author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1 class | downloads float64 1 1M ⌀ | gated bool 2 classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2 classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
chenghao | null | null | null | false | null | false | chenghao/ledgar_qa | 2022-09-14T00:26:36.000Z | null | false | 8f10e489090968f4bcef4cff3ff53487cb2e9a01 | [] | [
"license:mit"
] | https://huggingface.co/datasets/chenghao/ledgar_qa/resolve/main/README.md | ---
license: mit
---
|
teletubbee | null | null | null | false | null | false | teletubbee/bees | 2022-09-14T04:27:04.000Z | null | false | dde2c6e5c48757257ad9e4c7db754e29d439c240 | [] | [
"license:cc"
] | https://huggingface.co/datasets/teletubbee/bees/resolve/main/README.md | ---
license: cc
---
|
Fhrozen | null | null | null | false | null | false | Fhrozen/CABankSakura | 2022-09-20T15:36:00.000Z | null | false | 6179895822186e195f7a673d70a6c1aaeaa03a39 | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:ja",
"license:cc",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:found",
"task_categories:automatic-speech-recognition",
"task_categories:audio-... | https://huggingface.co/datasets/Fhrozen/CABankSakura/resolve/main/README.md | ---
pretty_name: banksakura
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ja
license:
- cc
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- found
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- automatic-speech-recognition
- speech-recognition
- speaker-identification
---
# CABank Japanese Sakura Corpus
- Susanne Miyata
- Department of Medical Sciences
- Aichi Shukotoku University
- smiyata@asu.aasa.ac.jp
- website: https://ca.talkbank.org/access/Sakura.html
## Important
This data set is a copy from the original one located at https://ca.talkbank.org/access/Sakura.html.
## Details
- Participants: 31
- Type of Study: xxx
- Location: Japan
- Media type: audio
- DOI: doi:10.21415/T5M90R
## Citation information
Some citation here.
In accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references.
## Project Description
This corpus of 18 conversations is the product of six graduation theses on gender differences in students' group talk. Each conversation lasted between 12 and 35 minutes (avg. 25 minutes) resulting in an overall time of 7 hours and 30 minutes. 31 Students (19 female, 12 male) participated in the study (Table 1). The participants gathered in groups of 4 students, either of the same or the opposite sex (6 conversations with a group of 4 female students, 6 with 4 male students, and 6 conversations with 2 male and 2 female students), according to age (first and third year students) and affiliation (two academic departments). In addition, the participants of each conversation came from the same small-sized class and were well acquainted.
The participants were informed that their conversations may be transcribed and a video recorded for use in possible publication when recruited. Additionally, permission was asked once more after the transcription in cases where either private information had been displayed, or a misunderstanding concerning the nature and degree of the publication of the conversations became apparent during the conversation.
The recordings took place in a small conference room at the university between or after lectures. The participants were given a card with a conversation topic to start with, but were free to vary (topic 1 "What do you expect from an opposite sex friend?" [isee ni motomeru koto]; topic 2 "Are you a dog lover or a cat lover?" [inuha ka nekoha ka]; topic 3 "About part-time work" [arubaito ni tsuite]). The investigator was not present during the recording. The combination of participants, the topic, and the duration of the 18 conversations are given in Table 2.
The participants produced 15,449 utterances overall (female: 8,027 utterances, male: 7,422 utterances). All utterances were linked to video and transcribed in regular Japanese orthography and Latin script (Wakachi2002), and provided with morphological tags (JMOR04.1). Proper names were replaced by pseudonyms.
## Acknowledgements
Additional contributors: Banno, Kyoko; Konishi, Saya; Matsui, Ayumi; Matsumoto, Shiori; Oogi, Rie; Takahashi, Akane; Muraki, Kyoko.
|
Fhrozen | null | null | null | false | null | false | Fhrozen/CABankSakuraCHJP | 2022-09-20T15:20:21.000Z | null | false | 583ad2d23e82e94ac31772ce432a0c515a4ad51d | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:ja",
"license:cc",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:found",
"task_categories:automatic-speech-recognition",
"task_categories:audio-... | https://huggingface.co/datasets/Fhrozen/CABankSakuraCHJP/resolve/main/README.md | ---
pretty_name: banksakura
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ja
license:
- cc
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- found
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- automatic-speech-recognition
- speech-recognition
- speaker-identification
---
# CABank Japanese CallHome Corpus
- Participants: 120
- Type of Study: phone call
- Location: United States
- Media type: audio
- DOI: doi:10.21415/T5H59V
- Web: https://ca.talkbank.org/access/CallHome/jpn.html
## Citation information
Some citation here.
In accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references.
## Project Description
This is the Japanese portion of CallHome.
Speakers were solicited by the LDC to participate in this telephone speech collection effort via the internet, publications (advertisements), and personal contacts. A total of 200 call originators were found, each of whom placed a telephone call via a toll-free robot operator maintained by the LDC. Access to the robot operator was possible via a unique Personal Identification Number (PIN) issued by the recruiting staff at the LDC when the caller enrolled in the project. The participants were made aware that their telephone call would be recorded, as were the call recipients. The call was allowed only if both parties agreed to being recorded. Each caller was allowed to talk up to 30 minutes. Upon successful completion of the call, the caller was paid $20 (in addition to making a free long-distance telephone call). Each caller was allowed to place only one telephone call.
Although the goal of the call collection effort was to have unique speakers in all calls, a handful of repeat speakers are included in the corpus. In all, 200 calls were transcribed. Of these, 80 have been designated as training calls, 20 as development test calls, and 100 as evaluation test calls. For each of the training and development test calls, a contiguous 10-minute region was selected for transcription; for the evaluation test calls, a 5-minute region was transcribed. For the present publication, only 20 of the evaluation test calls are being released; the remaining 80 test calls are being held in reserve for future LVCSR benchmark tests.
After a successful call was completed, a human audit of each telephone call was conducted to verify that the proper language was spoken, to check the quality of the recording, and to select and describe the region to be transcribed. The description of the transcribed region provides information about channel quality, number of speakers, their gender, and other attributes.
## Acknowledgements
Andrew Yankes reformatted this corpus into accord with current versions of CHAT.
|
simeneide | null | null | null | false | 2 | false | simeneide/recsys_slates_dataset | 2022-09-14T08:51:42.000Z | null | false | acac1e8a2f086619a3f86242e3485b3b6069d496 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/simeneide/recsys_slates_dataset/resolve/main/README.md |
# FINN.no Slate Dataset for Recommender Systems
> Data and helper functions for FINN.no slate dataset containing both viewed items and clicks from the FINN.no second hand marketplace.
Note: The dataset is originally hosted at https://github.com/finn-no/recsys_slates_dataset and this is a copy of the readme until this repo is properly created "huggingface-style".
We release the *FINN.no slate dataset* to improve recommender systems research.
The dataset includes both search and recommendation interactions between users and the platform over a 30 day period.
The dataset has logged both exposures and clicks, *including interactions where the user did not click on any of the items in the slate*.
To our knowledge there exists no such large-scale dataset, and we hope this contribution can help researchers constructing improved models and improve offline evaluation metrics.
For each user u and interaction step t we recorded all items in the visible slate  ) (up to the scroll length ), and the user's click response .
The dataset consists of 37.4 million interactions, |U| ≈ 2.3) million users and |I| ≈ 1.3 million items that belong to one of G = 290 item groups. For a detailed description of the data please see the [paper](https://arxiv.org/abs/2104.15046).
FINN.no is the leading marketplace in the Norwegian classifieds market and provides users with a platform to buy and sell general merchandise, cars, real estate, as well as house rentals and job offerings.
For questions, email simen.eide@finn.no or file an issue.
## Install
`pip install recsys_slates_dataset`
## How to use
To download the generic numpy data files:
```
from recsys_slates_dataset import data_helper
data_helper.download_data_files(data_dir="data")
```
Download and prepare data into ready-to-use PyTorch dataloaders:
``` python
from recsys_slates_dataset import dataset_torch
ind2val, itemattr, dataloaders = dataset_torch.load_dataloaders(data_dir="data")
```
## Organization
The repository is organized as follows:
- The dataset is placed in `data/` and stored using git-lfs. We also provide an automatic download function in the pip package (preferred usage).
- The code open sourced from the article ["Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling"](https://arxiv.org/abs/2104.15046) is found in (`code_eide_et_al21/`). However, we are in the process of making the data more generally available which makes the code incompatible with the current (newer) version of the data. Please use [the v1.0 release of the repository](https://github.com/finn-no/recsys-slates-dataset/tree/v1.0) for a compatible version of the code and dataset.
## Quickstart dataset [](https://colab.research.google.com/github/finn-no/recsys-slates-dataset/blob/main/examples/quickstart-finn-recsys-slate-data.ipynb)
We provide a quickstart Jupyter notebook that runs on Google Colab (quickstart-finn-recsys-slate-data.ipynb) which includes all necessary steps above.
It gives a quick introduction to how to use the dataset.
## Example training scripts
We provide an example training jupyter notebook that implements a matrix factorization model with categorical loss that can be found in `examples/`.
It is also runnable using Google Colab: [](https://colab.research.google.com/github/finn-no/recsys-slates-dataset/blob/main/examples/matrix_factorization.ipynb)
There is ongoing work in progress to build additional examples and use them as benchmarks for the dataset.
### Dataset files
The dataset `data.npz` contains the following fields:
- userId: The unique identifier of the user.
- click: The items the user clicked on in each of the 20 presented slates.
- click_idx: The index the clicked item was on in each of the 20 presented slates.
- slate_lengths: The length of the 20 presented slates.
- slate: All the items in each of the 20 presented slates.
- interaction_type: The recommendation slate can be the result of a search query (1), a recommendation (2) or can be undefined (0).
The dataset `itemattr.npz` contains the categories ranging from 0 to 290. Corresponding with the 290 unique groups that the items belong to. These 290 unique groups are constructed using a combination of categorical information and the geographical location.
The dataset `ind2val.json` contains the mapping between the indices and the values of the categories (e.g. `"287": "JOB, Rogaland"`) and interaction types (e.g. `"1": "search"`).
## Citations
This repository accompanies the paper ["Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling"](https://arxiv.org/abs/2104.15046) by Simen Eide, David S. Leslie and Arnoldo Frigessi.
The article is under review, and the preprint can be obtained [here](https://arxiv.org/abs/2104.15046).
If you use either the code, data or paper, please consider citing the paper.
```
Eide, S., Leslie, D.S. & Frigessi, A. Dynamic slate recommendation with gated recurrent units and Thompson sampling. Data Min Knowl Disc (2022). https://doi.org/10.1007/s10618-022-00849-w
```
---
license: apache-2.0
---
|
mishagrin | null | null | null | false | null | false | mishagrin/shitty_salt | 2022-09-14T08:32:30.000Z | null | false | 887e21ba999085e25e9cb621ad5118d5edc0439a | [] | [
"license:unlicense"
] | https://huggingface.co/datasets/mishagrin/shitty_salt/resolve/main/README.md | ---
license: unlicense
---
|
ryanxingql | null | null | null | false | null | false | ryanxingql/MFQEv2 | 2022-09-14T08:48:17.000Z | null | false | 826870d287708d23f6828c7cd2405b715c4f1d29 | [] | [
"arxiv:1902.09707",
"license:apache-2.0"
] | https://huggingface.co/datasets/ryanxingql/MFQEv2/resolve/main/README.md | ---
license: apache-2.0
---
# MFQEv2 Dataset
For some video enhancement/restoration tasks, lossless reference videos are necessary.
We open-source the dataset used in our [MFQEv2 paper](https://arxiv.org/abs/1902.09707), which includes 108 lossless YUV videos for training and 18 test videos recommended by [ITU-T](https://ieeexplore.ieee.org/document/6317156).
## 1. Content
- 108 lossless YUV videos for training.
- 18 lossless YUV videos for test, recommended by ITU-T.
- An HEVC compression tool box.
43.1 GB in total.
## 2. Download Raw Videos
[[Dropbox]](https://www.dropbox.com/sh/tphdy1lmlpz7zq3/AABR4Qim-P-3xGtouWk6ohi5a?dl=0)
or [[百度网盘 (key: mfqe)]](https://pan.baidu.com/s/1oBZf75bFGRanLmQQLAg4Ew)
## 3. Compress Videos
We compress both training and test videos by [HM](https://hevc.hhi.fraunhofer.de/) 16.5 at low delay P (LDP) mode with QP=37. The video compression toolbox is provided at the dataset folder.
We will get:
```tex
MFQEv2_dataset/
├── train_108/
│ ├── raw/
│ └── HM16.5_LDP/
│ └── QP37/
├── test_18/
│ ├── raw/
│ └── HM16.5_LDP/
│ └── QP37/
├── video_compression/
│ └── ...
