2125`, `2126`, `2127`, `2128`, `2129`, `2132`, `2133`, `2134`, `2136`, `2137`, `2138`, `2139`, `2140`, `2141`, `2142`, `2143`, `2145`, `2146`, `2147`, `2148`, `2149`, `2150`, `2151`, `2152`, `2153`, `2154`, `2155`, `2157`, `2159`, `2160`, `2161`, `2162`, `2163`, `2164`, `2166`, `2167`, `2169`, `2172`, `2173`, `2174`, `2175`, `2178`, `2180`, `2181`, `2184`, `2186`, `2189`, `2190`, `2191`, `2192`, `2194`, `2195`, `2197`, `2199`, `2200`, `2202`, `2203`, `2204`, `2205`, `2210`, `2211`, `2212`, `2214`, `2215`, `2216`, `2217`, `2218`, `2219`, `2220`, `2221`, `2222`, `2223`, `2225`, `2227`, `2228`, `2229`, `2230`, `2231`, `2232`, `2233`, `2234`, `2235`, `2238`, `2239`, `2240`, `2241`, `2242`, `2243`, `2244`, `2245`, `2246`, `2250`, `2252`, `2254`, `2255`, `2256`, `2257`, `2258`, `2259`, `2260`, `2262`, `2264`, `2265`, `2266`, `2267`, `2268`, `2269`, `2270`, `2271`, `2272`, `2273`, `2274`, `2275`, `2276`, `2277`, `2278`, `2279`, `2280`, `2281`, `2283`, 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4d05b0c223d8edeaa7972c652722331c
gpl-3.0
['spacy', 'token-classification']
false
Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.98 | | `TOKEN_P` | 99.98 | | `TOKEN_R` | 99.99 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 97.99 | | `SENTS_P` | 97.43 | | `SENTS_R` | 98.55 | | `TAG_ACC` | 98.92 | | `POS_ACC` | 99.03 | | `MORPH_ACC` | 97.96 | | `DEP_UAS` | 93.99 | | `DEP_LAS` | 91.95 | | `LEMMA_ACC` | 98.93 |
ad8a6a024efeb1e3400633e338ec74a6
mit
['generated_from_trainer']
false
hardcore_albattani This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
714d14300675be164769e4029b483de6
mit
['generated_from_trainer']
false
Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 1661599744}, 'generation': {'every_n_steps': 32, 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 4096}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'every_n_steps': 32, 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '81a1701e025d2c65ae6e8c2103df559071523ee0', 'value_head_config': {'is_detached': False}}, 'path_or_name': 'tomekkorbak/goofy_pasteur'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 512, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'hardcore_albattani', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 3346, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1661599744, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
a0be85291ee84b1a13a8948085a6b909
apache-2.0
['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer']
false
xls-r-300m-pa This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set: - Loss: 1.0443 - Wer: 0.5715
3eedbc8c56b6c917f00df3c851f87e92
apache-2.0
['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 500.0 - mixed_precision_training: Native AMP
a63b00d818de7e3018182284037f2816
apache-2.0
['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 4.6694 | 19.22 | 500 | 4.0455 | 1.0 | | 3.3907 | 38.45 | 1000 | 3.2836 | 1.0 | | 2.0866 | 57.67 | 1500 | 1.2788 | 0.7715 | | 1.4106 | 76.9 | 2000 | 0.7866 | 0.6891 | | 1.1711 | 96.15 | 2500 | 0.6556 | 0.6272 | | 1.038 | 115.37 | 3000 | 0.6195 | 0.5680 | | 0.8989 | 134.6 | 3500 | 0.6563 | 0.5602 | | 0.8021 | 153.82 | 4000 | 0.6644 | 0.5327 | | 0.7161 | 173.07 | 4500 | 0.6844 | 0.5253 | | 0.6449 | 192.3 | 5000 | 0.7018 | 0.5331 | | 0.5659 | 211.52 | 5500 | 0.7451 | 0.5465 | | 0.5118 | 230.75 | 6000 | 0.7857 | 0.5386 | | 0.4385 | 249.97 | 6500 | 0.8062 | 0.5382 | | 0.3984 | 269.22 | 7000 | 0.8316 | 0.5621 | | 0.3666 | 288.45 | 7500 | 0.8736 | 0.5504 | | 0.3256 | 307.67 | 8000 | 0.9133 | 0.5688 | | 0.289 | 326.9 | 8500 | 0.9556 | 0.5684 | | 0.2663 | 346.15 | 9000 | 0.9344 | 0.5708 | | 0.2445 | 365.37 | 9500 | 0.9472 | 0.5590 | | 0.2289 | 384.6 | 10000 | 0.9713 | 0.5672 | | 0.2048 | 403.82 | 10500 | 0.9978 | 0.5762 | | 0.1857 | 423.07 | 11000 | 1.0230 | 0.5798 | | 0.1751 | 442.3 | 11500 | 1.0409 | 0.5755 | | 0.1688 | 461.52 | 12000 | 1.0445 | 0.5727 | | 0.1633 | 480.75 | 12500 | 1.0484 | 0.5739 | | 0.1488 | 499.97 | 13000 | 1.0443 | 0.5715 |
