Upload batch 84 (20 files, last=huggingface_dataset/Dataset_Card/society-ethics_papers.md)

huggingface_dataset/Dataset_Card/Bingsu_KcBERT_Pre-Training_Corpus.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- crowdsourced
+language:
+- ko
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: KcBERT Pre-Training Corpus (Korean News Comments)
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- fill-mask
+- text-generation
+task_ids:
+- masked-language-modeling
+- language-modeling
+---
+
+# KcBERT Pre-Training Corpus (Korean News Comments)
+
+## Dataset Description
+
+- **Homepage:** [KcBERT Pre-Training Corpus](https://www.kaggle.com/datasets/junbumlee/kcbert-pretraining-corpus-korean-news-comments)
+
+- **Repository:** [Beomi/KcBERT](https://github.com/Beomi/KcBERT)
+
+- **Paper:** [Needs More Information]
+
+- **Leaderboard:** [Needs More Information]
+
+- **Point of Contact:** [Needs More Information]
+
+
+## KcBERT
+[beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base)
+
+Github KcBERT Repo: [https://github.com/Beomi/KcBERT](https://github.com/Beomi/KcBERT)  
+KcBERT is Korean Comments BERT pretrained on this Corpus set.  
+(You can use it via Huggingface's Transformers library!)
+
+This Kaggle Dataset contains **CLEANED** dataset preprocessed with the code below.
+
+```python
+import re
+import emoji
+from soynlp.normalizer import repeat_normalize
+
+emojis = ''.join(emoji.UNICODE_EMOJI.keys())
+pattern = re.compile(f'[^ .,?!/@$%~％·∼()\x00-\x7Fㄱ-힣{emojis}]+')
+url_pattern = re.compile(
+    r'https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)')
+
+def clean(x):
+    x = pattern.sub(' ', x)
+    x = url_pattern.sub('', x)
+    x = x.strip()
+    x = repeat_normalize(x, num_repeats=2)
+    return x
+```
+
+### License
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+
+## Dataset Structure
+### Data Instance
+```pycon
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("Bingsu/KcBERT_Pre-Training_Corpus")
+>>> dataset
+DatasetDict({
+	train: Dataset({
+		features: ['text'],
+		num_rows: 86246285
+	})
+})
+```
+
+### Data Size
+
+download: 7.90 GiB<br>
+generated: 11.86 GiB<br>
+total: 19.76 GiB
+
+※ You can download this dataset from [kaggle](https://www.kaggle.com/datasets/junbumlee/kcbert-pretraining-corpus-korean-news-comments), and it's 5 GiB. (12.48 GiB when uncompressed)
+
+### Data Fields
+
+- text: `string`
+
+### Data Splits
+
+|            |   train  |
+| ---------- | -------- |
+| # of texts | 86246285 |

huggingface_dataset/Dataset_Card/Cohere_miracl-yo-corpus-22-12.md

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+---
+annotations_creators:
+- expert-generated
+
+language:
+- yo
+
+multilinguality:
+- multilingual
+
+size_categories: []
+source_datasets: []
+tags: []
+
+task_categories:
+- text-retrieval
+
+license:
+- apache-2.0
+
+task_ids:
+- document-retrieval
+---
+
+# MIRACL (yo) embedded with cohere.ai `multilingual-22-12` encoder
+
+We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
+
+The query embeddings can be found in [Cohere/miracl-yo-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-yo-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-yo-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-yo-corpus-22-12).
+
+For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
+
+
+Dataset info:
+> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
+>
+> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
+
+## Embeddings
+We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages.  If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
+
+
+## Loading the dataset
+
+In [miracl-yo-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-yo-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
+
+You can either load the dataset like this:
+```python
+from datasets import load_dataset
+docs = load_dataset(f"Cohere/miracl-yo-corpus-22-12", split="train")
+```
+
+Or you can also stream it without downloading it before:
+```python
+from datasets import load_dataset
+docs = load_dataset(f"Cohere/miracl-yo-corpus-22-12", split="train", streaming=True)
+
+for doc in docs:
+	docid = doc['docid']
+	title = doc['title']
+	text = doc['text']
+	emb = doc['emb']
+```
+
+## Search
+
+Have a look at [miracl-yo-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-yo-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
+
+To search in the documents, you must use **dot-product**. 
+
+
+And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
+
+A full search example:
+```python
+# Attention! For large datasets, this requires a lot of memory to store
+# all document embeddings and to compute the dot product scores.
+# Only use this for smaller datasets. For large datasets, use a vector DB
+
+from datasets import load_dataset
+import torch
+
+#Load documents + embeddings
+docs = load_dataset(f"Cohere/miracl-yo-corpus-22-12", split="train")
+doc_embeddings = torch.tensor(docs['emb'])
+
+# Load queries 
+queries = load_dataset(f"Cohere/miracl-yo-queries-22-12", split="dev")
+
+# Select the first query as example
+qid = 0
+query = queries[qid]
+query_embedding = torch.tensor(queries['emb'])
+
+# Compute dot score between query embedding and document embeddings
+dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
+top_k = torch.topk(dot_scores, k=3)
+
+# Print results
+print("Query:", query['query'])
+for doc_id in top_k.indices[0].tolist():
+    print(docs[doc_id]['title'])
+    print(docs[doc_id]['text'])
+```
+
+You can get embeddings for new queries using our API:
+```python
+#Run: pip install cohere
+import cohere
+co = cohere.Client(f"{api_key}")  # You should add your cohere API Key here :))
+texts = ['my search query']
+response = co.embed(texts=texts, model='multilingual-22-12')
+query_embedding = response.embeddings[0] # Get the embedding for the first text
+```
+
+## Performance
+
+In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
+
+
+We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
+
+Note: MIRACL only annotated a small fraction  of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.  
+
+
+| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
+|---|---|---|---|---|
+| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
+| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
+| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
+| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
+| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
+| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
+| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
+| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
+| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
+| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
+| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
+
+Further languages (not supported by Elasticsearch):
+| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
+|---|---|---|
+| miracl-fa | 44.8 | 53.6 |
+| miracl-ja | 49.0 | 61.0 |
+| miracl-ko | 50.9 | 64.8 |
+| miracl-sw | 61.4 | 74.5 |
+| miracl-te | 67.8 | 72.3 |
+| miracl-th | 60.2 | 71.9 |
+| miracl-yo | 56.4 | 62.2 |
+| miracl-zh | 43.8 | 56.5 |
+| **Avg** | 54.3 | 64.6 |
+

huggingface_dataset/Dataset_Card/HuggingFaceM4_COCO.md

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+---
+license: cc-by-4.0
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://cocodataset.org/](https://cocodataset.org/)
+- **Repository:**
+- **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+MS COCO is a large-scale object detection, segmentation, and captioning dataset.
+COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.
+
+As of now, there is only the 2014 subset (with Karpathy annotations and splits), but feel free to contribute the 2017 subset of COCO!
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance has the following structure:
+```
+{
+    'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F69C1BA8550>,
+    'filepath': 'COCO_val2014_000000522418.jpg',
+    'sentids': [681330, 686718, 688839, 693159, 693204],
+    'filename': 'COCO_val2014_000000522418.jpg',
+    'imgid': 1,
+    'split': 'restval',
+    'sentences': {
+        'tokens': ['a', 'woman', 'wearing', 'a', 'net', 'on', 'her', 'head', 'cutting', 'a', 'cake'],
+        'raw': 'A woman wearing a net on her head cutting a cake. ',
+        'imgid': 1,
+        'sentid': 681330
+    },
+    'cocoid': 522418
+}
+```
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

