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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
davanstrien | null | null | null | false | null | false | davanstrien/vfr | 2021-11-22T17:07:01.000Z | null | true | 8ba0d981dd7c71485c520ca61c0e62ee8e434234 | [] | [
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"languages:en-GB",
"languages:en-US",
"languages:de-DE",
"languages:fr-FR",
"languages:nl-NL",
"licenses:cc0-1.0",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_da... | https://huggingface.co/datasets/davanstrien/vfr/resolve/main/README.md | |
davidwisdom | null | null | null | false | 321 | false | davidwisdom/reddit-randomness | 2021-11-06T23:56:43.000Z | null | false | 01740f7cd9ffa5855819bd828d5dcb03578abf0e | [] | [] | https://huggingface.co/datasets/davidwisdom/reddit-randomness/resolve/main/README.md | # Reddit Randomness Dataset
A dataset I created because I was curious about how "random" r/random really is.
This data was collected by sending `GET` requests to `https://www.reddit.com/r/random` for a few hours on September 19th, 2021.
I scraped a bit of metadata about the subreddits as well.
`randomness_12k_clean.csv` reports the random subreddits as they happened and `summary.csv` lists some metadata about each subreddit.
# The Data
## `randomness_12k_clean.csv`
This file serves as a record of the 12,055 successful results I got from r/random.
Each row represents one result.
### Fields
* `subreddit`: The name of the subreddit that the scraper recieved from r/random (`string`)
* `response_code`: HTTP response code the scraper recieved when it sent a `GET` request to /r/random (`int`, always `302`)
## `summary.csv`
As the name suggests, this file summarizes `randomness_12k_clean.csv` into the information that I cared about when I analyzed this data.
Each row represents one of the 3,679 unique subreddits and includes some stats about the subreddit as well as the number of times it appears in the results.
### Fields
* `subreddit`: The name of the subreddit (`string`, unique)
* `subscribers`: How many subscribers the subreddit had (`int`, max of `99_886`)
* `current_users`: How many users accessed the subreddit in the past 15 minutes (`int`, max of `999`)
* `creation_date`: Date that the subreddit was created (`YYYY-MM-DD` or `Error:PrivateSub` or `Error:Banned`)
* `date_accessed`: Date that I collected the values in `subscribers` and `current_users` (`YYYY-MM-DD`)
* `time_accessed_UTC`: Time that I collected the values in `subscribers` and `current_users`, reported in UTC+0 (`HH:MM:SS`)
* `appearances`: How many times the subreddit shows up in `randomness_12k_clean.csv` (`int`, max of `9`)
# Missing Values and Quirks
In the `summary.csv` file, there are three missing values.
After I collected the number of subscribers and the number of current users, I went back about a week later to collect the creation date of each subreddit.
In that week, three subreddits had been banned or taken private. I filled in the values with a descriptive string.
* SomethingWasWrong (`Error:PrivateSub`)
* HannahowoOnlyfans (`Error:Banned`)
* JanetGuzman (`Error:Banned`)
I think there are a few NSFW subreddits in the results, even though I only queried r/random and not r/randnsfw.
As a simple example, searching the data for "nsfw" shows that I got the subreddit r/nsfwanimegifs twice.
# License
This dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ |
debajyotidatta | null | null | null | false | 165 | false | debajyotidatta/biosses | 2022-02-01T01:46:29.000Z | null | false | c0b444a1e1fd9773a8ed19fdf9d1034f6b922ead | [] | [
"license:gpl-3.0"
] | https://huggingface.co/datasets/debajyotidatta/biosses/resolve/main/README.md | ---
license: gpl-3.0
---
|
debatelab | null | null | null | false | 340 | false | debatelab/aaac | 2022-10-24T16:25:56.000Z | aaac | false | 6e8e9947c03e380226bb9b3e2e1839d8bd2c05d2 | [] | [
"arxiv:2110.01509",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarizati... | https://huggingface.co/datasets/debatelab/aaac/resolve/main/README.md | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
- text-retrieval
- text-generation
task_ids:
- parsing
- text-simplification
paperswithcode_id: aaac
pretty_name: Artificial Argument Analysis Corpus
language_bcp47:
- en-US
tags:
- argument-mining
- conditional-text-generation
- structure-prediction
---
# Dataset Card for Artificial Argument Analysis Corpus (AAAC)
## 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)
- [Construction of the Synthetic Data](#construction-of-the-synthetic-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://debatelab.github.io/journal/deepa2.html
- **Repository:** None
- **Paper:** G. Betz, K. Richardson. *DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models*. https://arxiv.org/abs/2110.01509
- **Leaderboard:** None
### Dataset Summary
DeepA2 is a modular framework for deep argument analysis. DeepA2 datasets contain comprehensive logical reconstructions of informally presented arguments in short argumentative texts. This document describes two synthetic DeepA2 datasets for artificial argument analysis: AAAC01 and AAAC02.