8e79a27d401a7fa5504a9020bbb4624a
mit
['torch']
false
BERT BASE (cased) finetuned on Bulgarian named-entity-recognition data Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is cased: it does make a difference between bulgarian and Bulgarian. The training data is Bulgarian text from [OSCAR](https://oscar-corpus.com/post/oscar-2019/), [Chitanka](https://chitanka.info/) and [Wikipedia](https://bg.wikipedia.org/). It was finetuned on public named-entity-recognition Bulgarian data. Then, it was compressed via [progressive module replacing](https://arxiv.org/abs/2002.02925).
d91d82ce9f3f7921dbea8e857fd0f253
mit
['torch']
false
How to use Here is how to use this model in PyTorch: ```python >>> from transformers import pipeline >>> >>> model = pipeline( >>> 'ner', >>> model='rmihaylov/bert-base-ner-theseus-bg', >>> tokenizer='rmihaylov/bert-base-ner-theseus-bg', >>> device=0, >>> revision=None) >>> output = model('Здравей, аз се казвам Иван.') >>> print(output) [{'end': 26, 'entity': 'B-PER', 'index': 6, 'score': 0.9937722, 'start': 21, 'word': '▁Иван'}] ```
8eb2171daaef6c86678dcc8cd8c9e346
cc-by-4.0
['question generation']
false
Model Card of `research-backup/bart-base-squad-qg-no-answer` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).
cfa9f87429647ed5c23f0767a4446d2d
cc-by-4.0
['question generation']
false
model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-base-squad-qg-no-answer") output = pipe("<hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>") ```
7ccb99e50414dfd91aec9e58a47f4f20
cc-by-4.0
['question generation']
false
Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 52.64 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 37.04 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 28.15 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 21.97 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 23.72 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 63.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 49.7 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
94dae42675a4282731704c46bbf5d342
cc-by-4.0
['question generation']
false
Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 4 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-base-squad-qg-no-answer/raw/main/trainer_config.json).
a0e6908349983b3ebd51a1c8e3939c16
creativeml-openrail-m
['text-to-image', 'stable-diffusion']
false
purple_skin_care Dreambooth model trained by nsaghatelyan with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
23ca71f6069940dfefa177b244333648
apache-2.0
['generated_from_trainer']
false
wav2vec2-base-cvbn-37knew This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the cvbn dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2208 - eval_wer: 0.2889 - eval_runtime: 336.8019 - eval_samples_per_second: 8.907 - eval_steps_per_second: 0.558 - epoch: 4.11 - step: 9600
84bf4b2f42eff2e56f87ede53e61ba70
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP
7f8aec1c290b05e139acde505285be05
apache-2.0
['generated_from_trainer']
false
bert-small-finetuned-ner-to-multilabel-wnut-17 This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2186
b5aabcac62d1704bdab94928ec897fd2
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1987 | 1.18 | 500 | 0.2114 | | 0.1235 | 2.35 | 1000 | 0.1894 | | 0.0924 | 3.53 | 1500 | 0.1890 | | 0.0735 | 4.71 | 2000 | 0.1958 | | 0.0534 | 5.88 | 2500 | 0.1993 | | 0.0401 | 7.06 | 3000 | 0.2186 |
071448c770ee1f833f767db367b0fa5e