huggingface_dataset/Dataset_Card/PublicPrompts_Karsh.md

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+---
+license: openrail++
+---
+Textual Inversion embedding to create portraits in the style of the most famous portrait photographer ever, "Yousuf Karsh"
+Trigger word is "karsh"
+
+
+Example images generated with this prompt template: portrait photo of "character", highly detailed, by karsh
+
+
+![05898-3647817921-portrait photo of The joker, highly detailed, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663304101-63507e5e18a4f616c9dfba19.png)
+
+![05793-3428311090-portrait photo of wonder woman, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663318736-63507e5e18a4f616c9dfba19.png)
+
+![05921-3536412260-portrait photo of harley quinn, highly detailed, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663375997-63507e5e18a4f616c9dfba19.png)
+
+![05941-2474898187-portrait photo of Han solo, highly detailed, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663376996-63507e5e18a4f616c9dfba19.png)
+
+![05960-3239527709-portrait photo of Yoda, highly detailed, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663377978-63507e5e18a4f616c9dfba19.png)
+
+![05790-1118996825-portrait photo of Harley Quinn, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663368112-63507e5e18a4f616c9dfba19.png)
+
+![05826-790578085-portrait photo of Sonic the hedgehog, highly detailed, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663378683-63507e5e18a4f616c9dfba19.png)
+
+![05834-879254331-portrait photo of pikachu, highly detailed, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663376663-63507e5e18a4f616c9dfba19.png)
+
+![05840-2710542385-portrait photo of indiana jones, highly detailed, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663377666-63507e5e18a4f616c9dfba19.png)
+
+![05875-2065006831-portrait photo of master chief, highly detailed, by karsh.png](https://s3.amazonaws.com/moonup/production/uploads/1669663379060-63507e5e18a4f616c9dfba19.png)
+
+
+

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-lener_br-lener_br-280a5d-1776961679.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- lener_br
+eval_info:
+  task: entity_extraction
+  model: pierreguillou/ner-bert-large-cased-pt-lenerbr
+  metrics: []
+  dataset_name: lener_br
+  dataset_config: lener_br
+  dataset_split: test
+  col_mapping:
+    tokens: tokens
+    tags: ner_tags
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: pierreguillou/ner-bert-large-cased-pt-lenerbr
+* Dataset: lener_br
+* Config: lener_br
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-launch__gov_report-plain_text-1abd3a-16146233.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- launch/gov_report
+eval_info:
+  task: summarization
+  model: google/bigbird-pegasus-large-pubmed
+  metrics: ['bertscore']
+  dataset_name: launch/gov_report
+  dataset_config: plain_text
+  dataset_split: test
+  col_mapping:
+    text: document
+    target: summary
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: google/bigbird-pegasus-large-pubmed
+* Dataset: launch/gov_report
+* Config: plain_text
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-b95cefb8-9695269.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- tals/vitaminc
+eval_info:
+  task: multi_class_classification
+  model: tals/albert-base-vitaminc
+  metrics: []
+  dataset_name: tals/vitaminc
+  dataset_config: tals--vitaminc
+  dataset_split: test
+  col_mapping:
+    text: claim
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Multi-class Text Classification
+* Model: tals/albert-base-vitaminc
+* Dataset: tals/vitaminc
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@SalimHF](https://huggingface.co/SalimHF) for evaluating this model.

huggingface_dataset/Dataset_Card/eugenesiow_Set5.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- found
+language: []
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- other
+task_ids: []
+pretty_name: Set5
+tags:
+- other-image-super-resolution
+---
+
+# Dataset Card for Set5
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage**: http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html
+- **Repository**: https://huggingface.co/datasets/eugenesiow/Set5
+- **Paper**: http://people.rennes.inria.fr/Aline.Roumy/publi/12bmvc_Bevilacqua_lowComplexitySR.pdf
+- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
+
+### Dataset Summary
+
+Set5 is a evaluation dataset with 5 RGB images for the image super resolution task. The 5 images of the dataset are (“baby”, “bird”, “butterfly”, “head”, “woman”).
+
+Install with `pip`:
+```bash
+pip install datasets super-image
+```
+
+Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
+```python
+from datasets import load_dataset
+from super_image import EdsrModel
+from super_image.data import EvalDataset, EvalMetrics
+
+dataset = load_dataset('eugenesiow/Set5', 'bicubic_x2', split='validation')
+eval_dataset = EvalDataset(dataset)
+model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
+EvalMetrics().evaluate(model, eval_dataset)
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset is commonly used for evaluation of the `image-super-resolution` task.
+
+Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
+- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
+- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
+- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
+- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
+
+### Languages
+
+Not applicable.
+
+## Dataset Structure
+
+### Data Instances
+
+An example of `validation` for `bicubic_x2` looks as follows.
+```
+{
+    "hr": "/.cache/huggingface/datasets/downloads/extracted/Set5_HR/baby.png",
+    "lr": "/.cache/huggingface/datasets/downloads/extracted/Set5_LR_x2/baby.png"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
+- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
+
+### Data Splits
+
+| name  |validation|
+|-------|---:|
+|bicubic_x2|5|
+|bicubic_x3|5|
+|bicubic_x4|5|
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+No annotations.
+
+#### Who are the annotators?
+
+No annotators.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+- **Original Authors**: [Bevilacqua et al.](http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html)
+
+### Licensing Information
+
+Academic use only.
+
+### Citation Information
+
+```bibtex
+@article{bevilacqua2012low,
+  title={Low-complexity single-image super-resolution based on nonnegative neighbor embedding},
+  author={Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line},
+  year={2012},
+  publisher={BMVA press}
+}
+```
+
+### Contributions
+
+Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.

huggingface_dataset/Dataset_Card/hamnaanaa_Duckietown-Multiclass-Semantic-Segmentation-Dataset.md

@@ -0,0 +1,49 @@
+---
+license: openrail
+task_categories:
+- image-segmentation
+tags:
+- Duckietown
+- Lane Following
+- Autonomous Driving
+pretty_name: Duckietown Multiclass Semantic Segmentation Dataset
+size_categories:
+- n<1K
+---
+
+# Multiclass Semantic Segmentation Duckietown Dataset
+A dataset of multiclass semantic segmentation image annotations for the first 250 images of the ["Duckietown Object Detection Dataset"](https://docs.duckietown.org/daffy/AIDO/out/object_detection_dataset.html).
+
+| Raw Image | Segmentated Image |
+| --- | --- |
+| <img width="915" alt="raw_image" src="https://user-images.githubusercontent.com/42655977/211690204-301193c3-a651-4a3a-bd66-6458cf3a8778.png"> | <img width="915" alt="segmentation_mask" src="https://user-images.githubusercontent.com/42655977/211690212-2c9ca63a-f3ae-4d65-a4e0-ea76b20a616f.png"> |
+
+# Semantic Classes
+
+This dataset defines 8 semantic classes (7 distinct classes + implicit background class):
+| Class | XML Label | Description | Color (RGB) |
+| --- | --- | --- | --- |
+| Ego Lane | `Ego Lane` | The lane the agent is supposed to be driving in (default right-hand traffic assumed) | `[102,255,102]` |
+| Opposite Lane | `Opposite Lane` | The lane opposite to the one the agent is supposed to be driving in (default right-hand traffic assumed) | `[245,147,49]` |
+| Road End | `Road End` | Perpendicular red indicator found in Duckietown indicating the end of the road or the beginning of an intersection | `[184,61,245]` |
+| Intersection | `Intersection` | Road tile with no lane markings that has either 3 (T-intersection) or 4 (X-intersection) adjacent road tiles  | `[50,183,250]` |
+| Middle Lane | `Middle Lane` | Broken yellow lane in the middle of the road separating lanes  | `[255,255,0]` |
+| Side Lane | `Side Lane` | Solid white lane marking the road boundary  | `[255,255,255]` |
+| Background | `Background` | Unclassified | - (implicit class) |
+
+### **Notice**:
+
+(1) The color assignment is purely a suggestion as the color information encoded in the annotation file is not used by the `cvat_preprocessor.py` and can therefore be overwritten by any other mapping. The specified color mapping is mentioned here for explanatory and consistency reasons as this mapping is used in `dataloader.py` (see [Usage](#usage) for more information).
+
+(2) `[Ego Lane, Opposite Lane, Intersection]` are three semantic classes for essentially the same road tiles - the three classes were added to introduce more information for some use cases. Keep in mind, that some semantic segmentation neural network have a hard time learning the difference between these classes, leading to a poor performance on detecting these classes. In such case, treating these three classes as one *"Road"* class helps improving the segmentation performance.
+
+(3) The `Middle Lane` and `Side Lane` classes were added later and thus only the first 125 images were annotated. If you want to use these, use the `segmentation_annotation.xml` annotation file. Otherwise, `segmentation_annotation_old.xml` stores 250 images (including the 125 images from the other annotation file) but without these two classes.
+
+(4) `Background` is a special semantic class as it is not stored in the annotation file. This class is assigned to all pixels that don't have any other class (see `dataloader.py` for a reference solution for that).
+
+# Usage
+[](#usage)
+
+Due to the rather large size of the original dataset *(~750MB)*, this repository only contains annotations file stored in `CVAT for Images 1.1` format as well as two python files:
+- `cvat_preprocessor.py`: A collection of helper functions to read the annotations file and extract the annotation masks stored as polygons.
+- `dataloader.py`: A [_PyTorch_](https://pytorch.org)-specific example implementation of a wrapper-dataset to use with PyTorch machine learning models. 