└── README.md
```
### Ubuntu
1. `cd video_compression/`
2. Edit `option.yml`.
3. `chmod +x TAppEncoderStatic`
4. `python unzip_n_compress.py`
### Windows
1. Unzip `train_108.zip` and `test_18.zip` manually!
2. `cd video_compression\`
3. Edit `option.yml` (e.g., `system: windows`).
4. `python unzip_n_compress.py`
## 4. Citation
If you find this helpful, please star and cite:
```tex
@article{2019xing,
doi = {10.1109/tpami.2019.2944806},
url = {https://doi.org/10.1109%2Ftpami.2019.2944806},
year = 2021,
month = {mar},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
volume = {43},
number = {3},
pages = {949--963},
author = {Zhenyu Guan and Qunliang Xing and Mai Xu and Ren Yang and Tie Liu and Zulin Wang},
title = {{MFQE} 2.0: A New Approach for Multi-Frame Quality Enhancement on Compressed Video},
journal = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence}
}
```
|
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-emotion-default-2feb36-1456053837 | 2022-09-14T09:16:38.000Z | null | false | d88018ac299bf2075e1860461d0165ed88e97d99 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-emotion-default-2feb36-1456053837/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: Emanuel/twitter-emotion-deberta-v3-base
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
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: Emanuel/twitter-emotion-deberta-v3-base
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-8ddaed-1457553860 | 2022-09-14T13:30:24.000Z | null | false | 3de4889cb01d4c83cff36d11aafd915429ac3488 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-8ddaed-1457553860/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: ARTeLab/it5-summarization-fanpage
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# 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: ARTeLab/it5-summarization-fanpage
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ehahaha](https://huggingface.co/ehahaha) for evaluating this model. |
lambdalabs | null | null | null | false | 16,301 | false | lambdalabs/pokemon-blip-captions | 2022-09-21T10:38:05.000Z | null | false | 8b762e1dac1b31d60e01ee8f08a9d8a232b59e17 | [] | [
"license:cc-by-nc-sa-4.0",
"annotations_creators:machine-generated",
"language:en",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:huggan/few-shot-pokemon",
"task_categories:text-to-image"
] | https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/resolve/main/README.md | ---
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: 'Pokémon BLIP captions'
size_categories:
- n<1K
source_datasets:
- huggan/few-shot-pokemon
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for Pokémon BLIP captions
_Dataset used to train [Pokémon text to image model](https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning)_
BLIP generated captions for Pokémon images from Few Shot Pokémon dataset introduced by _Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis_ (FastGAN). Original images were obtained from [FastGAN-pytorch](https://github.com/odegeasslbc/FastGAN-pytorch) and captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
## Examples
> a drawing of a green pokemon with red eyes
> a green and yellow toy with a red nose
> a red and white ball with an angry look on its face
## Citation
If you use this dataset, please cite it as:
```
@misc{pinkney2022pokemon,
author = {Pinkney, Justin N. M.},
title = {Pokemon BLIP captions},
year={2022},
howpublished= {\url{https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/}}
}
``` |
allenai | null | null | null | false | 1 | false | allenai/cochrane_sparse_max | 2022-11-03T22:41:25.000Z | multi-document-summarization | false | 9752a97b2f1f3a6473a935f623dd78807ca2af1d | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"task_categories:summarization",
"task_... | https://huggingface.co/datasets/allenai/cochrane_sparse_max/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
task_ids:
- summarization-other-query-based-summarization
- summarization-other-query-based-multi-document-summarization
- summarization-other-scientific-documents-summarization
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [Cochrane](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `target` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==25`
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7226 | 0.4023 | 0.1729 | 0.5676 | |
allenai | null | null | null | false | 1 | false | allenai/cochrane_sparse_mean | 2022-11-03T23:29:41.000Z | multi-document-summarization | false | 759b1173ad9a0a2cc23aba2f0c29f03ff3e30fe8 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"task_categories:summarization",
"task_... | https://huggingface.co/datasets/allenai/cochrane_sparse_mean/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
task_ids:
- summarization-other-query-based-summarization
- summarization-other-query-based-multi-document-summarization
- summarization-other-scientific-documents-summarization
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [Cochrane](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `target` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==9`
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7226 | 0.4023 | 0.3095 | 0.4443 | |
allenai | null | null | null | false | 1 | false | allenai/cochrane_sparse_oracle | 2022-11-03T22:41:20.000Z | multi-document-summarization | false | 745f13a0d18d8ba1fab6a16b18515fb5bf9bc8ae | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"task_categories:summarization",
"task_... | https://huggingface.co/datasets/allenai/cochrane_sparse_oracle/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
task_ids:
- summarization-other-query-based-summarization
- summarization-other-query-based-multi-document-summarization
- summarization-other-scientific-documents-summarization
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [Cochrane](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `target` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7226 | 0.4023 | 0.4023 | 0.4023 | |
n1ghtf4l1 | null | null | null | false | null | false | n1ghtf4l1/vigilant-fortnight | 2022-11-01T06:59:48.000Z | null | false | 9d84b3ac8da24fbce401b98a178082e54a1bca8f | [] | [
"license:mit"
] | https://huggingface.co/datasets/n1ghtf4l1/vigilant-fortnight/resolve/main/README.md | ---
license: mit
---
This contains the datasets for the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the [competition website](http://trojandetection.ai/).
# **Trojan Detection**
##### Detect and Analyze Trojan attacks on deep neural networks that are designed to be difficult to detect.
### **Overview**
Neural Trojans are a growing concern for the security of ML systems, but little is known about the fundamental offense-defense balance of Trojan detection. Early work suggests that standard Trojan attacks may be easy to detect, but recently it has been shown that in simple cases one can design practically undetectable Trojans.
This repository contains code for the **Trojan Detection Challenge (TDC) NeurIPS 2022** [competition](https://trojandetection.ai/).
There are 3 main tracks for this competition:
- **Trojan Detection Track**: Given a dataset of Trojaned and clean networks spanning multiple data sources, build a Trojan detector that classifies a test set of networks with held-out labels (Trojan, clean). For more information, see here.
- **Trojan Analysis Track**: Given a dataset of Trojaned networks spanning multiple data sources, predict various properties of Trojaned networks on a test set with held-out labels. This track has two subtracks: (1) target label prediction, (2) trigger synthesis. For more information, see here.
- **Evasive Trojans Track**: Given a dataset of clean networks and a list of attack specifications, train a small set of Trojaned networks meeting the specifications and upload them to the evaluation server. The server will verify that the attack specifications are met, then train and evaluate a baseline Trojan detector using held-out clean networks and the submitted Trojaned networks. The task is to create Trojaned networks that are hard to detect. For more information, see here.
The competition has two rounds: In the primary round, participants will compete on the three main tracks. In the final round, the solution of the first-place team in the Evasive Trojans track will be used to train a new set of hard-to-detect Trojans, and participants will compete to detect these networks. For more information on the final round, see here.
### **Contents**
There are four folders corresponding to different tracks and subtracks: 1) Trojan Detection, 2) Trojan Analysis (Target Label Prediction), 3) Trojan Analysis (Trigger Synthesis), and 4) Evasive Trojans. We provide starter code for submitting baselines in ```example_submission.ipynb``` under each folder. The ```tdc_datasets``` folder is expected to be under the same parent directory as ```tdc-starter-kit```. The datasets are available [here](https://zenodo.org/record/6894041). You can download them from the Zenodo website or by running ```download_datasets.py```.
The ```utils.py``` file contains helper functions for loading new models, generating new attack specifications, and training clean/Trojaned networks. This is primarily used for the Evasive Trojans Track starter kit. It also contains the load_data function for loading data sources (CIFAR-10/100, GTSRB, MNIST), which may be of general use. To load GTSRB images, unzip ```gtsrb_preprocessed.zip``` in the data folder (NOTE: This folder is only for storing data sources. The network datasets are stored in tdc_datasets, which must be downloaded from Zenodo). You may need to adjust the paths in the load_data function depending on your working directory. The ```wrn.py``` file contains the definition of the Wide Residual Network class used for CIFAR-10 and CIFAR-100 models. When loading networks from the competition datasets, ```wrn.py``` must be in your path. See the example submission notebooks for details.
### **Data**
Unlike standard machine learning tasks, the datasets consist of neural networks. That is, rather than making predictions on input images, goal will be identifying hidden functionality in neural networks. Networks are trained on four standard data sources: MNIST, CIFAR-10, CIFAR-100, and GTSRB. Variants of two standard Trojan attacks are used that are modified to be harder to detect. For the Detection Track, the training, validation, and test sets have 1,000 neural networks each. Networks are split evenly across all four data sources. Half of the networks are Trojaned, and there is a 50/50 split between the two attack types.
## How to Use
**Clone this repository, download the competition [datasets](https://huggingface.co/datasets/n1ghtf4l1/vigilant-fortnight/blob/main/tdc_datasets.zip) from my HuggingFace repository and unzip adjacent to the repository**. Ensure that Jupyter version is up-to-date (fairly recent). To avoid errors with model incompatibility, please use PyTorch version 1.11.0. Run one of the example notebooks or start building your own submission.
### **Additional Information**
#### **Model Architectures and Data Sources**
Networks have been trained on four standard data sources: MNIST, CIFAR-10, CIFAR-100, and GTSRB. GTSRB images are resized to 32x32.
For MNIST, convolutional networks have been used. For CIFAR-10 and CIFAR-100, Wide Residual Networks have been used. For GTSRB, Vision Transformers have been used.
#### **Trojan Attacks**
Trojaned networks have been trained with patch and whole-image attacks. These attacks are variants of the foundational BadNets and blended attacks modified to be harder to detect. These modified attacks use a simple change to the standard Trojan training procedure. Instead of training Trojaned networks from scratch, they were fine-tuned from the starting parameters of clean networks and regularize them with various similarity losses such that they are similar to the distribution of clean networks. Additionally, the networks have been trained to have high specificity for the particular trigger pattern associated with the attack. In extensive experiments, baseline detectors have been verified obtain substantially lower performance on these hard-to-detect Trojans.
All patch attacks in datasets use random trigger patterns sampled from an independent Bernoulli 0/1 distribution for each pixel and color channel (for Trojan detection and target label prediction, patches are black-and-white; for trigger synthesis, patches are colored). Each patch attack uses a different location and size for its trigger mask. All blended attacks in our datasets use random trigger trigger patterns sampled from an independent Uniform(0,1) distribution for each pixel and color channel. All attacks are all-to-one with a random target label. For more details, please see the starter kit.
MNTD, Neural Cleanse, and ABS has been used as baseline Trojan detectors for participants to improve upon. These are well-known Trojan detectors from the academic literature, each with a distinct approach to Trojan detection. Also a specificity-based detector has been used as a baseline, since Trojan attacks with low specificity can be highly susceptible to such a detector. The specificity detector applies random triggers to inputs from a given data source, then runs these triggered inputs through the network in question. The negative entropy of the average posterior is used as a detection score. This leverages the fact that Trojan attacks without specificity are activated quite frequently by randomly sampled triggers. |
dwisaji | null | null | null | false | 2 | false | dwisaji/indonesia-telecomunication-sentiment-dataset | 2022-09-16T11:36:02.000Z | null | false | 6dd53ddc97b18d6fc7c29252712ff261543e0fea | [] | [
"license:mit"
] | https://huggingface.co/datasets/dwisaji/indonesia-telecomunication-sentiment-dataset/resolve/main/README.md | ---
license: mit
---
Dataset Contain sentimen for Indonesia Communication Industry. Source from Twitter and manually annotated in prodigy spacy
|
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-HadiPourmousa__TextSummarization-HadiPourmousa__TextSum-31dfb4-1463253931 | 2022-09-14T16:06:24.000Z | null | false | c66d38584e94865e84e2295385fd18b39e721d79 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:HadiPourmousa/TextSummarization"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-HadiPourmousa__TextSummarization-HadiPourmousa__TextSum-31dfb4-1463253931/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- HadiPourmousa/TextSummarization
eval_info:
task: summarization
model: t5-base
metrics: []
dataset_name: HadiPourmousa/TextSummarization
dataset_config: HadiPourmousa--TextSummarization
dataset_split: train
col_mapping:
text: Text
target: Title
---
# 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: t5-base
* Dataset: HadiPourmousa/TextSummarization
* Config: HadiPourmousa--TextSummarization
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@marcmaxmeister](https://huggingface.co/marcmaxmeister) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-HadiPourmousa__TextSummarization-HadiPourmousa__TextSum-31dfb4-1463253932 | 2022-09-14T16:05:51.000Z | null | false | 2a8b1b48cf1266ce9417abd61b51e004491e6e5d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:HadiPourmousa/TextSummarization"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-HadiPourmousa__TextSummarization-HadiPourmousa__TextSum-31dfb4-1463253932/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- HadiPourmousa/TextSummarization
eval_info:
task: summarization
model: shivaniNK8/t5-small-finetuned-cnn-news
metrics: []
dataset_name: HadiPourmousa/TextSummarization
dataset_config: HadiPourmousa--TextSummarization
dataset_split: train
col_mapping:
text: Text
target: Title
---
# 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: shivaniNK8/t5-small-finetuned-cnn-news
* Dataset: HadiPourmousa/TextSummarization
* Config: HadiPourmousa--TextSummarization
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@marcmaxmeister](https://huggingface.co/marcmaxmeister) for evaluating this model. |
collectivat | null | null | null | false | 1 | false | collectivat/salom-ladino-articles | 2022-10-25T11:46:20.000Z | null | false | 46db0397e01c802cd02a14c954cc3e60a4f929a3 | [] | [
"arxiv:2205.15599",
"annotations_creators:found",
"language_creators:found",
"language:lad",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-generation",
"task_ids:language-modeling"
] | https://huggingface.co/datasets/collectivat/salom-ladino-articles/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- lad
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Şalom Ladino articles text corpus
Text corpus compiled from 397 articles from the Judeo-Espanyol section of [Şalom newspaper](https://www.salom.com.tr/haberler/17/judeo-espanyol). Original sentences and articles belong to Şalom.