```sh
# clone
git lfs clone https://huggingface.co/datasets/debatelab/aaac
```
```python
import pandas as pd
from datasets import Dataset
# loading train split as pandas df
df = pd.read_json("aaac/aaac01_train.jsonl", lines=True, orient="records")
# creating dataset from pandas df
Dataset.from_pandas(df)
```
### Supported Tasks and Leaderboards
The multi-dimensional datasets can be used to define various text-2-text tasks (see also [Betz and Richardson 2021](https://arxiv.org/abs/2110.01509)), for example:
* Premise extraction,
* Conclusion extraction,
* Logical formalization,
* Logical reconstrcution.
### Languages
English.
## Dataset Structure
### Data Instances
The following histograms (number of dataset records with given property) describe and compare the two datasets AAAC01 (train split, N=16000) and AAAC02 (dev split, N=4000).
|AAAC01 / train split|AAAC02 / dev split|
|-|-|
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
### Data Fields
The following multi-dimensional example record (2-step argument with one implicit premise) illustrates the structure of the AAAC datasets.
#### argument_source
```
If someone was discovered in 'Moonlight', then they won't play the lead in 'Booksmart',
because being a candidate for the lead in 'Booksmart' is sufficient for not being an
Oscar-Nominee for a role in 'Eighth Grade'. Yet every BAFTA-Nominee for a role in 'The
Shape of Water' is a fan-favourite since 'Moonlight' or a supporting actor in 'Black Panther'.
And if someone is a supporting actor in 'Black Panther', then they could never become the
main actor in 'Booksmart'. Consequently, if someone is a BAFTA-Nominee for a role in
'The Shape of Water', then they are not a candidate for the lead in 'Booksmart'.
```
#### reason_statements
```json
[
{"text":"being a candidate for the lead in 'Booksmart' is sufficient for
not being an Oscar-Nominee for a role in 'Eighth Grade'","starts_at":96,
"ref_reco":2},
{"text":"every BAFTA-Nominee for a role in 'The Shape of Water' is a
fan-favourite since 'Moonlight' or a supporting actor in 'Black Panther'",
"starts_at":221,"ref_reco":4},
{"text":"if someone is a supporting actor in 'Black Panther', then they
could never become the main actor in 'Booksmart'","starts_at":359,
"ref_reco":5}
]
```
#### conclusion_statements
```json
[
{"text":"If someone was discovered in 'Moonlight', then they won't play the
lead in 'Booksmart'","starts_at":0,"ref_reco":3},
{"text":"if someone is a BAFTA-Nominee for a role in 'The Shape of Water',
then they are not a candidate for the lead in 'Booksmart'","starts_at":486,
"ref_reco":6}
]
```
#### distractors
`[]`
#### argdown_reconstruction
```
(1) If someone is a fan-favourite since 'Moonlight', then they are an Oscar-Nominee for a role in 'Eighth Grade'.
(2) If someone is a candidate for the lead in 'Booksmart', then they are not an Oscar-Nominee for a role in 'Eighth Grade'.
--
with hypothetical syllogism {variant: ["negation variant", "transposition"], uses: [1,2]}
--
(3) If someone is beloved for their role in 'Moonlight', then they don't audition in
'Booksmart'.
(4) If someone is a BAFTA-Nominee for a role in 'The Shape of Water', then they are a fan-favourite since 'Moonlight' or a supporting actor in 'Black Panther'.
(5) If someone is a supporting actor in 'Black Panther', then they don't audition in
'Booksmart'.
--
with generalized dilemma {variant: ["negation variant"], uses: [3,4,5]}
--
(6) If someone is a BAFTA-Nominee for a role in 'The Shape of Water', then they are not a
candidate for the lead in 'Booksmart'.