huggingface_dataset/Dataset_Card/huggingartists_cocomelon.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/cocomelon"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of the generated dataset:** 0.084755 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/a6115c556163f271124bacf8a07db45d.499x499x1.png&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/cocomelon">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">Cocomelon</div>
+    <a href="https://genius.com/artists/cocomelon">
+    	<div style="text-align: center; font-size: 14px;">@cocomelon</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/cocomelon).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/cocomelon")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|53|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/cocomelon")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/huggingartists_mikhail-krug.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/mikhail-krug"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of the generated dataset:** 0.051644 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/510c736f2e8270a8e8ddbd62ab75d943.466x466x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/mikhail-krug">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">Михаил Круг (Mikhail Krug)</div>
+    <a href="https://genius.com/artists/mikhail-krug">
+    	<div style="text-align: center; font-size: 14px;">@mikhail-krug</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/mikhail-krug).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/mikhail-krug")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|13|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/mikhail-krug")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/ipipan_nkjp1m.md

@@ -0,0 +1,1284 @@
+---
+annotations_creators:
+- expert-generated
+language:
+- pl
+language_creators:
+- expert-generated
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: NKJP1M
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags:
+- National Corpus of Polish
+- Narodowy Korpus Języka Polskiego
+task_categories:
+- token-classification
+task_ids:
+- part-of-speech
+- lemmatization
+dataset_info:
+  features:
+  - name: nkjp_text
+    dtype: string
+  - name: nkjp_par
+    dtype: string
+  - name: nkjp_sent
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: lemmas
+    sequence: string
+  - name: cposes
+    sequence:
+      class_label:
+        names:
+          0: A
+          1: Adv
+          2: Comp
+          3: Conj
+          4: Dig
+          5: Interj
+          6: N
+          7: Num
+          8: Part
+          9: Prep
+          10: Punct
+          11: V
+          12: X
+  - name: poses
+    sequence:
+      class_label:
+        names:
+          0: adj
+          1: adja
+          2: adjc
+          3: adjp
+          4: adv
+          5: aglt
+          6: bedzie
+          7: brev
+          8: comp
+          9: conj
+          10: depr
+          11: dig
+          12: fin
+          13: frag
+          14: ger
+          15: imps
+          16: impt
+          17: inf
+          18: interj
+          19: interp
+          20: num
+          21: numcomp
+          22: pact
+          23: pacta
+          24: pant
+          25: part
+          26: pcon
+          27: ppas
+          28: ppron12
+          29: ppron3
+          30: praet
+          31: pred
+          32: prep
+          33: romandig
+          34: siebie
+          35: subst
+          36: sym
+          37: winien
+          38: xxs
+          39: xxx
+  - name: tags
+    sequence:
+      class_label:
+        names:
+          0: adj:pl:acc:f:com
+          1: adj:pl:acc:f:pos
+          2: adj:pl:acc:f:sup
+          3: adj:pl:acc:m1:com
+          4: adj:pl:acc:m1:pos
+          5: adj:pl:acc:m1:sup
+          6: adj:pl:acc:m2:com
+          7: adj:pl:acc:m2:pos
+          8: adj:pl:acc:m2:sup
+          9: adj:pl:acc:m3:com
+          10: adj:pl:acc:m3:pos
+          11: adj:pl:acc:m3:sup
+          12: adj:pl:acc:n:com
+          13: adj:pl:acc:n:pos
+          14: adj:pl:acc:n:sup
+          15: adj:pl:dat:f:com
+          16: adj:pl:dat:f:pos
+          17: adj:pl:dat:f:sup
+          18: adj:pl:dat:m1:com
+          19: adj:pl:dat:m1:pos
+          20: adj:pl:dat:m1:sup
+          21: adj:pl:dat:m2:pos
+          22: adj:pl:dat:m3:com
+          23: adj:pl:dat:m3:pos
+          24: adj:pl:dat:n:pos
+          25: adj:pl:dat:n:sup
+          26: adj:pl:gen:f:com
+          27: adj:pl:gen:f:pos
+          28: adj:pl:gen:f:sup
+          29: adj:pl:gen:m1:com
+          30: adj:pl:gen:m1:pos
+          31: adj:pl:gen:m1:sup
+          32: adj:pl:gen:m2:com
+          33: adj:pl:gen:m2:pos
+          34: adj:pl:gen:m2:sup
+          35: adj:pl:gen:m3:com
+          36: adj:pl:gen:m3:pos
+          37: adj:pl:gen:m3:sup
+          38: adj:pl:gen:n:com
+          39: adj:pl:gen:n:pos
+          40: adj:pl:gen:n:sup
+          41: adj:pl:inst:f:com
+          42: adj:pl:inst:f:pos
+          43: adj:pl:inst:f:sup
+          44: adj:pl:inst:m1:com
+          45: adj:pl:inst:m1:pos
+          46: adj:pl:inst:m1:sup
+          47: adj:pl:inst:m2:pos
+          48: adj:pl:inst:m3:com
+          49: adj:pl:inst:m3:pos
+          50: adj:pl:inst:m3:sup
+          51: adj:pl:inst:n:com
+          52: adj:pl:inst:n:pos
+          53: adj:pl:inst:n:sup
+          54: adj:pl:loc:f:com
+          55: adj:pl:loc:f:pos
+          56: adj:pl:loc:f:sup
+          57: adj:pl:loc:m1:com
+          58: adj:pl:loc:m1:pos
+          59: adj:pl:loc:m1:sup
+          60: adj:pl:loc:m2:pos
+          61: adj:pl:loc:m3:com
+          62: adj:pl:loc:m3:pos
+          63: adj:pl:loc:m3:sup
+          64: adj:pl:loc:n:com
+          65: adj:pl:loc:n:pos
+          66: adj:pl:loc:n:sup
+          67: adj:pl:nom:f:com
+          68: adj:pl:nom:f:pos
+          69: adj:pl:nom:f:sup
+          70: adj:pl:nom:m1:com
+          71: adj:pl:nom:m1:pos
+          72: adj:pl:nom:m1:sup
+          73: adj:pl:nom:m2:com
+          74: adj:pl:nom:m2:pos
+          75: adj:pl:nom:m2:sup
+          76: adj:pl:nom:m3:com
+          77: adj:pl:nom:m3:pos
+          78: adj:pl:nom:m3:sup
+          79: adj:pl:nom:n:com
+          80: adj:pl:nom:n:pos
+          81: adj:pl:nom:n:sup
+          82: adj:pl:voc:f:pos
+          83: adj:pl:voc:m1:pos
+          84: adj:pl:voc:m2:pos
+          85: adj:pl:voc:n:pos
+          86: adj:sg:acc:f:com