Size: 176,843 words
[Offical link](https://data.sefarad.com.tr/dataset/salom-ladino-articles-text-corpus)
Paper on [ArXiv](https://arxiv.org/abs/2205.15599)
Citation:
```
Preparing an endangered language for the digital age: The Case of Judeo-Spanish. Alp Öktem, Rodolfo Zevallos, Yasmin Moslem, Güneş Öztürk, Karen Şarhon.
Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC 2022. Marseille, France. 20 June 2022
```
This dataset is created as part of project "Judeo-Spanish: Connecting the two ends of the Mediterranean" carried out by Col·lectivaT and Sephardic Center of Istanbul within the framework of the “Grant Scheme for Common Cultural Heritage: Preservation and Dialogue between Turkey and the EU–II (CCH-II)” implemented by the Ministry of Culture and Tourism of the Republic of Turkey with the financial support of the European Union. The content of this website is the sole responsibility of Col·lectivaT and does not necessarily reflect the views of the European Union. |
collectivat | null | null | null | false | null | false | collectivat/una-fraza-al-diya | 2022-10-25T11:46:11.000Z | null | false | a91c62f46e6e69eb7ab019798e5913c135d061f8 | [] | [
"arxiv:2205.15599",
"annotations_creators:found",
"language_creators:found",
"language:lad",
"language:es",
"language:tr",
"language:en",
"license:cc-by-4.0",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-generation",
"task_cat... | https://huggingface.co/datasets/collectivat/una-fraza-al-diya/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- lad
- es
- tr
- en
license: cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- translation
task_ids:
- language-modeling
---
# Una fraza al diya
Ladino language learning sentences prepared by Karen Sarhon of Sephardic Center of Istanbul. Each sentence has translations in Turkish, English, Spanish. Includes audio and image. 307 sentences in total.
Source: https://sefarad.com.tr/judeo-espanyolladino/frazadeldia/
Images and audio: http://collectivat.cat/share/judeoespanyol_audio_image.zip
[Offical link on Ladino Data Hub](https://data.sefarad.com.tr/dataset/una-fraza-al-diya-skad)
Paper on [ArXiv](https://arxiv.org/abs/2205.15599)
Citation:
```
Preparing an endangered language for the digital age: The Case of Judeo-Spanish. Alp Öktem, Rodolfo Zevallos, Yasmin Moslem, Güneş Öztürk, Karen Şarhon.
Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC 2022. Marseille, France. 20 June 2022
```
This dataset is created as part of project "Judeo-Spanish: Connecting the two ends of the Mediterranean" carried out by Col·lectivaT and Sephardic Center of Istanbul within the framework of the “Grant Scheme for Common Cultural Heritage: Preservation and Dialogue between Turkey and the EU–II (CCH-II)” implemented by the Ministry of Culture and Tourism of the Republic of Turkey with the financial support of the European Union. The content of this website is the sole responsibility of Col·lectivaT and does not necessarily reflect the views of the European Union.
|
neulab | null | @inproceedings{yin2018learning,
title={Learning to mine aligned code and natural language pairs from stack overflow},
author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
pages={476--486},
year={2018},
organization={IEEE}
} | CoNaLa is a dataset of code and natural language pairs crawled from Stack Overflow, for more details please refer to this paper: https://arxiv.org/pdf/1805.08949.pdf or the dataset page https://conala-corpus.github.io/. | false | 196 | false | neulab/conala | 2022-10-20T20:25:00.000Z | null | false | fbc749f1c537e5c3834e93b15784302e331debe2 | [] | [
"arxiv:1805.08949",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:code",
"license:mit",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:code-generation"
] | https://huggingface.co/datasets/neulab/conala/resolve/main/README.md | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: CoNaLa
tags:
- code-generation
---
## Dataset Description
- **Repository:** https://conala-corpus.github.io/
- **Paper:** [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://arxiv.org/pdf/1805.08949.pdf)
### Dataset Summary
[CoNaLa](https://conala-corpus.github.io/) is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.
### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations.
### Languages
English - Python code.
## Dataset Structure
```python
dataset_curated = load_dataset("neulab/conala")
DatasetDict({
train: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 2379
})
test: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 500
})
})
dataset_mined = load_dataset("neulab/conala", "mined")
DatasetDict({
train: Dataset({
features: ['question_id', 'parent_answer_post_id', 'prob', 'snippet', 'intent', 'id'],
num_rows: 593891
})
})
```
### Data Instances
#### CoNaLa - curated
This is the curated dataset by annotators
```
{
'question_id': 41067960,
'intent': 'How to convert a list of multiple integers into a single integer?',
'rewritten_intent': "Concatenate elements of a list 'x' of multiple integers to a single integer",
'snippet': 'sum(d * 10 ** i for i, d in enumerate(x[::-1]))'
}
```
#### CoNaLa - mined
This is the automatically mined dataset before curation
```
{
'question_id': 34705205,
'parent_answer_post_id': 34705233,
'prob': 0.8690001442846342,
'snippet': 'sorted(l, key=lambda x: (-int(x[1]), x[0]))',
'intent': 'Sort a nested list by two elements',
'id': '34705205_34705233_0'
}
```
### Data Fields
Curated:
|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|rewritten_intent|string|Crowdsourced revised intents that try to better reflect the full meaning of the code|
|snippet|string| Code snippet that implements the intent|
Mined:
|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|parent_answer_post_id|int64|Id of the answer post from which the candidate snippet is extracted|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|snippet|string| Code snippet that implements the intent|
|id|string|Unique id for this intent/snippet pair|
|prob|float64|Probability given by the mining model|
### Data Splits
There are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.
## Dataset Creation
The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original [paper](https://arxiv.org/pdf/1805.08949.pdf)
### Citation Information
```
@inproceedings{yin2018learning,
title={Learning to mine aligned code and natural language pairs from stack overflow},
author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
pages={476--486},
year={2018},
organization={IEEE}
}
``` |
sparanoid | null | null | null | false | 2 | false | sparanoid/images | 2022-09-14T19:31:11.000Z | null | false | 9c2c99e06171661d6c6659334ee1668c4853e23b | [] | [
"license:other"
] | https://huggingface.co/datasets/sparanoid/images/resolve/main/README.md | ---
license: other
---
|
mrmoor | null | null | null | false | 82 | false | mrmoor/cyber-threat-intelligence | 2022-10-23T09:12:59.000Z | null | false | 0680dc6441ef1c20661cffcdfa88ea40dcd9489c | [] | [
"license:unknown"
] | https://huggingface.co/datasets/mrmoor/cyber-threat-intelligence/resolve/main/README.md | ---
license: unknown
---
|
allenai | null | null | null | false | 1 | false | allenai/wcep_sparse_max | 2022-11-03T21:22:12.000Z | wcep | false | 92077d00f00f98327af41fd3ac976b88509e3cd9 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/allenai/wcep_sparse_max/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: WCEP-10
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: wcep
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==10`
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8836 | 0.6658 | 0.6296 | 0.6746 | |
allenai | null | null | null | false | 1 | false | allenai/wcep_sparse_mean | 2022-11-03T21:24:38.000Z | wcep | false | 41e2f75667e9333a317667abbf130b7640caccf2 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/allenai/wcep_sparse_mean/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: WCEP-10
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: wcep
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==9`
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8836 | 0.6658 | 0.6601 | 0.6388 | |
allenai | null | null | null | false | 1 | false | allenai/wcep_sparse_oracle | 2022-11-03T22:29:35.000Z | wcep | false | 3649623a165c2d4027225874b5c3f319e9942aca | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/allenai/wcep_sparse_oracle/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: WCEP-10
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: wcep
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8836 | 0.6658 | 0.6658 | 0.6658 | |
daspartho | null | null | null | false | 3 | false | daspartho/subreddit-posts | 2022-09-28T16:22:56.000Z | null | false | 3307d22552c23c4ad3ae333fcf53f12c4c78c4b2 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/daspartho/subreddit-posts/resolve/main/README.md | ---
license: apache-2.0
---
Dataset of titles of the top 1000 posts from the top 250 subreddits scraped using [PRAW](https://praw.readthedocs.io/en/stable/index.html).
For steps to create the dataset check out the [dataset](https://github.com/daspartho/predict-subreddit/blob/main/dataset.ipynb) notebook in the github repo or open in [Colab](https://colab.research.google.com/github/daspartho/predict-subreddit/blob/main/dataset.ipynb). |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-samsum-samsum-5abc44-1464853958 | 2022-09-14T21:32:43.000Z | null | false | 554b062213e9b94c22c98dea9a72b1c451db1785 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-samsum-samsum-5abc44-1464853958/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: SamuelAllen123/t5-efficient-large-nl36_sum
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
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: SamuelAllen123/t5-efficient-large-nl36_sum
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@samuelfipps123](https://huggingface.co/samuelfipps123) for evaluating this model. |
MikroKat | null | null | null | false | null | false | MikroKat/Tech | 2022-09-14T23:01:46.000Z | null | false | 3ac89d2b8d4eccdabc8afaaec715996921212d31 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/MikroKat/Tech/resolve/main/README.md | ---
license: afl-3.0
---
|
Euclid | null | null | null | false | 1 | false | Euclid/testing | 2022-09-14T23:24:04.000Z | null | false | ca43c11697a25fb353a7781171bca42f8367b785 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/Euclid/testing/resolve/main/README.md | ---
license: afl-3.0
---
|
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-fb0535-1465153964 | 2022-09-16T06:49:48.000Z | null | false | b707596946d87b12e0b9c3fdfb92280c73505003 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-fb0535-1465153964/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# 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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-big_patent-y-3c6f0a-1465253965 | 2022-09-16T09:16:49.000Z | null | false | b03bcdf81535a6550ece72d65a15f8a9132a5177 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:big_patent"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-big_patent-y-3c6f0a-1465253965/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- big_patent
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
metrics: []
dataset_name: big_patent
dataset_config: y
dataset_split: test
col_mapping:
text: description
target: abstract
---
# 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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
* Dataset: big_patent
* Config: y
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
Euclid | null | null | null | false | 1 | false | Euclid/chammuu | 2022-09-14T23:26:47.000Z | null | false | 7cc95ea515fc325023e94c1a495cd9224efeefd0 | [] | [
"license:other"
] | https://huggingface.co/datasets/Euclid/chammuu/resolve/main/README.md | ---
license: other
---
|
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-billsum-default-a34c3f-1465353966 | 2022-09-15T13:21:49.000Z | null | false | 574d5679836e0858757a0d3a15f6e88d52a8b12d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:billsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-billsum-default-a34c3f-1465353966/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- billsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
metrics: []
dataset_name: billsum
dataset_config: default
dataset_split: test
col_mapping:
text: text
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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
* Dataset: billsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-samsum-samsum-89ef9c-1465453967 | 2022-09-15T00:39:49.000Z | null | false | e802fcbc2e19103618b1e7afd9c0835d85642bc9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-samsum-samsum-89ef9c-1465453967/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-launch__gov_report-plain_text-c8c9c8-1465553968 | 2022-09-15T05:53:11.000Z | null | false | 3739d09f05f0116bde477fbc5e9b4c8346db847d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:launch/gov_report"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-launch__gov_report-plain_text-c8c9c8-1465553968/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- launch/gov_report
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
metrics: []
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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-Blaise-g__PubMed_summ-Blaise-g__PubMed_summ-0234b8-1465653969 | 2022-09-16T06:40:02.000Z | null | false | f6b8ab257df3565fbb66b5aa490535371936aa04 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Blaise-g/PubMed_summ"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-Blaise-g__PubMed_summ-Blaise-g__PubMed_summ-0234b8-1465653969/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/PubMed_summ
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
metrics: []
dataset_name: Blaise-g/PubMed_summ
dataset_config: Blaise-g--PubMed_summ
dataset_split: test
col_mapping:
text: article
target: abstract
---
# 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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
* Dataset: Blaise-g/PubMed_summ
* Config: Blaise-g--PubMed_summ
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-ccdv__arxiv-summarization-document-47d12e-1465753970 | 2022-09-16T05:46:07.000Z | null | false | ea5404aecf4e9eecb11b8a4e655b959ae298648c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:ccdv/arxiv-summarization"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-ccdv__arxiv-summarization-document-47d12e-1465753970/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ccdv/arxiv-summarization
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
metrics: []
dataset_name: ccdv/arxiv-summarization
dataset_config: document
dataset_split: test
col_mapping:
text: article
target: abstract
---
# 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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
* Dataset: ccdv/arxiv-summarization
* Config: document
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-228ea1-1466053986 | 2022-09-15T11:16:52.000Z | null | false | df25b0c51d06c4aef5f462ac1bcd0d0e37eeac82 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:kmfoda/booksum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-228ea1-1466053986/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- kmfoda/booksum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
metrics: []
dataset_name: kmfoda/booksum
dataset_config: kmfoda--booksum
dataset_split: test
col_mapping:
text: chapter
target: summary_text
---
# 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: pszemraj/long-t5-tglobal-base-16384-booksum-V12
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-1006ec-1466153987 | 2022-09-16T07:13:52.000Z | null | false | f8322d1772f53552a45d61d20fb69ecc61562e33 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:kmfoda/booksum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-1006ec-1466153987/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- kmfoda/booksum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
metrics: []
dataset_name: kmfoda/booksum
dataset_config: kmfoda--booksum
dataset_split: test
col_mapping:
text: chapter
target: summary_text
---
# 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-1006ec-1466153988 | 2022-09-16T06:16:26.000Z | null | false | 5049442efa4cb3d9d27987be83961addba9d6ea4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:kmfoda/booksum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-1006ec-1466153988/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- kmfoda/booksum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11
metrics: []
dataset_name: kmfoda/booksum
dataset_config: kmfoda--booksum
dataset_split: test
col_mapping:
text: chapter
target: summary_text
---
# 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
AnachronicRodent | null | null | null | false | 1 | false | AnachronicRodent/MikwaTest | 2022-09-15T04:19:56.000Z | null | false | d383ce5ed1d6a52e831af930c97d4155902dff5e | [] | [
"license:cc-by-nc-4.0"
] | https://huggingface.co/datasets/AnachronicRodent/MikwaTest/resolve/main/README.md | ---
license: cc-by-nc-4.0
---
|
bitext | null | null | null | false | 10 | false | bitext/customer-support-intent-dataset | 2022-09-23T06:42:24.000Z | null | false | 24529dde1908239e91697cdd6952bb03f9cd9004 | [] | [] | https://huggingface.co/datasets/bitext/customer-support-intent-dataset/resolve/main/README.md | Bitext - Customer Service Tagged Training Dataset for Intent Detection
======================================================================
Overview
--------
The dataset can be used to train intent recognition models on Natural Language Understanding (NLU) platforms: LUIS, Dialogflow, Lex, RASA and more.