+          87: adj:sg:acc:f:pos
+          88: adj:sg:acc:f:sup
+          89: adj:sg:acc:m1:com
+          90: adj:sg:acc:m1:pos
+          91: adj:sg:acc:m1:sup
+          92: adj:sg:acc:m2:com
+          93: adj:sg:acc:m2:pos
+          94: adj:sg:acc:m2:sup
+          95: adj:sg:acc:m3:com
+          96: adj:sg:acc:m3:pos
+          97: adj:sg:acc:m3:sup
+          98: adj:sg:acc:n:com
+          99: adj:sg:acc:n:pos
+          100: adj:sg:acc:n:sup
+          101: adj:sg:dat:f:com
+          102: adj:sg:dat:f:pos
+          103: adj:sg:dat:f:sup
+          104: adj:sg:dat:m1:com
+          105: adj:sg:dat:m1:pos
+          106: adj:sg:dat:m1:sup
+          107: adj:sg:dat:m2:pos
+          108: adj:sg:dat:m3:com
+          109: adj:sg:dat:m3:pos
+          110: adj:sg:dat:m3:sup
+          111: adj:sg:dat:n:com
+          112: adj:sg:dat:n:pos
+          113: adj:sg:dat:n:sup
+          114: adj:sg:gen:f:com
+          115: adj:sg:gen:f:pos
+          116: adj:sg:gen:f:sup
+          117: adj:sg:gen:m1:com
+          118: adj:sg:gen:m1:pos
+          119: adj:sg:gen:m1:sup
+          120: adj:sg:gen:m2:pos
+          121: adj:sg:gen:m2:sup
+          122: adj:sg:gen:m3:com
+          123: adj:sg:gen:m3:pos
+          124: adj:sg:gen:m3:sup
+          125: adj:sg:gen:n:com
+          126: adj:sg:gen:n:pos
+          127: adj:sg:gen:n:sup
+          128: adj:sg:inst:f:com
+          129: adj:sg:inst:f:pos
+          130: adj:sg:inst:f:sup
+          131: adj:sg:inst:m1:com
+          132: adj:sg:inst:m1:pos
+          133: adj:sg:inst:m1:sup
+          134: adj:sg:inst:m2:com
+          135: adj:sg:inst:m2:pos
+          136: adj:sg:inst:m2:sup
+          137: adj:sg:inst:m3:com
+          138: adj:sg:inst:m3:pos
+          139: adj:sg:inst:m3:sup
+          140: adj:sg:inst:n:com
+          141: adj:sg:inst:n:pos
+          142: adj:sg:inst:n:sup
+          143: adj:sg:loc:f:com
+          144: adj:sg:loc:f:pos
+          145: adj:sg:loc:f:sup
+          146: adj:sg:loc:m1:com
+          147: adj:sg:loc:m1:pos
+          148: adj:sg:loc:m1:sup
+          149: adj:sg:loc:m2:com
+          150: adj:sg:loc:m2:pos
+          151: adj:sg:loc:m3:com
+          152: adj:sg:loc:m3:pos
+          153: adj:sg:loc:m3:sup
+          154: adj:sg:loc:n:com
+          155: adj:sg:loc:n:pos
+          156: adj:sg:loc:n:sup
+          157: adj:sg:nom:f:com
+          158: adj:sg:nom:f:pos
+          159: adj:sg:nom:f:sup
+          160: adj:sg:nom:m1:com
+          161: adj:sg:nom:m1:pos
+          162: adj:sg:nom:m1:sup
+          163: adj:sg:nom:m2:com
+          164: adj:sg:nom:m2:pos
+          165: adj:sg:nom:m2:sup
+          166: adj:sg:nom:m3:com
+          167: adj:sg:nom:m3:pos
+          168: adj:sg:nom:m3:sup
+          169: adj:sg:nom:n:com
+          170: adj:sg:nom:n:pos
+          171: adj:sg:nom:n:sup
+          172: adj:sg:voc:f:pos
+          173: adj:sg:voc:f:sup
+          174: adj:sg:voc:m1:pos
+          175: adj:sg:voc:m1:sup
+          176: adj:sg:voc:m2:pos
+          177: adj:sg:voc:m3:pos
+          178: adj:sg:voc:n:pos
+          179: adja
+          180: adjc
+          181: adjp:dat
+          182: adjp:gen
+          183: adv
+          184: adv:com
+          185: adv:pos
+          186: adv:sup
+          187: aglt:pl:pri:imperf:nwok
+          188: aglt:pl:sec:imperf:nwok
+          189: aglt:sg:pri:imperf:nwok
+          190: aglt:sg:pri:imperf:wok
+          191: aglt:sg:sec:imperf:nwok
+          192: aglt:sg:sec:imperf:wok
+          193: bedzie:pl:pri:imperf
+          194: bedzie:pl:sec:imperf
+          195: bedzie:pl:ter:imperf
+          196: bedzie:sg:pri:imperf
+          197: bedzie:sg:sec:imperf
+          198: bedzie:sg:ter:imperf
+          199: brev:npun
+          200: brev:pun
+          201: comp
+          202: conj
+          203: depr:pl:acc:m2
+          204: depr:pl:nom:m2
+          205: depr:pl:voc:m2
+          206: dig
+          207: fin:pl:pri:imperf
+          208: fin:pl:pri:perf
+          209: fin:pl:sec:imperf
+          210: fin:pl:sec:perf
+          211: fin:pl:ter:imperf
+          212: fin:pl:ter:perf
+          213: fin:sg:pri:imperf
+          214: fin:sg:pri:perf
+          215: fin:sg:sec:imperf
+          216: fin:sg:sec:perf
+          217: fin:sg:ter:imperf
+          218: fin:sg:ter:perf
+          219: frag
+          220: ger:pl:acc:n:imperf:aff
+          221: ger:pl:acc:n:perf:aff
+          222: ger:pl:dat:n:perf:aff
+          223: ger:pl:gen:n:imperf:aff
+          224: ger:pl:gen:n:perf:aff
+          225: ger:pl:inst:n:imperf:aff
+          226: ger:pl:inst:n:perf:aff
+          227: ger:pl:loc:n:imperf:aff
+          228: ger:pl:loc:n:perf:aff
+          229: ger:pl:nom:n:imperf:aff
+          230: ger:pl:nom:n:perf:aff
+          231: ger:sg:acc:n:imperf:aff
+          232: ger:sg:acc:n:imperf:neg
+          233: ger:sg:acc:n:perf:aff
+          234: ger:sg:acc:n:perf:neg
+          235: ger:sg:dat:n:imperf:aff
+          236: ger:sg:dat:n:perf:aff
+          237: ger:sg:dat:n:perf:neg
+          238: ger:sg:gen:n:imperf:aff
+          239: ger:sg:gen:n:imperf:neg
+          240: ger:sg:gen:n:perf:aff
+          241: ger:sg:gen:n:perf:neg
+          242: ger:sg:inst:n:imperf:aff
+          243: ger:sg:inst:n:imperf:neg
+          244: ger:sg:inst:n:perf:aff
+          245: ger:sg:inst:n:perf:neg
+          246: ger:sg:loc:n:imperf:aff
+          247: ger:sg:loc:n:imperf:neg
+          248: ger:sg:loc:n:perf:aff
+          249: ger:sg:loc:n:perf:neg
+          250: ger:sg:nom:n:imperf:aff
+          251: ger:sg:nom:n:imperf:neg
+          252: ger:sg:nom:n:perf:aff
+          253: ger:sg:nom:n:perf:neg
+          254: imps:imperf
+          255: imps:perf
+          256: impt:pl:pri:imperf
+          257: impt:pl:pri:perf
+          258: impt:pl:sec:imperf
+          259: impt:pl:sec:perf
+          260: impt:sg:pri:imperf
+          261: impt:sg:sec:imperf
+          262: impt:sg:sec:perf
+          263: inf:imperf
+          264: inf:perf
+          265: interj
+          266: interp
+          267: num:pl:acc:f:congr:ncol
+          268: num:pl:acc:f:rec
+          269: num:pl:acc:f:rec:ncol
+          270: num:pl:acc:m1:rec
+          271: num:pl:acc:m1:rec:col
+          272: num:pl:acc:m1:rec:ncol
+          273: num:pl:acc:m2:congr:ncol
+          274: num:pl:acc:m2:rec
+          275: num:pl:acc:m2:rec:ncol
+          276: num:pl:acc:m3:congr
+          277: num:pl:acc:m3:congr:ncol
+          278: num:pl:acc:m3:rec
+          279: num:pl:acc:m3:rec:ncol
+          280: num:pl:acc:n:congr:ncol
+          281: num:pl:acc:n:rec
+          282: num:pl:acc:n:rec:col
+          283: num:pl:acc:n:rec:ncol
+          284: num:pl:dat:f:congr
+          285: num:pl:dat:f:congr:ncol
+          286: num:pl:dat:m1:congr
+          287: num:pl:dat:m1:congr:col
+          288: num:pl:dat:m1:congr:ncol
+          289: num:pl:dat:m2:congr
+          290: num:pl:dat:m3:congr:ncol
+          291: num:pl:dat:n:congr
+          292: num:pl:dat:n:congr:ncol
+          293: num:pl:gen:f:congr
+          294: num:pl:gen:f:congr:ncol
+          295: num:pl:gen:f:rec
+          296: num:pl:gen:f:rec:ncol
+          297: num:pl:gen:m1:congr
+          298: num:pl:gen:m1:congr:ncol
+          299: num:pl:gen:m1:rec
+          300: num:pl:gen:m1:rec:col
+          301: num:pl:gen:m2:congr
+          302: num:pl:gen:m2:congr:ncol
+          303: num:pl:gen:m2:rec
+          304: num:pl:gen:m3:congr
+          305: num:pl:gen:m3:congr:ncol
+          306: num:pl:gen:m3:rec
+          307: num:pl:gen:m3:rec:ncol
+          308: num:pl:gen:n:congr
+          309: num:pl:gen:n:congr:ncol
+          310: num:pl:gen:n:rec
+          311: num:pl:gen:n:rec:col
+          312: num:pl:inst:f:congr
+          313: num:pl:inst:f:congr:ncol
+          314: num:pl:inst:m1:congr
+          315: num:pl:inst:m1:congr:ncol
+          316: num:pl:inst:m1:rec:col
+          317: num:pl:inst:m2:congr
+          318: num:pl:inst:m2:congr:ncol
+          319: num:pl:inst:m3:congr
+          320: num:pl:inst:m3:congr:ncol
+          321: num:pl:inst:n:congr
+          322: num:pl:inst:n:congr:ncol
+          323: num:pl:inst:n:rec:col
+          324: num:pl:loc:f:congr
+          325: num:pl:loc:f:congr:ncol
+          326: num:pl:loc:m1:congr
+          327: num:pl:loc:m1:congr:ncol
+          328: num:pl:loc:m2:congr
+          329: num:pl:loc:m2:congr:ncol
+          330: num:pl:loc:m3:congr
+          331: num:pl:loc:m3:congr:ncol
+          332: num:pl:loc:n:congr
+          333: num:pl:loc:n:congr:ncol
+          334: num:pl:nom:f:congr:ncol
+          335: num:pl:nom:f:rec
+          336: num:pl:nom:f:rec:ncol
+          337: num:pl:nom:m1:congr:ncol
+          338: num:pl:nom:m1:rec
+          339: num:pl:nom:m1:rec:col
+          340: num:pl:nom:m1:rec:ncol
+          341: num:pl:nom:m2:congr:ncol
+          342: num:pl:nom:m2:rec
+          343: num:pl:nom:m2:rec:ncol
+          344: num:pl:nom:m3:congr:ncol
+          345: num:pl:nom:m3:rec
+          346: num:pl:nom:m3:rec:ncol
+          347: num:pl:nom:n:congr
+          348: num:pl:nom:n:congr:ncol
+          349: num:pl:nom:n:rec
+          350: num:pl:nom:n:rec:col
+          351: num:pl:nom:n:rec:ncol
+          352: num:sg:acc:f:rec
+          353: num:sg:acc:f:rec:ncol
+          354: num:sg:acc:m1:rec:ncol
+          355: num:sg:acc:m2:rec
+          356: num:sg:acc:m3:rec
+          357: num:sg:acc:m3:rec:ncol
+          358: num:sg:acc:n:rec
+          359: num:sg:gen:f:rec
+          360: num:sg:gen:m3:rec
+          361: num:sg:gen:n:rec
+          362: num:sg:inst:m3:rec
+          363: num:sg:loc:f:rec
+          364: num:sg:loc:m3:congr
+          365: num:sg:loc:m3:rec
+          366: num:sg:nom:f:rec
+          367: num:sg:nom:m2:rec
+          368: num:sg:nom:m3:rec
+          369: num:sg:nom:m3:rec:ncol
+          370: num:sg:nom:n:rec
+          371: numcomp
+          372: pact:pl:acc:f:imperf:aff
+          373: pact:pl:acc:f:imperf:neg
+          374: pact:pl:acc:m1:imperf:aff
+          375: pact:pl:acc:m2:imperf:aff
+          376: pact:pl:acc:m3:imperf:aff
+          377: pact:pl:acc:m3:imperf:neg
+          378: pact:pl:acc:n:imperf:aff
+          379: pact:pl:acc:n:imperf:neg
+          380: pact:pl:dat:f:imperf:aff
+          381: pact:pl:dat:m1:imperf:aff
+          382: pact:pl:dat:m2:imperf:aff
+          383: pact:pl:dat:m3:imperf:aff
+          384: pact:pl:dat:n:imperf:aff
+          385: pact:pl:gen:f:imperf:aff
+          386: pact:pl:gen:f:imperf:neg
+          387: pact:pl:gen:m1:imperf:aff
+          388: pact:pl:gen:m1:imperf:neg
+          389: pact:pl:gen:m2:imperf:aff
+          390: pact:pl:gen:m3:imperf:aff
+          391: pact:pl:gen:m3:imperf:neg
+          392: pact:pl:gen:n:imperf:aff
+          393: pact:pl:inst:f:imperf:aff
+          394: pact:pl:inst:m1:imperf:aff
+          395: pact:pl:inst:m2:imperf:aff
+          396: pact:pl:inst:m3:imperf:aff
+          397: pact:pl:inst:m3:imperf:neg
+          398: pact:pl:inst:n:imperf:aff
+          399: pact:pl:inst:n:imperf:neg
+          400: pact:pl:loc:f:imperf:aff
+          401: pact:pl:loc:m1:imperf:aff
+          402: pact:pl:loc:m3:imperf:aff
+          403: pact:pl:loc:m3:imperf:neg
+          404: pact:pl:loc:n:imperf:aff
+          405: pact:pl:loc:n:imperf:neg
+          406: pact:pl:nom:f:imperf:aff
+          407: pact:pl:nom:f:imperf:neg
+          408: pact:pl:nom:m1:imperf:aff
+          409: pact:pl:nom:m2:imperf:aff
+          410: pact:pl:nom:m3:imperf:aff
+          411: pact:pl:nom:n:imperf:aff
+          412: pact:pl:nom:n:imperf:neg
+          413: pact:pl:voc:f:imperf:aff
+          414: pact:sg:acc:f:imperf:aff
+          415: pact:sg:acc:f:imperf:neg
+          416: pact:sg:acc:m1:imperf:aff
+          417: pact:sg:acc:m2:imperf:aff
+          418: pact:sg:acc:m3:imperf:aff
+          419: pact:sg:acc:n:imperf:aff
+          420: pact:sg:acc:n:imperf:neg
+          421: pact:sg:dat:f:imperf:aff
+          422: pact:sg:dat:m1:imperf:aff
+          423: pact:sg:dat:m2:imperf:aff
+          424: pact:sg:dat:m3:imperf:aff
+          425: pact:sg:dat:n:imperf:aff
+          426: pact:sg:gen:f:imperf:aff
+          427: pact:sg:gen:f:imperf:neg
+          428: pact:sg:gen:m1:imperf:aff
+          429: pact:sg:gen:m1:imperf:neg
+          430: pact:sg:gen:m2:imperf:aff
+          431: pact:sg:gen:m3:imperf:aff
+          432: pact:sg:gen:m3:imperf:neg
+          433: pact:sg:gen:n:imperf:aff
+          434: pact:sg:gen:n:imperf:neg
+          435: pact:sg:inst:f:imperf:aff
+          436: pact:sg:inst:f:imperf:neg
+          437: pact:sg:inst:m1:imperf:aff
+          438: pact:sg:inst:m1:imperf:neg
+          439: pact:sg:inst:m2:imperf:aff
+          440: pact:sg:inst:m2:imperf:neg
+          441: pact:sg:inst:m3:imperf:aff
+          442: pact:sg:inst:m3:imperf:neg