The dataset covers the "Customer Service" domain and includes:
- 11 categories or intent groups
- 27 intents assigned to one of the 11 categories
- 8,175 utterances assigned to the 27 intents
Additionally, each utterance is enriched with tags that indicate the type of language variation that the utterance expresses. Examples include:
- The tag “COLLOQUIAL” indicates that the utterance contains informal expressions: “can u close my account”
- The tag “INTERROGATIVE” indicates that the utterance is a question: “how do I open an account”
- The tag “OFFENSIVE” indicates that the utterance contains offensive expressions: “open my f****** account”
There are a total of 11 tags. See below for a full list of tags, categories and intents.
The purpose of these tags is to customize the dataset so the trained bot can easily adapt to different user language profiles. A bot that sells sneakers and targets a younger population should be proficient in colloquial language; while a classical retail banking bot should be able to handle more formal or polite language.
These intents have been selected from Bitext's collection of 20 domain-specific datasets (banking, retail, utilities...), covering the intents that are common across all 20 domains. For a full list of domains see https://www.bitext.com/chatbot-verticals/.
Utterances and Linguistic Tags
------------------------------------
The dataset contains 8,175 training utterances, with between 290 and 324 utterances per intent.
The dataset has been split into training (80%), validation (10%) and testing (10%) sets, preserving the distribution of intents and linguistic phenomena.
The dataset also reflects commonly occurring linguistic phenomena of real-life chatbots, such as: spelling mistakes, run-on words, punctuation errors…
Each entry in the dataset contains the following four fields:
- utterance: a user utterance from the Customer Service domain
- intent: the intent corresponding to the user utterance
- category: the high-level semantic category for the intent
- tags: different tags that reflect the types of language variations expressed in the utterance
The dataset contains tags that reflect different language phenomena like colloquial or offensive language. So if an utterance for intent “cancel_order” contains the “COLLOQUIAL” tag, the utterance will express an informal language variation like: “can u cancel my order”
Each utterance is enriched with one or more of these tags:
- Register tags: colloquial language, polite language…
- Q - Colloquial variation
- P - Politeness variation
- Content tags: offensive language, keyword language…
- W - Offensive language
- K - Keyword language
- Linguistic tags: syntactic and morphological tags (interrogative sentence, coordinated sentence…)
- B - Basic syntactic structure
- C - Coordinated syntactic structure
- I - Interrogative structure
- M - Morphological variation (plurals, tenses…)
- L - Lexical variation (synonyms)
- E - Expanded abbreviations (I'm -> I am, I'd -> I would…)
- Real-life errors: spelling errors, punctuation errors…
- Z - Noise phenomena like spelling or punctuation errors
These tags indicate the type of language variation that the utterance expresses. When associated to each utterance, they allow Conversational Designers to customize training datasets to different user profiles with different uses of language. Through these tags, many different datasets can be created to make the resulting assistant more accurate and robust. A bot that sells sneakers should be mainly targeted to younger population that use a more colloquial language; while a classical retail banking bot should be able to handle more formal or polite language.
Categories and Intents
----------------------
The categories and intents covered by the dataset are:
- ACCOUNT: create_account, delete_account, edit_account, recover_password, registration_problems, switch_account
- CANCELLATION_FEE: check_cancellation_fee
- CONTACT: contact_customer_service, contact_human_agent
- DELIVERY: delivery_options, delivery_period
- FEEDBACK: complaint, review
- INVOICE: check_invoice, get_invoice
- NEWSLETTER: newsletter_subscription,
- ORDER: cancel_order, change_order, place_order, track_order
- PAYMENT: check_payment_methods, payment_issue
- REFUND: check_refund_policy, get_refund, track_refund
- SHIPPING_ADDRESS: change_shipping_address, set_up_shipping_address
(c) Bitext Innovations, 2022
|
nanom | null | @dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
} | null | false | 272 | false | nanom/splittedspanish3bwc | 2022-09-15T14:22:47.000Z | null | false | d2c893e054245cf00d42509eb5457eb409f40a4b | [] | [
"language:es",
"multilinguality:monolingual",
"license:mit"
] | https://huggingface.co/datasets/nanom/splittedspanish3bwc/resolve/main/README.md | ---
language:
- 'es'
multilinguality:
- monolingual
pretty_name: "Unannotated Spanish 3 Billion Words Corpora"
license:
- mit
---
# Dataset Card for Unannotated Spanish 3 Billion Words Corpora
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Source Data](#source-data)
- [Data Subset](#data-subset)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/josecannete/spanish-corpora
- **Paper:** https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf
### Dataset Summary
* Number of lines: 300904000 (300M)
* Number of tokens: 2996016962 (3B)
* Number of chars: 18431160978 (18.4B)
### Languages
* Spanish
### Source Data
* Available to download here: [Zenodo](https://doi.org/10.5281/zenodo.3247731)
### Data Subset
* Spanish Wikis: Wich include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (https://github.com/josecannete/wikiextractorforBERT) using the wikis dump of 20/04/2019.
* ParaCrawl: Spanish portion of ParaCrawl (http://opus.nlpl.eu/ParaCrawl.php)
* EUBookshop: Spanish portion of EUBookshop (http://opus.nlpl.eu/EUbookshop.php)
* MultiUN: Spanish portion of MultiUN (http://opus.nlpl.eu/MultiUN.php)
* OpenSubtitles: Spanish portion of OpenSubtitles2018 (http://opus.nlpl.eu/OpenSubtitles-v2018.php)
* DGC: Spanish portion of DGT (http://opus.nlpl.eu/DGT.php)
* DOGC: Spanish portion of DOGC (http://opus.nlpl.eu/DOGC.php)
* ECB: Spanish portion of ECB (http://opus.nlpl.eu/ECB.php)
* EMEA: Spanish portion of EMEA (http://opus.nlpl.eu/EMEA.php)
* Europarl: Spanish portion of Europarl (http://opus.nlpl.eu/Europarl.php)
* GlobalVoices: Spanish portion of GlobalVoices (http://opus.nlpl.eu/GlobalVoices.php)
* JRC: Spanish portion of JRC (http://opus.nlpl.eu/JRC-Acquis.php)
* News-Commentary11: Spanish portion of NCv11 (http://opus.nlpl.eu/News-Commentary-v11.php)
* TED: Spanish portion of TED (http://opus.nlpl.eu/TED2013.php)
* UN: Spanish portion of UN (http://opus.nlpl.eu/UN.php)
## Additional Information
### Licensing Information
* [MIT Licence](https://github.com/josecannete/spanish-corpora/blob/master/LICENSE)
### Citation Information
```
@dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
}
@inproceedings{CaneteCFP2020,
title={Spanish Pre-Trained BERT Model and Evaluation Data},
author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge},
booktitle={PML4DC at ICLR 2020},
year={2020}
}
``` |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626245 | 2022-09-15T05:55:06.000Z | null | false | ceea7758a71df239a2aec65d28e54c5207f3e5b2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626245/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Adrian/distilbert-base-uncased-finetuned-squad-colab
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Adrian/distilbert-base-uncased-finetuned-squad-colab
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626243 | 2022-09-15T05:55:06.000Z | null | false | cc9a1b600ae3a78649cb2aed244118c15eccadc4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626243/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: 21iridescent/distilbert-base-uncased-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: 21iridescent/distilbert-base-uncased-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626246 | 2022-09-15T05:57:19.000Z | null | false | 15a694a839c2cac55ecb0a6dc6a7ff1dfc395b2c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626246/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Akari/albert-base-v2-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Akari/albert-base-v2-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626244 | 2022-09-15T05:55:14.000Z | null | false | 3ff4b745deb79d6834359d9e3d9d38fbecad9a80 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626244/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: 21iridescent/distilroberta-base-finetuned-squad2-lwt
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: 21iridescent/distilroberta-base-finetuned-squad2-lwt
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626247 | 2022-09-15T06:01:39.000Z | null | false | 57b74ba8affbdcd36661fcd37b7b315f83c3cb31 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626247/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Akihiro2/bert-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Akihiro2/bert-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626248 | 2022-09-15T06:02:49.000Z | null | false | 307626be4df7c25e14c9e122770bea7b5c4b0a6d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626248/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: AyushPJ/test-squad-trained-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: AyushPJ/test-squad-trained-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-07bda3-16636249 | 2022-09-15T06:03:24.000Z | null | false | c036789ee389f8b75efc172316b8153ead77708e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-07bda3-16636249/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: haritzpuerto/MiniLM-L12-H384-uncased-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: haritzpuerto/MiniLM-L12-H384-uncased-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@timbmg](https://huggingface.co/timbmg) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-972433-16666252 | 2022-09-15T07:07:27.000Z | null | false | 14c2a7d0daa831f77cf485eda29f3b92bf5a9cb9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-972433-16666252/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: mrm8488/longformer-base-4096-finetuned-squadv2
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mrm8488/longformer-base-4096-finetuned-squadv2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Liam-Scott-Russell](https://huggingface.co/Liam-Scott-Russell) for evaluating this model. |
OddBunny | null | null | null | false | 2 | false | OddBunny/fox_femboy | 2022-09-18T17:43:18.000Z | null | false | c487313ad85c48d196cd3aa4373ebddb42447e23 | [] | [
"license:cc-by-nc-nd-4.0"
] | https://huggingface.co/datasets/OddBunny/fox_femboy/resolve/main/README.md | ---
license: cc-by-nc-nd-4.0
---
|
taspecustu | null | null | null | false | 2 | false | taspecustu/Nanachi | 2022-09-15T12:32:36.000Z | null | false | 5e4f6b0f9b29eeb9034c01d76ccaf6e71f3db775 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/taspecustu/Nanachi/resolve/main/README.md | ---
license: cc-by-4.0
---
|
ImageIN | null | null | null | false | 4 | false | ImageIN/IA_unlabelled | 2022-10-21T14:38:12.000Z | null | false | dd7d748ed3c8e00fd078e625a01c2d9addff358b | [] | [] | https://huggingface.co/datasets/ImageIN/IA_unlabelled/resolve/main/README.md | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: 'Internet Archive historic book pages unlabelled.'
size_categories: []
source_datasets: []
tags: []
task_categories: []
task_ids: []
---
# Data card for Internet Archive historic book pages unlabelled.
- `10,844,387` unlabelled pages from historical books from the internet archive.