+          443: pact:sg:inst:n:imperf:aff
+          444: pact:sg:loc:f:imperf:aff
+          445: pact:sg:loc:f:imperf:neg
+          446: pact:sg:loc:m1:imperf:aff
+          447: pact:sg:loc:m2:imperf:aff
+          448: pact:sg:loc:m3:imperf:aff
+          449: pact:sg:loc:m3:imperf:neg
+          450: pact:sg:loc:n:imperf:aff
+          451: pact:sg:loc:n:imperf:neg
+          452: pact:sg:nom:f:imperf:aff
+          453: pact:sg:nom:f:imperf:neg
+          454: pact:sg:nom:m1:imperf:aff
+          455: pact:sg:nom:m1:imperf:neg
+          456: pact:sg:nom:m2:imperf:aff
+          457: pact:sg:nom:m3:imperf:aff
+          458: pact:sg:nom:m3:imperf:neg
+          459: pact:sg:nom:n:imperf:aff
+          460: pact:sg:nom:n:imperf:neg
+          461: pact:sg:voc:m1:imperf:aff
+          462: pacta
+          463: pant:perf
+          464: part
+          465: part:nwok
+          466: part:wok
+          467: pcon:imperf
+          468: ppas:pl:acc:f:imperf:aff
+          469: ppas:pl:acc:f:perf:aff
+          470: ppas:pl:acc:f:perf:neg
+          471: ppas:pl:acc:m1:imperf:aff
+          472: ppas:pl:acc:m1:imperf:neg
+          473: ppas:pl:acc:m1:perf:aff
+          474: ppas:pl:acc:m1:perf:neg
+          475: ppas:pl:acc:m2:imperf:aff
+          476: ppas:pl:acc:m2:perf:aff
+          477: ppas:pl:acc:m3:imperf:aff
+          478: ppas:pl:acc:m3:perf:aff
+          479: ppas:pl:acc:m3:perf:neg
+          480: ppas:pl:acc:n:imperf:aff
+          481: ppas:pl:acc:n:imperf:neg
+          482: ppas:pl:acc:n:perf:aff
+          483: ppas:pl:acc:n:perf:neg
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+          866: praet:sg:m1:imperf
+          867: praet:sg:m1:imperf:agl
+          868: praet:sg:m1:imperf:nagl
+          869: praet:sg:m1:perf
+          870: praet:sg:m1:perf:agl
+          871: praet:sg:m1:perf:nagl
+          872: praet:sg:m2:imperf
+          873: praet:sg:m2:imperf:nagl
+          874: praet:sg:m2:perf
+          875: praet:sg:m2:perf:nagl
+          876: praet:sg:m3:imperf
+          877: praet:sg:m3:imperf:nagl
+          878: praet:sg:m3:perf
+          879: praet:sg:m3:perf:nagl
+          880: praet:sg:n:imperf
+          881: praet:sg:n:perf
+          882: pred
+          883: prep:acc
+          884: prep:acc:nwok
+          885: prep:acc:wok
+          886: prep:dat
+          887: prep:gen
+          888: prep:gen:nwok
+          889: prep:gen:wok
+          890: prep:inst
+          891: prep:inst:nwok
+          892: prep:inst:wok
+          893: prep:loc
+          894: prep:loc:nwok
+          895: prep:loc:wok
+          896: prep:nom
+          897: romandig
+          898: siebie:acc
+          899: siebie:dat
+          900: siebie:gen
+          901: siebie:inst
+          902: siebie:loc
+          903: subst:pl:acc:f
+          904: subst:pl:acc:m1
+          905: subst:pl:acc:m1:pt
+          906: subst:pl:acc:m2
+          907: subst:pl:acc:m3
+          908: subst:pl:acc:n:col
+          909: subst:pl:acc:n:ncol
+          910: subst:pl:acc:n:pt
+          911: subst:pl:dat:f
+          912: subst:pl:dat:m1
+          913: subst:pl:dat:m1:pt
+          914: subst:pl:dat:m2
+          915: subst:pl:dat:m3
+          916: subst:pl:dat:n:col
+          917: subst:pl:dat:n:ncol
+          918: subst:pl:dat:n:pt
+          919: subst:pl:gen:f
+          920: subst:pl:gen:m1
+          921: subst:pl:gen:m1:pt
+          922: subst:pl:gen:m2
+          923: subst:pl:gen:m3
+          924: subst:pl:gen:n:col
+          925: subst:pl:gen:n:ncol
+          926: subst:pl:gen:n:pt
+          927: subst:pl:inst:f
+          928: subst:pl:inst:m1
+          929: subst:pl:inst:m1:pt
+          930: subst:pl:inst:m2
+          931: subst:pl:inst:m3
+          932: subst:pl:inst:n:col
+          933: subst:pl:inst:n:ncol
+          934: subst:pl:inst:n:pt
+          935: subst:pl:loc:f
+          936: subst:pl:loc:m1
+          937: subst:pl:loc:m1:pt
+          938: subst:pl:loc:m2
+          939: subst:pl:loc:m3
+          940: subst:pl:loc:n:col
+          941: subst:pl:loc:n:ncol
+          942: subst:pl:loc:n:pt
+          943: subst:pl:nom:f
+          944: subst:pl:nom:m1
+          945: subst:pl:nom:m1:pt
+          946: subst:pl:nom:m2
+          947: subst:pl:nom:m3
+          948: subst:pl:nom:n:col
+          949: subst:pl:nom:n:ncol
+          950: subst:pl:nom:n:pt
+          951: subst:pl:voc:f
+          952: subst:pl:voc:m1
+          953: subst:pl:voc:m1:pt
+          954: subst:pl:voc:m3
+          955: subst:pl:voc:n:col
+          956: subst:pl:voc:n:ncol
+          957: subst:pl:voc:n:pt
+          958: subst:sg:acc:f
+          959: subst:sg:acc:m1
+          960: subst:sg:acc:m2
+          961: subst:sg:acc:m3
+          962: subst:sg:acc:n:col
+          963: subst:sg:acc:n:ncol
+          964: subst:sg:dat:f
+          965: subst:sg:dat:m1
+          966: subst:sg:dat:m2
+          967: subst:sg:dat:m3
+          968: subst:sg:dat:n:col
+          969: subst:sg:dat:n:ncol
+          970: subst:sg:gen:f
+          971: subst:sg:gen:m1
+          972: subst:sg:gen:m2
+          973: subst:sg:gen:m3
+          974: subst:sg:gen:n:col
+          975: subst:sg:gen:n:ncol
+          976: subst:sg:inst:f
+          977: subst:sg:inst:m1
+          978: subst:sg:inst:m2
+          979: subst:sg:inst:m3
+          980: subst:sg:inst:n:col
+          981: subst:sg:inst:n:ncol
+          982: subst:sg:loc:f
+          983: subst:sg:loc:m1
+          984: subst:sg:loc:m2
+          985: subst:sg:loc:m3
+          986: subst:sg:loc:n:col
+          987: subst:sg:loc:n:ncol
+          988: subst:sg:nom:f
+          989: subst:sg:nom:m1
+          990: subst:sg:nom:m2
+          991: subst:sg:nom:m3
+          992: subst:sg:nom:n:col
+          993: subst:sg:nom:n:ncol
+          994: subst:sg:voc:f
+          995: subst:sg:voc:m1
+          996: subst:sg:voc:m2
+          997: subst:sg:voc:m3
+          998: subst:sg:voc:n:col
+          999: subst:sg:voc:n:ncol
+          1000: sym
+          1001: winien:pl:f:imperf
+          1002: winien:pl:m1:imperf
+          1003: winien:pl:m2:imperf
+          1004: winien:pl:m3:imperf
+          1005: winien:pl:n:imperf
+          1006: winien:sg:f:imperf
+          1007: winien:sg:m1:imperf
+          1008: winien:sg:m2:imperf
+          1009: winien:sg:m3:imperf
+          1010: winien:sg:n:imperf
+          1011: xxs:acc
+          1012: xxs:dat
+          1013: xxs:gen
+          1014: xxs:inst
+          1015: xxs:loc
+          1016: xxs:nom
+          1017: xxs:voc
+          1018: xxx
+  - name: nps
+    sequence: bool
+  - name: nkjp_ids
+    sequence: string
+  config_name: nkjp1m
+  splits:
+  - name: test
+    num_bytes: 8324533
+    num_examples: 8964
+  - name: train
+    num_bytes: 65022406
+    num_examples: 68943
+  - name: validation
+    num_bytes: 7465442
+    num_examples: 7755
+  download_size: 16167009
+  dataset_size: 80812381
+---
+# Dataset Card for NKJP1M – The manually annotated subcorpus of the National Corpus of Polish
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [NKJP1M](http://clip.ipipan.waw.pl/NationalCorpusOfPolish)
+- **Repository:** [NKJP1M-SGJP](http://download.sgjp.pl/morfeusz/current/)
+- **Paper:** [NKJP book](http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf)
+- **Point of Contact:** mailto:morfeusz@ipipan.waw.pl
+
+### Dataset Summary
+
+This is the official dataset for NKJP1M – the 1-million token balanced subcorpus of the National Corpus of Polish (Narodowy Korpus Języka Polskiego)
+
+Besides the text (divided into paragraphs/samples and sentences) the set contains lemmas and morpho-syntactic tags for all tokens in the corpus.
+
+This release, known as NKJP1M-SGJP, corresponds to the version 1.2 of the corpus with later corrections and improvements. In particular the morpho-syntactic annotation has been aligned with the present version of Morfeusz2 SGJP morphological analyser (as of 2022.12.04).
+
+### Supported Tasks and Leaderboards
+
+The main use of this resource lays in training models for lemmatisation and part of speech tagging of Polish.
+
+### Languages
+
+Polish (monolingual)
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{'nkjp_text': 'NKJP_1M_1102000002',
+ 'nkjp_par': 'morph_1-p',
+ 'nkjp_sent': 'morph_1.18-s',
+ 'tokens': ['-', 'Nie', 'mam', 'pieniędzy', ',', 'da', 'mi', 'pani', 'wywiad', '?'],
+ 'lemmas': ['-', 'nie', 'mieć', 'pieniądz', ',', 'dać', 'ja', 'pani', 'wywiad', '?'],
+ 'cposes': [8, 11, 10, 9, 8, 10, 9, 9, 9, 8],
+ 'poses': [19, 25, 12, 35, 19, 12, 28, 35, 35, 19],
+ 'tags': [266, 464, 213, 923, 266, 218, 692, 988, 961, 266],
+ 'nps': [False, False, False, False, True, False, False, False, False, True],
+ 'nkjp_ids': ['morph_1.9-seg', 'morph_1.10-seg', 'morph_1.11-seg', 'morph_1.12-seg', 'morph_1.13-seg', 'morph_1.14-seg', 'morph_1.15-seg', 'morph_1.16-seg', 'morph_1.17-seg', 'morph_1.18-seg']}
+```
+
+### Data Fields
+
+- `nkjp_text`, `nkjp_par`, `nkjp_sent` (strings): XML identifiers of the present text (document), paragraph and sentence in NKJP. (These allow to map the data point back to the source corpus and to identify paragraphs/samples.)
+- `tokens` (sequence of strings): tokens of the text defined as in NKJP.
+- `lemmas` (sequence of strings): lemmas corresponding to the tokens.
+- `tags` (sequence of labels): morpho-syntactic tags according to Morfeusz2 tagset (1019 distinct tags).
+- `poses` (sequence of labels): flexemic class (detailed part of speech, 40 classes) – the first element of the corresponding tag.
+- `cposes` (sequence of labels): coarse part of speech (13 classes): all verbal and deverbal flexemic classes get mapped to a `V`, nominal – `N`, adjectival – `A`, “strange” (abbreviations, alien elements, symbols, emojis…)  – `X`, rest as in `poses`.
+- `nps` (sequence of booleans): `True` means that the corresponding token is not preceded by a space in the source text.
+- `nkjp_ids` (sequence of strings): XML identifiers of particular tokens in NKJP (probably an overkill).
+
+### Data Splits
+
+|           | Train  | Validation | Test   |
+| -----     | ------ | -----      | ----   |
+| sentences | 68943  | 7755       |  8964  |
+| tokens    | 978368 | 112454     | 125059 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+The National Corpus of Polish (NKJP) was envisioned as the reference corpus of contemporary Polish.
+
+The manually annotated subcorpus (NKJP1M) was thought of as the training data for various NLP tasks.
+
+### Source Data
+
+NKJP is balanced with respect to Polish readership.  The detailed rationale is described in Chapter 3 of the [NKJP book](http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf) (roughly: 50% press, 30% books, 10% speech, 10% other).  The corpus contains texts from the years 1945–2010 (with 80% of the text in the range 1990–2010).  Only original Polish texts were gathered (no translations from other languages). The composition of NKJP1M follows this schema (see Chapter 5).
+
+### Annotations
+
+The rules of morphosyntactic annotation used for NKJP are discussed in Chapter 6 of the [NKJP book](http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf).  Presently (2020), the corpus uses a common tagset with the morphological analyzer [Morfeusz 2](http://morfeusz.sgjp.pl/).
+
+#### Annotation process
+
+The texts were processed with Morfeusz and then the resulting annotations were manually disambiguated and validated/corrected. Each text sample was independently processed by two annotators. In case of annotation conflicts an adjudicator stepped in.
+
+### Licensing Information
+
+![Creative Commons License](https://i.creativecommons.org/l/by/4.0/80x15.png) This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
+
+### Citation Information
+
+Info on the source corpus: [link](http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf)
+
+```
+@Book{nkjp:12,
+  editor =       "Adam Przepiórkowski and Mirosław Bańko and Rafał
+                  L. Górski and Barbara Lewandowska-Tomaszczyk",
+  title =        "Narodowy Korpus Języka Polskiego",
+  year =         2012,
+  address =      "Warszawa",
+  pdf =          "http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf",
+  publisher =    "Wydawnictwo Naukowe PWN"}
+```
+
+Current annotation scheme: [link](https://jezyk-polski.pl/index.php/jp/article/view/72)
+
+```
+@article{
+    kie:etal:21,
+    author = "Kieraś, Witold and Woliński, Marcin and Nitoń, Bartłomiej",
+    doi = "https://doi.org/10.31286/JP.101.2.5",
+    title = "Nowe wielowarstwowe znakowanie lingwistyczne zrównoważonego {N}arodowego {K}orpusu {J}ęzyka {P}olskiego",
+    url = "https://jezyk-polski.pl/index.php/jp/article/view/72",
+    journal = "Język Polski",
+    number = "2",
+    volume = "CI",
+    year = "2021",
+    pages = "59--70"
+}
+```
+
+<!--
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
+-->