- Intended to be used for:
- pre-training computer vision models in an unsupervised manner
- using weak supervision to generate labels |
Kipol | null | null | null | false | 2 | false | Kipol/vs_art | 2022-09-15T15:18:08.000Z | null | false | bc2dd80f3fe48061b9648e867ef6f41a71ed5660 | [] | [
"license:cc"
] | https://huggingface.co/datasets/Kipol/vs_art/resolve/main/README.md | ---
license: cc
---
|
spiccolo | null | null | null | false | 2 | false | spiccolo/gene_expression_omnibus_nlp | 2022-10-13T16:34:55.000Z | null | false | e0aa6f54740139a2bde073beac5f93403ed2e990 | [] | [] | https://huggingface.co/datasets/spiccolo/gene_expression_omnibus_nlp/resolve/main/README.md | annotations_creators:
- no-annotation
languages:
-English
All data pulled from Gene Expression Omnibus website. tab separated file with GSE number followed by title and abstract text. |
hemangjoshi37a | null | null | null | false | 2 | false | hemangjoshi37a/autotrain-data-ratnakar_1000_sample_curated | 2022-10-01T10:38:09.000Z | null | false | b6c2a8357526949b79bcf8df0f2a80505ca63c52 | [] | [
"language:en"
] | https://huggingface.co/datasets/hemangjoshi37a/autotrain-data-ratnakar_1000_sample_curated/resolve/main/README.md | ---
language:
- en
---
# AutoTrain Dataset for project: ratnakar_1000_sample_curated
## Dataset Description
This dataset has been automatically processed by AutoTrain for project ratnakar_1000_sample_curated.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"tokens": [
"INTRADAY",
"NAHARINDUS",
" ABOVE ",
"128",
" - 129 SL ",
"126",
" TARGET ",
"140",
" "
],
"tags": [
8,
10,
0,
3,
0,
9,
0,
5,
0
]
},
{
"tokens": [
"INTRADAY",
"ASTRON",
" ABV ",
"39",
" SL ",
"37.50",
" TARGET ",
"45",
" "
],
"tags": [
8,
10,
0,
3,
0,
9,
0,
5,
0
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"tags": "Sequence(feature=ClassLabel(num_classes=12, names=['NANA', 'btst', 'delivery', 'enter', 'entry_momentum', 'exit', 'exit2', 'exit3', 'intraday', 'sl', 'symbol', 'touched'], id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 726 |
| valid | 259 |
# GitHub Link to this project : [Telegram Trade Msg Backtest ML](https://github.com/hemangjoshi37a/TelegramTradeMsgBacktestML)
# Need custom model for your application? : Place a order on hjLabs.in : [Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning](https://hjlabs.in/product/custom-token-classification-or-named-entity-recognition-ner-model-as-in-natural-language-processing-nlp-machine-learning/)
## What this repository contains? :
1. Label data using LabelStudio NER(Named Entity Recognition or Token Classification) tool.
 convert to 
2. Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script
3. Train NER model on Hugginface-autoTrain.
4. Use Hugginface-autoTrain model to predict labels on new data in LabelStudio using LabelStudio-ML-Backend.
5. Define python function to predict labels using Hugginface-autoTrain model.
6. Only label new data from newly predicted-labels-dataset that has falsified labels.
7. Backtest Truely labelled dataset against real historical data of the stock using zerodha kiteconnect and jugaad_trader.
8. Evaluate total gained percentage since inception summation-wise and compounded and plot.
9. Listen to telegram channel for new LIVE messages using telegram API for algotrading.
10. Serve the app as flask web API for web request and respond to it as labelled tokens.
11. Outperforming or underperforming results of the telegram channel tips against exchange index by percentage.
Place a custom order on hjLabs.in : [https://hjLabs.in](https://hjlabs.in/?product=custom-algotrading-software-for-zerodha-and-angel-w-source-code)
----------------------------------------------------------------------
### Contact us
Mobile : [+917016525813](tel:+917016525813)
Whatsapp & Telegram : [+919409077371](tel:+919409077371)
Email : [hemangjoshi37a@gmail.com](mailto:hemangjoshi37a@gmail.com)
Place a custom order on hjLabs.in : [https://hjLabs.in](https://hjlabs.in/)
Please contribute your suggestions and corections to support our efforts.
Thank you.
Buy us a coffee for $5 on PayPal ?
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=5JXC8VRCSUZWJ)
----------------------------------------------------------------------
### Checkout Our Other Repositories
- [pyPortMan](https://github.com/hemangjoshi37a/pyPortMan)
- [transformers_stock_prediction](https://github.com/hemangjoshi37a/transformers_stock_prediction)
- [TrendMaster](https://github.com/hemangjoshi37a/TrendMaster)
- [hjAlgos_notebooks](https://github.com/hemangjoshi37a/hjAlgos_notebooks)
- [AutoCut](https://github.com/hemangjoshi37a/AutoCut)
- [My_Projects](https://github.com/hemangjoshi37a/My_Projects)
- [Cool Arduino and ESP8266 or NodeMCU Projects](https://github.com/hemangjoshi37a/my_Arduino)
- [Telegram Trade Msg Backtest ML](https://github.com/hemangjoshi37a/TelegramTradeMsgBacktestML)
### Checkout Our Other Products
- [WiFi IoT LED Matrix Display](https://hjlabs.in/product/wifi-iot-led-display)
- [SWiBoard WiFi Switch Board IoT Device](https://hjlabs.in/product/swiboard-wifi-switch-board-iot-device)
- [Electric Bicycle](https://hjlabs.in/product/electric-bicycle)
- [Product 3D Design Service with Solidworks](https://hjlabs.in/product/product-3d-design-with-solidworks/)
- [AutoCut : Automatic Wire Cutter Machine](https://hjlabs.in/product/automatic-wire-cutter-machine/)
- [Custom AlgoTrading Software Coding Services](https://hjlabs.in/product/custom-algotrading-software-for-zerodha-and-angel-w-source-code//)
- [SWiBoard :Tasmota MQTT Control App](https://play.google.com/store/apps/details?id=in.hjlabs.swiboard)
- [Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning](https://hjlabs.in/product/custom-token-classification-or-named-entity-recognition-ner-model-as-in-natural-language-processing-nlp-machine-learning/)
## Some Cool Arduino and ESP8266 (or NodeMCU) IoT projects:
- [IoT_LED_over_ESP8266_NodeMCU : Turn LED on and off using web server hosted on a nodemcu or esp8266](https://github.com/hemangjoshi37a/my_Arduino/tree/master/IoT_LED_over_ESP8266_NodeMCU)
- [ESP8266_NodeMCU_BasicOTA : Simple OTA (Over The Air) upload code from Arduino IDE using WiFi to NodeMCU or ESP8266](https://github.com/hemangjoshi37a/my_Arduino/tree/master/ESP8266_NodeMCU_BasicOTA)
- [IoT_CSV_SD : Read analog value of Voltage and Current and write it to SD Card in CSV format for Arduino, ESP8266, NodeMCU etc](https://github.com/hemangjoshi37a/my_Arduino/tree/master/IoT_CSV_SD)
- [Honeywell_I2C_Datalogger : Log data in A SD Card from a Honeywell I2C HIH8000 or HIH6000 series sensor having external I2C RTC clock](https://github.com/hemangjoshi37a/my_Arduino/tree/master/Honeywell_I2C_Datalogger)
- [IoT_Load_Cell_using_ESP8266_NodeMC : Read ADC value from High Precision 12bit ADS1015 ADC Sensor and Display on SSD1306 SPI Display as progress bar for Arduino or ESP8266 or NodeMCU](https://github.com/hemangjoshi37a/my_Arduino/tree/master/IoT_Load_Cell_using_ESP8266_NodeMC)
- [IoT_SSD1306_ESP8266_NodeMCU : Read from High Precision 12bit ADC seonsor ADS1015 and display to SSD1306 SPI as progress bar in ESP8266 or NodeMCU or Arduino](https://github.com/hemangjoshi37a/my_Arduino/tree/master/IoT_SSD1306_ESP8266_NodeMCU)
## Checkout Our Awesome 3D GrabCAD Models:
- [AutoCut : Automatic Wire Cutter Machine](https://grabcad.com/library/automatic-wire-cutter-machine-1)
- [ESP Matrix Display 5mm Acrylic Box](https://grabcad.com/library/esp-matrix-display-5mm-acrylic-box-1)
- [Arcylic Bending Machine w/ Hot Air Gun](https://grabcad.com/library/arcylic-bending-machine-w-hot-air-gun-1)
- [Automatic Wire Cutter/Stripper](https://grabcad.com/library/automatic-wire-cutter-stripper-1)
## Our HuggingFace Models :
- [hemangjoshi37a/autotrain-ratnakar_1000_sample_curated-1474454086 : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.](https://huggingface.co/hemangjoshi37a/autotrain-ratnakar_1000_sample_curated-1474454086)
## Our HuggingFace Datasets :
- [hemangjoshi37a/autotrain-data-ratnakar_1000_sample_curated : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.](https://huggingface.co/datasets/hemangjoshi37a/autotrain-data-ratnakar_1000_sample_curated)
## We sell Gigs on Fiverr :
- [code android and ios app for you using flutter firebase software stack](https://business.fiverr.com/share/3v14pr)
- [code custom algotrading software for zerodha or angel broking](https://business.fiverr.com/share/kzkvEy)
|
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-autoevaluate__zero-shot-classification-sample-autoevalu-acab52-16766274 | 2022-09-15T19:13:14.000Z | null | false | f5295abf41f24f8fc5b9790311a2484400dcdf00 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:autoevaluate/zero-shot-classification-sample"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-autoevaluate__zero-shot-classification-sample-autoevalu-acab52-16766274/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- autoevaluate/zero-shot-classification-sample
eval_info:
task: text_zero_shot_classification
model: autoevaluate/zero-shot-classification
metrics: []
dataset_name: autoevaluate/zero-shot-classification-sample
dataset_config: autoevaluate--zero-shot-classification-sample
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: autoevaluate/zero-shot-classification
* Dataset: autoevaluate/zero-shot-classification-sample
* Config: autoevaluate--zero-shot-classification-sample
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-Tristan__zero_shot_classification_test-Tristan__zero_sh-997db8-16786276 | 2022-09-15T19:26:29.000Z | null | false | be8e467ab348721baeae3c5e8761e120f1b9e341 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Tristan/zero_shot_classification_test"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-Tristan__zero_shot_classification_test-Tristan__zero_sh-997db8-16786276/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Tristan/zero_shot_classification_test
eval_info:
task: text_zero_shot_classification
model: autoevaluate/zero-shot-classification
metrics: []
dataset_name: Tristan/zero_shot_classification_test
dataset_config: Tristan--zero_shot_classification_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: autoevaluate/zero-shot-classification
* Dataset: Tristan/zero_shot_classification_test
* Config: Tristan--zero_shot_classification_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Tristan](https://huggingface.co/Tristan) for evaluating this model. |
polinaeterna | null | null | null | false | 2 | false | polinaeterna/earn | 2022-09-15T20:48:46.000Z | null | false | 5993d6f8de645d09e4e076540e6d25f0ee2b747a | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/polinaeterna/earn/resolve/main/README.md | ---
license: cc-by-sa-4.0
---
|
darcksky | null | null | null | false | 2 | false | darcksky/Ringsofsaturnlugalkien | 2022-09-16T03:01:05.000Z | null | false | 64df8d986e65b342699e9dbed622775ae1ce4ba1 | [] | [
"license:artistic-2.0"
] | https://huggingface.co/datasets/darcksky/Ringsofsaturnlugalkien/resolve/main/README.md | ---
license: artistic-2.0
---
|
g0d | null | null | null | false | 2 | false | g0d/BroadcastingCommission_Patois_Dataset | 2022-09-16T00:16:22.000Z | null | false | e36da016ad8b2fec475e4af1af4ce5e26766b1cd | [] | [
"license:other"
] | https://huggingface.co/datasets/g0d/BroadcastingCommission_Patois_Dataset/resolve/main/README.md | ---
license: other
---
|
Bingsu | null | null | null | false | 1 | false | Bingsu/openwebtext_20p | 2022-09-16T02:36:38.000Z | openwebtext | false | c2a2bfe23d23992408295e0dcaa40e1d06fbacc9 | [] | [
"annotations_creators:no-annotation",
"language:en",
"language_creators:found",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|openwebtext",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"tas... | https://huggingface.co/datasets/Bingsu/openwebtext_20p/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
paperswithcode_id: openwebtext
pretty_name: openwebtext_20p
size_categories:
- 1M<n<10M
source_datasets:
- extended|openwebtext
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# openwebtext_20p
## Dataset Description
- **Origin:** [openwebtext](https://huggingface.co/datasets/openwebtext)
- **Download Size** 4.60 GiB
- **Generated Size** 7.48 GiB
- **Total Size** 12.08 GiB
first 20% of [openwebtext](https://huggingface.co/datasets/openwebtext) |
codesue | null | @article{Kilgarriff2013,
doi = {10.1007/s10579-013-9251-2},
url = {https://doi.org/10.1007/s10579-013-9251-2},
year = {2013},
month = sep,
publisher = {Springer Science and Business Media {LLC}},
volume = {48},
number = {1},
pages = {121--163},
author = {Adam Kilgarriff and Frieda Charalabopoulou and Maria Gavrilidou and Janne Bondi Johannessen and Saussan Khalil and Sofie Johansson Kokkinakis and Robert Lew and Serge Sharoff and Ravikiran Vadlapudi and Elena Volodina},
title = {Corpus-based vocabulary lists for language learners for nine languages},
journal = {Language Resources and Evaluation}
} | The Swedish Kelly list is a freely available frequency-based vocabulary list that comprises general-purpose language of modern Swedish. The list was generated from a large web-acquired corpus (SweWaC) of 114 million words dating from the 2010s. It is adapted to the needs of language learners and contains 8,425 most frequent lemmas that cover 80% of SweWaC.\ | false | 1 | false | codesue/kelly | 2022-09-16T18:57:33.000Z | null | false | 2137d4b378715475fb63be6fee0258992c20388e | [] | [
"annotations_creators:expert-generated",
"language:sv",
"language_creators:expert-generated",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"tags:lexicon",
"tags:swedish",
"tags:CEFR",
"task_categories:text-classification",
"task_ids:text-scoring"
] | https://huggingface.co/datasets/codesue/kelly/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- sv
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: kelly
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- lexicon
- swedish
- CEFR
task_categories:
- text-classification
task_ids:
- text-scoring
---
# Dataset Card for Kelly
Keywords for Language Learning for Young and adults alike
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://spraakbanken.gu.se/en/resources/kelly
- **Paper:** https://link.springer.com/article/10.1007/s10579-013-9251-2
### Dataset Summary
The Swedish Kelly list is a freely available frequency-based vocabulary list
that comprises general-purpose language of modern Swedish. The list was
generated from a large web-acquired corpus (SweWaC) of 114 million words
dating from the 2010s. It is adapted to the needs of language learners and
contains 8,425 most frequent lemmas that cover 80% of SweWaC.