huggingface_dataset/Dataset_Card/irds_beir_msmarco_test.md

@@ -0,0 +1,63 @@
+---
+pretty_name: '`beir/msmarco/test`'
+viewer: false
+source_datasets: ['irds/beir_msmarco']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `beir/msmarco/test`
+
+The `beir/msmarco/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/msmarco/test).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=43
+ - `qrels`: (relevance assessments); count=9,260
+
+ - For `docs`, use [`irds/beir_msmarco`](https://huggingface.co/datasets/irds/beir_msmarco)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/beir_msmarco_test', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/beir_msmarco_test', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@inproceedings{Craswell2019TrecDl,
+  title={Overview of the TREC 2019 deep learning track},
+  author={Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Campos and Ellen Voorhees},
+  booktitle={TREC 2019},
+  year={2019}
+}
+@inproceedings{Bajaj2016Msmarco,
+  title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
+  author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
+  booktitle={InCoCo@NIPS},
+  year={2016}
+}
+@article{Thakur2021Beir,
+  title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
+  author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", 
+  journal= "arXiv preprint arXiv:2104.08663",
+  month = "4",
+  year = "2021",
+  url = "https://arxiv.org/abs/2104.08663",
+}
+```

huggingface_dataset/Dataset_Card/irds_mmarco_ru.md

@@ -0,0 +1,46 @@
+---
+pretty_name: '`mmarco/ru`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mmarco/ru`
+
+The `mmarco/ru` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/ru).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=8,841,823
+
+
+This dataset is used by: [`mmarco_ru_dev`](https://huggingface.co/datasets/irds/mmarco_ru_dev), [`mmarco_ru_train`](https://huggingface.co/datasets/irds/mmarco_ru_train)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/mmarco_ru', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@article{Bonifacio2021MMarco,
+    title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
+    author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
+    year={2021},
+    journal={arXiv:2108.13897}
+}
+```

huggingface_dataset/Dataset_Card/merkalo-ziri_qa_shreded.md

@@ -0,0 +1,96 @@
+---
+annotations_creators:
+- found
+language:
+- rus
+language_creators:
+- found
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: qa_main
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags: []
+task_categories:
+- question-answering
+task_ids: []
+---
+# Dataset Card for [Dataset Name]
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+## Dataset Description
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+### Dataset Summary
+[More Information Needed]
+### Supported Tasks and Leaderboards
+[More Information Needed]
+### Languages
+[More Information Needed]
+## Dataset Structure
+### Data Instances
+[More Information Needed]
+### Data Fields
+[More Information Needed]
+### Data Splits
+[More Information Needed]
+## Dataset Creation
+### Curation Rationale
+[More Information Needed]
+### Source Data
+#### Initial Data Collection and Normalization
+[More Information Needed]
+#### Who are the source language producers?
+[More Information Needed]
+### Annotations
+#### Annotation process
+[More Information Needed]
+#### Who are the annotators?
+[More Information Needed]
+### Personal and Sensitive Information
+[More Information Needed]
+## Considerations for Using the Data
+### Social Impact of Dataset
+[More Information Needed]
+### Discussion of Biases
+[More Information Needed]
+### Other Known Limitations
+[More Information Needed]
+## Additional Information
+### Dataset Curators
+[More Information Needed]
+### Licensing Information
+[More Information Needed]
+### Citation Information
+[More Information Needed]
+### Contributions
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

huggingface_dataset/Dataset_Card/qgallouedec_prj_gia_dataset_metaworld_pick_place_wall_v2_1111.md

@@ -0,0 +1,36 @@
+---
+library_name: gia
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- gia
+- multi-task
+- multi-modal
+- imitation-learning
+- offline-reinforcement-learning
+---
+
+An imitation learning environment for the pick-place-wall-v2 environment, sample for the policy pick-place-wall-v2 
+
+This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia 
+
+
+
+
+## Load dataset
+
+First, clone it with
+
+```sh
+git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_pick_place_wall_v2_1111
+```
+
+Then, load it with
+
+```python
+import numpy as np
+dataset = np.load("prj_gia_dataset_metaworld_pick_place_wall_v2_1111/dataset.npy", allow_pickle=True).item()
+print(dataset.keys())  # dict_keys(['observations', 'actions', 'dones', 'rewards'])
+```
+
+    

huggingface_dataset/Dataset_Card/ro_sts.md

@@ -0,0 +1,180 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- ro
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|other-sts-b
+task_categories:
+- text-classification
+task_ids:
+- text-scoring
+- semantic-similarity-scoring
+paperswithcode_id: null
+pretty_name: RO-STS
+dataset_info:
+  features:
+  - name: score
+    dtype: float32
+  - name: sentence1
+    dtype: string
+  - name: sentence2
+    dtype: string
+  config_name: ro_sts
+  splits:
+  - name: train
+    num_bytes: 879073
+    num_examples: 5749
+  - name: test
+    num_bytes: 194330
+    num_examples: 1379
+  - name: validation
+    num_bytes: 245926
+    num_examples: 1500
+  download_size: 1267607
+  dataset_size: 1319329
+---
+
+# Dataset Card for RO-STS
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [GitHub](https://github.com/dumitrescustefan/RO-STS)
+- **Repository:** [GitHub](https://github.com/dumitrescustefan/RO-STS)
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [email](dumitrescu.stefan@gmail.com)
+
+### Dataset Summary
+
+We present RO-STS - the Semantic Textual Similarity dataset for the Romanian language. It is a high-quality translation of the [STS English dataset](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). RO-STS contains 8,628 sentence pairs with their similarity scores. The original English sentences were collected from news headlines, captions of images and user forums, and are categorized accordingly. The Romanian release follows this categorization and provides the same train/validation/test split with 5,749/1,500/1,379 sentence pairs in each subset.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+The text dataset is in Romanian (`ro`)
+
+## Dataset Structure
+
+### Data Instances
+
+An example looks like this:
+
+```
+{'score': 1.5,
+ 'sentence1': 'Un bărbat cântă la harpă.',
+ 'sentence2': 'Un bărbat cântă la claviatură.',
+}
+```
+
+### Data Fields
+
+- `score`: a float representing the semantic similarity score where 0.0 is the lowest score and 5.0 is the highest
+- `sentence1`: a string representing a text
+- `sentence2`: another string to compare the previous text with
+
+### Data Splits
+
+The train/validation/test split contain 5,749/1,500/1,379 sentence pairs.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+[Needs More Information]
+
+#### Initial Data Collection and Normalization
+
+*To construct the dataset, we first obtained automatic translations using Google's translation engine. These were then manually checked, corrected, and cross-validated by human volunteers. *
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+#### Who are the annotators?
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+CC BY-SA 4.0 License
+
+### Citation Information
+
+```
+@inproceedings{dumitrescu2021liro,
+  title={Liro: Benchmark and leaderboard for romanian language tasks},
+  author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and others},
+  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
+  year={2021}
+}
+```
+
+### Contributions
+
+Thanks to [@lorinczb](https://github.com/lorinczb) for adding this dataset.

huggingface_dataset/Dataset_Card/society-ethics_papers.md

@@ -0,0 +1,30 @@
+---
+tags:
+- ethics
+---
+# Hugging Face Ethics & Society Papers
+
+This is an incomplete list of ethics-related papers published by researchers at Hugging Face.
+
+- Gradio: https://arxiv.org/abs/1906.02569
+- DistilBERT: http://arxiv.org/abs/1910.01108
+- RAFT: https://arxiv.org/abs/2109.14076
+- Interactive Model Cards: https://arxiv.org/abs/2205.02894
+- Data Governance in the Age of Large-Scale Data-Driven Language Technology: https://arxiv.org/abs/2206.03216
+- Quality at a Glance: https://arxiv.org/abs/2103.12028
+- A Framework for Deprecating Datasets: https://arxiv.org/abs/2111.04424
+- Bugs in the Data: http://arxiv.org/abs/2208.11695
+- Measuring Data: http://arxiv.org/abs/2212.05129
+- Perturbation Augmentation for Fairer NLP: http://arxiv.org/abs/2205.12586
+- SEAL: http://arxiv.org/abs/2210.05839
+- Multitask Prompted Training Enables Zero-Shot Task Generalization: http://arxiv.org/abs/2110.08207
+- BLOOM: https://arxiv.org/abs/2211.05100
+- ROOTS: https://arxiv.org/abs/2303.03915
+- Evaluate & Evaluation on the Hub: http://arxiv.org/abs/2210.01970
+- Spacerini: http://arxiv.org/abs/2302.14534
+- ROOTS Search Tool: https://arxiv.org/abs/2302.14035
+- Fair Diffusion: http://arxiv.org/abs/2302.10893
+- Counting Carbon: http://arxiv.org/abs/2302.08476
+- The Gradient of Generative AI Release: http://arxiv.org/abs/2302.04844
+- BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model: https://arxiv.org/abs/2212.04960
+- Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives: https://arxiv.org/abs/2301.08488

huggingface_dataset/Dataset_Card/tomekkorbak_pile-chunk-toxicity-scored-3.md

@@ -0,0 +1 @@
+A chunk 3 of the Pile (2.2m documents) scored using the Perspective API (on May 18-20 2022)

huggingface_dataset/Dataset_Card/trustwallet_24.md

@@ -0,0 +1,4 @@
+---
+license: artistic-2.0
+---
+crypto Trust**wallet customer service Support Number +**1-**818-869-**2884