### Languages
Swedish (sv-SE)
## Dataset Structure
### Data Instances
Here is a sample of the data:
```python
{
'id': 190,
'raw_frequency': 117835.0,
'relative_frequency': 1033.61,
'cefr_level': 'A1',
'source': 'SweWaC',
'marker': 'en',
'lemma': 'dag',
'pos': 'noun-en',
'examples': 'e.g. god dag'
}
```
This can be understood as:
> The common noun "dag" ("day") has a rank of 190 in the list. It was used 117,835
times in SweWaC, meaning it occured 1033.61 times per million words. This word
is among the most important vocabulary words for Swedish language learners and
should be learned at the A1 CEFR level. An example usage of this word is the
phrase "god dag" ("good day").
### Data Fields
- `id`: The row number for the data entry, starting at 1. Generally corresponds
to the rank of the word.
- `raw_frequency`: The raw frequency of the word.
- `relative_frequency`: The relative frequency of the word measured in
number of occurences per million words.
- `cefr_level`: The CEFR level (A1, A2, B1, B2, C1, C2) of the word.
- `source`: Whether the word came from SweWaC, translation lists (T2), or
was manually added (manual).
- `marker`: The grammatical marker of the word, if any, such as an article or
infinitive marker.
- `lemma`: The lemma of the word, sometimes provided with its spelling or
stylistic variants.
- `pos`: The word's part-of-speech.
- `examples`: Usage examples and comments. Only available for some of the words.
Manual entries were prepended to the list, giving them a higher rank than they
might otherwise have had. For example, the manual entry "Göteborg ("Gothenberg")
has a rank of 20, while the first non-manual entry "och" ("and") has a rank of
87. However, a conjunction and common stopword is far more likely to occur than
the name of a city.
### Data Splits
There is a single split, `train`.
## Dataset Creation
Please refer to the article [Corpus-based approaches for the creation of a frequency
based vocabulary list in the EU project KELLY – issues on reliability, validity and
coverage](https://gup.ub.gu.se/publication/148533?lang=en) for information about how
the original dataset was created and considerations for using the data.
**The following changes have been made to the original dataset**:
- Changed header names.
- Normalized the large web-acquired corpus name to "SweWac" in the `source` field.
- Set the relative frequency of manual entries to null rather than 1000000.
## Additional Information
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0)
### Citation Information
Please cite the authors if you use this dataset in your work:
```bibtex
@article{Kilgarriff2013,
doi = {10.1007/s10579-013-9251-2},
url = {https://doi.org/10.1007/s10579-013-9251-2},
year = {2013},
month = sep,
publisher = {Springer Science and Business Media {LLC}},
volume = {48},
number = {1},
pages = {121--163},
author = {Adam Kilgarriff and Frieda Charalabopoulou and Maria Gavrilidou and Janne Bondi Johannessen and Saussan Khalil and Sofie Johansson Kokkinakis and Robert Lew and Serge Sharoff and Ravikiran Vadlapudi and Elena Volodina},
title = {Corpus-based vocabulary lists for language learners for nine languages},
journal = {Language Resources and Evaluation}
}
```
### Contributions
Thanks to [@spraakbanken](https://github.com/spraakbanken) for creating this dataset
and to [@codesue](https://github.com/codesue) for adding it.
|
beki | null | null | null | false | 61 | false | beki/privy | 2022-11-09T04:49:44.000Z | null | false | 80d6498d8625be683fb0dba0cfdb5b2e0cfa5db3 | [] | [
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<200K",
"size_categories:300K<n<400K",
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"tags:pii-detection"
] | https://huggingface.co/datasets/beki/privy/resolve/main/README.md | ---
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<200K
- 300K<n<400K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
tags:
- pii-detection
train-eval-index:
- config: privy-small
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
metrics:
- type: seqeval
name: seqeval
---
# Dataset Card for "privy-english"
## 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://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy](https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy)
### Dataset Summary
A synthetic PII dataset generated using [Privy](https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy), a tool which parses OpenAPI specifications and generates synthetic request payloads, searching for keywords in API schema definitions to select appropriate data providers. Generated API payloads are converted to various protocol trace formats like JSON and SQL to approximate the data developers might encounter while debugging applications.
This labelled PII dataset consists of protocol traces (JSON, SQL (PostgreSQL, MySQL), HTML, and XML) generated from OpenAPI specifications and includes 60+ PII types.
### Supported Tasks and Leaderboards
Named Entity Recognition (NER) and PII classification.
### Label Scheme
<details>
<summary>View label scheme (26 labels for 60 PII data providers)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `PERSON`, `LOCATION`, `NRP`, `DATE_TIME`, `CREDIT_CARD`, `URL`, `IBAN_CODE`, `US_BANK_NUMBER`, `PHONE_NUMBER`, `US_SSN`, `US_PASSPORT`, `US_DRIVER_LICENSE`, `IP_ADDRESS`, `US_ITIN`, `EMAIL_ADDRESS`, `ORGANIZATION`, `TITLE`, `COORDINATE`, `IMEI`, `PASSWORD`, `LICENSE_PLATE`, `CURRENCY`, `ROUTING_NUMBER`, `SWIFT_CODE`, `MAC_ADDRESS`, `AGE` |
</details>
### Languages
English
## Dataset Structure
### Data Instances
A sample:
```
{
"full_text": "{\"full_name_female\": \"Bethany Williams\", \"NewServerCertificateName\": \"\", \"NewPath\": \"\", \"ServerCertificateName\": \"dCwMNqR\", \"Action\": \"\", \"Version\": \"u zNS zNS\"}",
"masked": "{\"full_name_female\": \"{{name_female}}\", \"NewServerCertificateName\": \"{{string}}\", \"NewPath\": \"{{string}}\", \"ServerCertificateName\": \"{{string}}\", \"Action\": \"{{string}}\", \"Version\": \"{{string}}\"}",
"spans": [
{
"entity_type": "PERSON",
"entity_value": "Bethany Williams",
"start_position": 22,
"end_position": 38
}
],
"template_id": 51889,
"metadata": null
}
```
## 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
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 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
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Contributions
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Datatang | null | null | null | false | 2 | false | Datatang/Mandarin_Spontaneous_Speech_Data_by_Mobile_Phone | 2022-09-16T10:25:52.000Z | null | false | 385440eda4255ec56432277f19ca50986272a0ef | [] | [] | https://huggingface.co/datasets/Datatang/Mandarin_Spontaneous_Speech_Data_by_Mobile_Phone/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Mandarin_Spontaneous_Speech_Data_by_Mobile_Phone
## 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://bit.ly/3BKC1xP
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The data were recorded by 700 Mandarin speakers, 65% of whom were women. There is no pre-made text, and speakers makes phone calls in a natural way while recording the contents of the calls. This data mainly labels the near-end speech, and the speech content is naturally colloquial.
For more details, please refer to the link: https://bit.ly/3BKC1xP
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Mandarin
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 2 | false | Datatang/Korean_Conversational_Speech_Data_by_Mobile_Phone | 2022-09-16T10:23:42.000Z | null | false | 9732d8c37715906c6c0b24201ac752dd5bb16bb6 | [] | [] | https://huggingface.co/datasets/Datatang/Korean_Conversational_Speech_Data_by_Mobile_Phone/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Korean_Conversational_Speech_Data_by_Mobile_Phone
## 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://bit.ly/3xt8dDm
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
About 700 Korean speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy.
For more details, please refer to the link: https://bit.ly/3xt8dDm
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Korean
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
Datatang | null | null | null | false | 2 | false | Datatang/Japanese_Conversational_Speech_by_Mobile_Phone | 2022-09-16T10:22:25.000Z | null | false | 01718993b09eed191564abf93e1228aa5b2c8a45 | [] | [] | https://huggingface.co/datasets/Datatang/Japanese_Conversational_Speech_by_Mobile_Phone/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Japanese_Conversational_Speech_by_Mobile_Phone
## 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://bit.ly/3dhzNfY
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
About 1000 speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy.
For more details, please refer to the link: https://bit.ly/3dhzNfY
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Japanese
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
Datatang | null | null | null | false | 2 | false | Datatang/Italian_Conversational_Speech_Data_by_Mobile_Phone | 2022-09-16T10:20:08.000Z | null | false | 00ac302b142df7a44057882907d35243c94bb517 | [] | [] | https://huggingface.co/datasets/Datatang/Italian_Conversational_Speech_Data_by_Mobile_Phone/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Datatang/Italian_Conversational_Speech_Data_by_Mobile_Phone
## 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://bit.ly/3DyMeyL
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
About 700 speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy.
For more details, please refer to the link: https://bit.ly/3DyMeyL
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Italian
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
psyche | null | null | null | false | 6 | false | psyche/korean_idioms | 2022-10-23T04:02:44.000Z | null | false | b96e3be1f0db925f88558b78d9092a1269c814e0 | [] | [
"annotations_creators:machine-generated",
"language:ko",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification"
] | https://huggingface.co/datasets/psyche/korean_idioms/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language:
- ko
language_creators:
- found
multilinguality:
- monolingual
pretty_name: psyche/korean_idioms
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- text-classification
---
NLI를 위한 한국어 속담 데이터셋입니다.
'question'은 속담의 의미와 보기(5지선다)가 표시되어 있으며,
'label'에는 정답의 번호(0-4)가 표시되어 있습니다.
licence: cc-by-sa-2.0-kr (원본 출처:국립국어원 표준국어대사전)
|Model| psyche/korean_idioms |
|:------:|:---:|
|klue/bert-base|0.7646| |
psyche | null | null | null | false | 2 | false | psyche/bool_sentence | 2022-10-23T02:52:40.000Z | null | false | 28fb0d7e0d32c1ac7b6dd09f8d9a4e283212e1c0 | [] | [
"annotations_creators:machine-generated",
"language:ko",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification"
] | https://huggingface.co/datasets/psyche/bool_sentence/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language:
- ko
language_creators:
- found
multilinguality:
- monolingual
pretty_name: psyche/bool_sentence
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- text-classification
task_ids: []
---
|Model| psyche/bool_sentence (10k) |
|:------:|:---:|
|klue/bert-base|0.9335|
licence: cc-by-sa-2.0-kr (원본 출처:국립국어원 표준국어대사전) |
jelber2 | null | null | null | false | 3 | false | jelber2/RustBioGPT | 2022-09-27T12:02:09.000Z | null | false | 7dfaa5ab1015d802d08b5ca624675a53d4502bda | [] | [
"license:mit"
] | https://huggingface.co/datasets/jelber2/RustBioGPT/resolve/main/README.md | ---
license: mit
---
```sh
git clone https://github.com/natir/br.git
git clone https://github.com/natir/pcon
git clone https://github.com/natir/yacrd
git clone https://github.com/natir/rasusa
git clone https://github.com/natir/fpa
git clone https://github.com/natir/kmrf
rm -f RustBioGPT-train.csv && for i in `find . -name "*.rs"`;do paste -d "," <(echo $i|perl -pe "s/\.\/(\w+)\/.+/\"\1\"/g") <(echo $i|perl -pe "s/(.+)/\"\1\"/g") <(perl -pe "s/\n/\\\n/g" $i|perl -pe s"/\"/\'/g" |perl -pe "s/(.+)/\"\1\"/g") <(echo "mit"|perl -pe "s/(.+)/\"\1\"/g") >> RustBioGPT-train.csv; done
sed -i '1i "repo_name","path","content","license"' RustBioGPT-train.csv
``` |
wjm123 | null | null | null | false | 4 | false | wjm123/wjm123 | 2022-09-16T13:18:02.000Z | null | false | 6a10b37e1971cde1ac72ff68a431519efcbe249a | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/wjm123/wjm123/resolve/main/README.md | ---
license: afl-3.0
---
|
cakiki | null | null | null | false | 1 | false | cakiki/token-graph | 2022-09-17T09:31:00.000Z | null | false | 5156a742da7df2bd1796e2e34840ca6231509e82 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/cakiki/token-graph/resolve/main/README.md | ---
license: apache-2.0
---
|
PlanTL-GOB-ES | null | ADD CITATION | professional translation into Spanish of Winograd NLI dataset as published in GLUE Benchmark.
The Winograd NLI dataset presents 855 sentence pairs,
in which the first sentence contains an ambiguity and the second one a possible interpretation of it.
The label indicates if the interpretation is correct (1) or not (0). | false | 1 | false | PlanTL-GOB-ES/wnli-es | 2022-11-15T17:30:16.000Z | null | false | 4a21b6934920f79132d4efae4ca863745d01faef | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:es",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|glue",
"task_categories:text-classification",
"task_ids:natural-language-inference"
] | https://huggingface.co/datasets/PlanTL-GOB-ES/wnli-es/resolve/main/README.md | ---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: wnli-es
size_categories:
- unknown
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# WNLI-es
## 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
- **Website:** https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html
- **Point of Contact:** [Carlos Rodríguez-Penagos](carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](carme.armentano@bsc.es)
### Dataset Summary
"A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from Terry Winograd." Source: [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
The [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) presents 855 sentence pairs, in which the first sentence contains an ambiguity and the second one a possible interpretation of it. The label indicates if the interpretation is correct (1) or not (0).
This dataset is a professional translation into Spanish of [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) as published in [GLUE Benchmark](https://gluebenchmark.com/tasks).
Both the original dataset and this translation are licenced under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
### Supported Tasks and Leaderboards
Textual entailment, Text classification, Language Model.
### Languages
* Spanish (es)
## Dataset Structure
### Data Instances
Three tsv files.
### Data Fields
- index
- sentence 1: first sentence of the pair
- sentence 2: second sentence of the pair
- label: relation between the two sentences:
* 0: the second sentence does not entail a correct interpretation of the first one (neutral)
* 1: the second sentence entails a correct interpretation of the first one (entailment)
### Data Splits
- wnli-train-es.csv: 636 sentence pairs
- wnli-dev-es.csv: 72 sentence pairs
- wnli-test-shuffled-es.csv: 147 sentence pairs
## Dataset Creation
### Curation Rationale
We translated this dataset to contribute to the development of language models in Spanish.
### Source Data
- [GLUE Benchmark site](https://gluebenchmark.com)
#### Initial Data Collection and Normalization
This is a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Spanish, commissioned by [BSC TeMU](https://temu.bsc.es/) within the the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
#### Who are the source language producers?
For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
### Annotations
#### Annotation process
We comissioned a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Spanish.
#### Who are the annotators?
Translation was commisioned to a professional translation agency.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contributes to the development of language models in Spanish.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
## Copyright
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
## Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
## Funding
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
|
autoevaluate | null | null | null | false | 5 | false | autoevaluate/autoeval-eval-squad_v2-squad_v2-e15d25-1483654271 | 2022-09-16T16:19:11.000Z | null | false | 4a15933dcd0acf4d468b13e12f601a4e456deeb6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-e15d25-1483654271/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Jiqing/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Jiqing/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-eval-squad_v2-squad_v2-e15d25-1483654272 | 2022-09-16T16:16:56.000Z | null | false | dd8b911a18f8578bdc3a4009ce27af553ff6dd62 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-e15d25-1483654272/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: MYX4567/distilbert-base-uncased-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: MYX4567/distilbert-base-uncased-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
Violence | null | null | null | false | 6 | false | Violence/Cloud | 2022-09-16T17:45:20.000Z | null | false | ad46374198d1c2b567649b3aef123d746ba4278c | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/Violence/Cloud/resolve/main/README.md | ---
license: afl-3.0
---
|
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-eval-autoevaluate__zero-shot-classification-sample-autoevalu-912bbb-1484454284 | 2022-09-16T17:56:15.000Z | null | false | ecd209ffe06e918e4c7e7ce8684640434697e830 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:autoevaluate/zero-shot-classification-sample"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-autoevaluate__zero-shot-classification-sample-autoevalu-912bbb-1484454284/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- autoevaluate/zero-shot-classification-sample
eval_info:
task: text_zero_shot_classification
model: mathemakitten/opt-125m
metrics: []
dataset_name: autoevaluate/zero-shot-classification-sample
dataset_config: autoevaluate--zero-shot-classification-sample
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: mathemakitten/opt-125m
* Dataset: autoevaluate/zero-shot-classification-sample
* Config: autoevaluate--zero-shot-classification-sample
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-eval-autoevaluate__zero-shot-classification-sample-autoevalu-c3526e-1484354283 | 2022-09-16T17:56:15.000Z | null | false | 63a9e740124aeaed97c6cc48ed107b95833d7121 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:autoevaluate/zero-shot-classification-sample"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-autoevaluate__zero-shot-classification-sample-autoevalu-c3526e-1484354283/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- autoevaluate/zero-shot-classification-sample
eval_info:
task: text_zero_shot_classification
model: mathemakitten/opt-125m
metrics: []
dataset_name: autoevaluate/zero-shot-classification-sample
dataset_config: autoevaluate--zero-shot-classification-sample
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: mathemakitten/opt-125m
* Dataset: autoevaluate/zero-shot-classification-sample
* Config: autoevaluate--zero-shot-classification-sample
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
spacemanidol | null | null | null | false | 6 | false | spacemanidol/query-rewriting-dense-retrieval | 2022-09-16T18:08:15.000Z | null | false | 589bf157b543e47fc4bc6e2d681eb765df768a60 | [] | [
"license:mit"
] | https://huggingface.co/datasets/spacemanidol/query-rewriting-dense-retrieval/resolve/main/README.md | ---
license: mit
---
|
jemale | null | null | null | false | 6 | false | jemale/test | 2022-09-16T18:27:16.000Z | null | false | 37ea2ff12fdef2021a8068cf76c186aa9c1ca50a | [] | [
"license:mit"
] | https://huggingface.co/datasets/jemale/test/resolve/main/README.md | ---
license: mit
---
|
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554291 | 2022-09-16T20:22:45.000Z | null | false | 4f7cf75267bc4b751a03ed9f668350be69d9ce4a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:conll2003"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554291/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: chandrasutrisnotjhong/bert-finetuned-ner
metrics: []
dataset_name: conll2003
dataset_config: conll2003
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: chandrasutrisnotjhong/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554292 | 2022-09-16T20:23:02.000Z | null | false | c816be36bf214a2b8ed525580d849ac7df0d2634 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:conll2003"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554292/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: baptiste/deberta-finetuned-ner
metrics: []
dataset_name: conll2003
dataset_config: conll2003
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: baptiste/deberta-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554294 | 2022-09-16T20:23:36.000Z | null | false | 4c2a0ee535002890fffbd6b6a0fe8afc5bc2f6cf | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:conll2003"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554294/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: mariolinml/roberta_large-ner-conll2003_0818_v0
metrics: []
dataset_name: conll2003
dataset_config: conll2003
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: mariolinml/roberta_large-ner-conll2003_0818_v0
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554295 | 2022-09-16T20:23:06.000Z | null | false | 5e2e4e90132c48d0b3e0afa6337a75225510eb8a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:conll2003"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554295/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: jjglilleberg/bert-finetuned-ner
metrics: []
dataset_name: conll2003
dataset_config: conll2003
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: jjglilleberg/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554297 | 2022-09-16T20:23:19.000Z | null | false | 2105a9d5dd2b3d9ca6f7a7d51c60455a31a40e2a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:conll2003"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-conll2003-conll2003-bc26c9-1485554297/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: Yv/bert-finetuned-ner
metrics: []
dataset_name: conll2003
dataset_config: conll2003
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: Yv/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-emotion-default-fe1aa0-1485654301 | 2022-09-16T20:22:59.000Z | null | false | 6d4a3c8d5c40bf818348fcef1f6147e947481fef | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-emotion-default-fe1aa0-1485654301/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: armandnlp/distilbert-base-uncased-finetuned-emotion
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
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: armandnlp/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-emotion-default-fe1aa0-1485654303 | 2022-09-16T20:23:06.000Z | null | false | f009dc448491e5daf234a5e867b3fb012e366dc9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-emotion-default-fe1aa0-1485654303/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: andreaschandra/distilbert-base-uncased-finetuned-emotion
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
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: andreaschandra/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-emotion-default-fe1aa0-1485654304 | 2022-09-16T20:23:15.000Z | null | false | b42408bed4845eabbde9ec840f2c77be1ce455ae | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-emotion-default-fe1aa0-1485654304/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: bousejin/distilbert-base-uncased-finetuned-emotion
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
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: bousejin/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
Chemsseddine | null | null | null | false | 8 | false | Chemsseddine/autotrain-data-consbert | 2022-09-16T21:03:18.000Z | null | false | 8f69a50e60bac11a0b2f12e5354f0678281aaf50 | [] | [
"task_categories:text-classification"
] | https://huggingface.co/datasets/Chemsseddine/autotrain-data-consbert/resolve/main/README.md | ---
task_categories:
- text-classification
---
# AutoTrain Dataset for project: consbert
## Dataset Description
This dataset has been automatically processed by AutoTrain for project consbert.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "DECLARATION OF PERFORMANCE fermacell Screws 1. unique identification code of the product type 2. purpose of use 3. manufacturer 5. system(s) for assessment and verification of constancy of performance 6. harmonised standard Notified body(ies) 7. Declared performance Essential feature Reaction to fire Tensile strength Length Corrosion protection (Reis oeueelt Nr. FC-0103 A FC-0103 A Drywall screws type TSN for fastening gypsum fibreboards James Hardie Europe GmbH Bennigsen- Platz 1 D-40474 Disseldorf Tel. +49 800 3864001 E-Mail fermacell jameshardie.de System 4 DIN EN 14566:2008+A1:2009 Stichting Hout Research (2590) Performance Al fulfilled <63mm Phosphated - Class 48 The performance of the above product corresponds to the declared performance(s). The manufacturer mentioned aboveis solely responsible for the preparation of the declaration of performancein accordance with Regulation (EU) No. 305/2011. Signed for the manufacturer and on behalf of the manufacturerof: Dusseldorf, 01.01.2020 2020 James Hardie Europe GmbH. and designate registered and incorporated trademarks of James Hardie Technology Limited Dr. J\u00e9rg Brinkmann (CEO) AESTUVER Seite 1/1 ",
"target": 1
},
{
"text": "DERBIGUM\u201d MAKING BUILDINGS SMART 9 - Performances d\u00e9clar\u00e9es selon EN 13707 : 2004 + A2: 2009 Caract\u00e9ristiques essentielles Performances Unit\u00e9s R\u00e9sistance a un feu ext\u00e9rieur (Note 1) FRoof (t3) - R\u00e9action au feu F - Etanch\u00e9it\u00e9 a l\u2019eau Conforme - Propri\u00e9t\u00e9s en traction : R\u00e9sistance en traction LxT* 900 x 700(+4 20%) N/50 mm Allongement LxT* 45 x 45 (+ 15) % R\u00e9sistance aux racines NPD** - R\u00e9sistance au poinconnementstatique (A) 20 kg R\u00e9sistance au choc (A et B) NPD** mm R\u00e9sistance a la d\u00e9chirure LxT* 200 x 200 (+ 20%) N R\u00e9sistance des jonctions: R\u00e9sistance au pelage NPD** N/50 mm R\u00e9sistance au cisaillement NPD** N/50 mm Durabilit\u00e9 : Sous UV, eau et chaleur Conforme - Pliabilit\u00e9 a froid apr\u00e9s vieillissement a la -10 (+ 5) \u00b0C chaleur Pliabilit\u00e9 a froid -18 \u00b0C Substances dangereuses (Note 2) - * L signifie la direction longitudinale, T signifie la direction transversale **NPD signifie Performance Non D\u00e9termin\u00e9e Note 1: Aucune performance ne peut \u00e9tre donn\u00e9e pourle produit seul, la performance de r\u00e9sistance a un feu ext\u00e9rieur d\u2019une toiture d\u00e9pend du syst\u00e9me complet Note 2: En l\u2019absence de norme d\u2019essai europ\u00e9enne harmonis\u00e9e, aucune performanceli\u00e9e au comportementa la lixiviation ne peut \u00e9tre d\u00e9clar\u00e9e, la d\u00e9claration doit \u00e9tre \u00e9tablie selon les dispositions nationales en vigueur. 10 - Les performances du produit identifi\u00e9 aux points 1 et 2 ci-dessus sont conformes aux performances d\u00e9clar\u00e9es indiqu\u00e9es au point 9. La pr\u00e9sente d\u00e9claration des performances est \u00e9tablie sous la seule responsabilit\u00e9 du fabricant identifi\u00e9 au point 4 Sign\u00e9 pourle fabricant et en son nom par: Mr Steve Geubels, Group Operations Director Perwez ,30/09/2016 Page 2 of 2 ",
"target": 8
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=9, names=['0', '1', '2', '3', '4', '5', '6', '7', '8'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 59 |
| valid | 18 |
|
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-Tristan__zero-shot-classification-large-test-Tristan__z-7873ce-1486054319 | 2022-09-17T00:43:54.000Z | null | false | 55c4e0884053ad905c6ceccdff7e02e8a0d9c7b8 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Tristan/zero-shot-classification-large-test"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-Tristan__zero-shot-classification-large-test-Tristan__z-7873ce-1486054319/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Tristan/zero-shot-classification-large-test
eval_info:
task: text_zero_shot_classification
model: autoevaluate/zero-shot-classification
metrics: []
dataset_name: Tristan/zero-shot-classification-large-test
dataset_config: Tristan--zero-shot-classification-large-test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: autoevaluate/zero-shot-classification
* Dataset: Tristan/zero-shot-classification-large-test
* Config: Tristan--zero-shot-classification-large-test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Tristan](https://huggingface.co/Tristan) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-samsum-samsum-7cb0ac-1486354325 | 2022-09-17T02:01:53.000Z | null | false | 35d2e5d9f41feed5ca053572780ad7263b060d96 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-samsum-samsum-7cb0ac-1486354325/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
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: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@samuelfipps123](https://huggingface.co/samuelfipps123) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-samsum-samsum-2c3c14-1486454326 | 2022-09-17T02:46:32.000Z | null | false | 834a9ec3ad3d01d96e9371cce33ce5a28a721102 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-samsum-samsum-2c3c14-1486454326/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: train
col_mapping:
text: dialogue
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: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
* Dataset: samsum
* Config: samsum
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-eval-samsum-samsum-1bb2ba-1486554327 | 2022-09-17T02:02:01.000Z | null | false | 7f5976b44f8b7f02b192b65fd7163c1a5a969940 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-samsum-samsum-1bb2ba-1486554327/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: validation
col_mapping:
text: dialogue
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: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
* Dataset: samsum
* Config: samsum
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model. |
firqaaa | null | null | null | false | 8 | false | firqaaa/mnli-id | 2022-09-18T02:19:53.000Z | null | false | ebf16d0b10414cc0bfedc10c1d1aafb81761364e | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/firqaaa/mnli-id/resolve/main/README.md | ---
license: cc-by-sa-4.0
---
|
darcy01 | null | null | null | false | 9 | false | darcy01/autotrain-data-opus-mt-en-zh_hanz | 2022-09-17T11:36:03.000Z | null | false | a26e48dc333aa4403237068028ac612fe2e9581f | [] | [
"language:en",
"language:zh",
"task_categories:translation"
] | https://huggingface.co/datasets/darcy01/autotrain-data-opus-mt-en-zh_hanz/resolve/main/README.md | ---
language:
- en
- zh
task_categories:
- translation
---
# AutoTrain Dataset for project: opus-mt-en-zh_hanz
## Dataset Description
This dataset has been automatically processed by AutoTrain for project opus-mt-en-zh_hanz.
### Languages
The BCP-47 code for the dataset's language is en2zh.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"source": "And then I hear something.",
"target": "\u63a5\u7740\u542c\u5230\u4ec0\u4e48\u52a8\u9759\u3002",
"feat_en_length": 26,
"feat_zh_length": 9
},
{
"source": "A ghostly iron whistle blows through the tunnels.",
"target": "\u9b3c\u9b45\u7684\u54e8\u58f0\u5439\u8fc7\u96a7\u9053\u3002",
"feat_en_length": 49,
"feat_zh_length": 10
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"source": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)",
"feat_en_length": "Value(dtype='int64', id=None)",
"feat_zh_length": "Value(dtype='int64', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 16350 |
| valid | 4088 |
|
darcy01 | null | null | null | false | 7 | false | darcy01/hanz_en-zh | 2022-09-17T11:38:43.000Z | null | false | 5875acfc5d2c5bc89e33fed4ba9251591fdb06d6 | [] | [
"license:bsd"
] | https://huggingface.co/datasets/darcy01/hanz_en-zh/resolve/main/README.md | ---
license: bsd
---
|
slone | null | null | null | false | 10 | false | slone/myv_ru_2022 | 2022-09-28T19:38:26.000Z | null | false | f8d2cc4cbdeb4b666ef8342830bcb6525ba09fbb | [] | [
"arxiv:2209.09368",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language:myv",
"language:ru",
"language_creators:found",
"language_creators:machine-generated",
"license:cc-by-sa-4.0",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:origi... | https://huggingface.co/datasets/slone/myv_ru_2022/resolve/main/README.md | ---
annotations_creators:
- found
- machine-generated
language:
- myv
- ru
language_creators:
- found
- machine-generated
license:
- cc-by-sa-4.0
multilinguality:
- translation
pretty_name: Erzya-Russian parallel corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- erzya
- mordovian
task_categories:
- translation
task_ids: []
---
# Dataset Card for **slone/myv_ru_2022**
## Dataset Description
- **Repository:** https://github.com/slone-nlp/myv-nmt
- **Paper:**: https://arxiv.org/abs/2209.09368
- **Point of Contact:** @cointegrated
### Dataset Summary
This is a corpus of parallel Erzya-Russian words, phrases and sentences, collected in the paper [The first neural machine translation system for the Erzya language](https://arxiv.org/abs/2209.09368).
Erzya (`myv`) is a language from the Uralic family. It is spoken primarily in the Republic of Mordovia and some other regions of Russia and other post-Soviet countries. We use the Cyrillic version of its script.
The corpus consists of the following parts:
| name | size | composition |
| -----| ---- | -------|
|train | 74503 | parallel words, phrases and sentences, mined from dictionaries, books and web texts |
| dev | 1500 | parallel sentences mined from books and web texts |
| test | 1500 | parallel sentences mined from books and web texts |
| mono | 333651| Erzya sentences mined from books and web texts, translated to Russian by a neural model |
The dev and test splits contain sentences from the following sources
| name | size | description|
| ---------------|----| -------|
|wiki |600 | Aligned sentences from linked Erzya and Russian Wikipedia articles |
|bible |400 | Paired verses from the Bible (https://finugorbib.com) |
|games |250 | Aligned sentences from the book *"Сказовые формы мордовской литературы", И.И. Шеянова, 2017, НИИ гуманитарых наук при Правительстве Республики Мордовия, Саранск* |
|tales |100 | Aligned sentences from the book *"Мордовские народные игры", В.С. Брыжинский, 2009, Мордовское книжное издательство, Саранск* |
|fiction |100 | Aligned sentences from modern Erzya prose and poetry (https://rus4all.ru/myv) |
|constitution | 50 | Aligned sentences from the Soviet 1938 constitution |
To load the first three parts (train, validation and test), use the code:
```Python
from datasets import load_dataset
data = load_dataset('slone/myv_ru_2022')
```
To load all four parts (included the back-translated data), please specify the data files explicitly:
```Python
from datasets import load_dataset
data_extended = load_dataset(
'slone/myv_ru_2022',
data_files={'train':'train.jsonl', 'validation': 'dev.jsonl', 'test': 'test.jsonl', 'mono': 'back_translated.jsonl'}
)
```
### Supported Tasks and Leaderboards
- `translation`: the dataset may be used to train `ru-myv` translation models. There are no specific leaderboards for it yet, but if you feel like discussing it, welcome to the comments!
### Languages
The main part of the dataset (`train`, `dev` and `test`) consists of "natural" Erzya (Cyrillic) and Russian sentences, translated to the other language by humans. There is also a larger Erzya-only part of the corpus (`mono`), translated to Russian automatically.
## Dataset Structure
### Data Instances
All data instances have three string fields: `myv`, `ru` and `src` (the last one is currently meaningful only for dev and test splits), for example:
```
{'myv': 'Сюкпря Пазонтень, кие кирвазтизе Титэнь седейс тынк кисэ секе жо бажамонть, кона палы минек седейсэяк!',
'ru': 'Благодарение Богу, вложившему в сердце Титово такое усердие к вам.',
'src': 'bible'}
```
### Data Fields
- `myv`: the Erzya text (word, phrase, or sentence)
- `ru`: the corresponding Russian text
- `src`: the source of data (only for dev and test splits)
### Data Splits
- train: parallel sentences, words and phrases, collected from various sources. Most of them are aligned automatically. Noisy.
- dev: 1500 parallel sentences, selected from the 6 most reliable and diverse sources.
- test: same as dev.
- mono: Erzya sentences collected from various sources, with the Russian counterpart generated by a neural machine translation model.
## Dataset Creation
### Curation Rationale
This is, as far as we know, the first publicly available parallel Russian-Erzya corpus, and the first medium-sized translation corpus for Erzya.
We hope that it sets a meaningful baseline for Erzya machine translation.
### Source Data
#### Initial Data Collection and Normalization
The dataset was collected from various sources (see below).
The texts were spit in sentences using the [razdel]() package.
For some sources, sentences were filtered by language using the [slone/fastText-LID-323](https://huggingface.co/slone/fastText-LID-323) model.
For most of the sources, `myv` and `ru` sentences were aligned automatically using the [slone/LaBSE-en-ru-myv-v1](https://huggingface.co/slone/LaBSE-en-ru-myv-v1) sentence encoder
and the code from [the paper repository](https://github.com/slone-nlp/myv-nmt).
#### Who are the source language producers?
The dataset comprises parallel `myv-ru` and monolingual `myv` texts from diverse sources:
- 12K parallel sentences from the Bible (http://finugorbib.com);
- 3K parallel Wikimedia sentences from OPUS;
- 42K parallel words or short phrases collected from various online dictionaries ();
- the Erzya Wikipedia and the corresponding articles from the Russian Wikipedia;
- 18 books, including 3 books with Erzya-Russian bitexts (http://lib.e-mordovia.ru);
- Soviet-time books and periodicals (https://fennougrica.kansalliskirjasto.fi);
- The Erzya part of Wikisource (https://wikisource.org/wiki/Main_Page/?oldid=895127);
- Short texts by modern Erzya authors (https://rus4all.ru/myv/);
- News articles from the Erzya Pravda website (http://erziapr.ru);
- Texts found in LiveJournal (https://www.livejournal.com) by searching with the 100 most frequent Erzya words.
### Annotations
No human annotation was involved in the data collection.
### Personal and Sensitive Information
All data was collected from public sources, so no sensitive information is expected in them.
However, some sentences collected, for example, from news articles or LiveJournal posts, can contain personal data.
## Considerations for Using the Data
### Social Impact of Dataset
Publication of this dataset may attract some attention to the endangered Erzya language.
### Discussion of Biases
Most of the dataset has been collected by automatical means, so it may contain errors and noise.
Some types of these errors are systemic: for example, the words for "Erzya" and "Russian" are often aligned together,
because they appear in the corresponding Wikipedias on similar positions.
### Other Known Limitations
The dataset is noisy: some texts in it may be ungrammatical, in a wrong language, or poorly aligned.
## Additional Information
### Dataset Curators
The data was collected by David Dale (https://huggingface.co/cointegrated).
### Licensing Information
The status of the dataset is not final, but after we check everything, we hope to be able to distribute it under the [CC-BY-SA license](http://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
[TBD]
|
teticio | null | null | null | false | 7 | false | teticio/audio-diffusion-instrumental-hiphop-256 | 2022-11-09T10:50:58.000Z | null | false | dbfe82d9d01c08ca01e402d466e1ac817bdbb182 | [] | [
"size_categories:10K<n<100K",
"tags:audio",
"tags:spectrograms",
"task_categories:image-to-image"
] | https://huggingface.co/datasets/teticio/audio-diffusion-instrumental-hiphop-256/resolve/main/README.md | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: Mel spectrograms of instrumental Hip Hop music
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- audio
- spectrograms
task_categories:
- image-to-image
task_ids: []
---
256x256 mel spectrograms of 5 second samples of instrumental Hip Hop. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models.
```
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
``` |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.