```
#### premises
```json
[
{"ref_reco":1,"text":"If someone is a fan-favourite since 'Moonlight', then
they are an Oscar-Nominee for a role in 'Eighth Grade'.","explicit":false},
{"ref_reco":2,"text":"If someone is a candidate for the lead in
'Booksmart', then they are not an Oscar-Nominee for a role in 'Eighth
Grade'.","explicit":true},
{"ref_reco":4,"text":"If someone is a BAFTA-Nominee for a role in 'The
Shape of Water', then they are a fan-favourite since 'Moonlight' or a
supporting actor in 'Black Panther'.","explicit":true},
{"ref_reco":5,"text":"If someone is a supporting actor in 'Black Panther',
then they don't audition in 'Booksmart'.","explicit":true}
]
```
#### premises_formalized
```json
[
{"form":"(x): ${F2}x -> ${F5}x","ref_reco":1},
{"form":"(x): ${F4}x -> ¬${F5}x","ref_reco":2},
{"form":"(x): ${F1}x -> (${F2}x v ${F3}x)","ref_reco":4},
{"form":"(x): ${F3}x -> ¬${F4}x","ref_reco":5}
]
```
#### conclusion
```json
[{"ref_reco":6,"text":"If someone is a BAFTA-Nominee for a role in 'The Shape
of Water', then they are not a candidate for the lead in 'Booksmart'.",
"explicit":true}]
```
#### conclusion_formalized
```json
[{"form":"(x): ${F1}x -> ¬${F4}x","ref_reco":6}]
```
#### intermediary_conclusions
```json
[{"ref_reco":3,"text":"If someone is beloved for their role in 'Moonlight',
then they don't audition in 'Booksmart'.","explicit":true}]
```
#### intermediary_conclusions_formalized
```json
[{"form":"(x): ${F2}x -> ¬${F4}x","ref_reco":3}]
```
#### plcd_subs
```json
{
"F1":"BAFTA-Nominee for a role in 'The Shape of Water'",
"F2":"fan-favourite since 'Moonlight'",
"F3":"supporting actor in 'Black Panther'",
"F4":"candidate for the lead in 'Booksmart'",
"F5":"Oscar-Nominee for a role in 'Eighth Grade'"
}
```
### Data Splits
Number of instances in the various splits:
| Split | AAAC01 | AAAC02 |
| :--- | :---: | :---: |
| TRAIN | 16,000 | 16,000 |
| DEV | 4,000 | 4,000 |
| TEST | 4,000 | 4,000 |
To correctly load a specific split, define `data_files` as follows:
```python
>>> data_files = {"train": "aaac01_train.jsonl", "eval": "aaac01_dev.jsonl", "test": "aaac01_test.jsonl"}
>>> dataset = load_dataset("debatelab/aaac", data_files=data_files)
```
## Dataset Creation
### Curation Rationale
Argument analysis refers to the interpretation and logical reconstruction of argumentative texts. Its goal is to make an argument transparent, so as to understand, appreciate and (possibly) criticize it. Argument analysis is a key critical thinking skill.
Here's a first example of an informally presented argument, **Descartes' Cogito**:
> I have convinced myself that there is absolutely nothing in the world, no sky, no earth, no minds, no bodies. Does it now follow that I too do not exist? No: if I convinced myself of something then I certainly existed. But there is a deceiver of supreme power and cunning who is deliberately and constantly deceiving me. In that case I too undoubtedly exist, if he is deceiving me; and let him deceive me as much as he can, he will never bring it about that I am nothing so long as I think that I am something. So after considering everything very thoroughly, I must finally conclude that this proposition, I am, I exist, is necessarily true whenever it is put forward by me or conceived in my mind. (AT 7:25, CSM 2:16f)
And here's a second example, taken from the *Debater's Handbook*, **Pro Censorship**:
> Freedom of speech is never an absolute right but an aspiration. It ceases to be a right when it causes harm to others -- we all recognise the value of, for example, legislating against incitement to racial hatred. Therefore it is not the case that censorship is wrong in principle.
Given such texts, argument analysis aims at answering the following questions:
1. Does the text present an argument?
2. If so, how many?
3. What is the argument supposed to show (conclusion)?
4. What exactly are the premises of the argument?
* Which statements, explicit in the text, are not relevant for the argument?
* Which premises are required, but not explicitly stated?
5. Is the argument deductively valid, inductively strong, or simply fallacious?
To answer these questions, argument analysts **interpret** the text by (re-)constructing its argument in a standardized way (typically as a premise-conclusion list) and by making use of logical streamlining and formalization.
A reconstruction of **Pro Censorship** which answers the above questions is:
```argdown
(1) Freedom of speech is never an absolute right but an aspiration.
(2) Censorship is wrong in principle only if freedom of speech is an
absolute right.
--with modus tollens--
(3) It is not the case that censorship is wrong in principle
```
There are typically multiple, more or less different interpretations and logical reconstructions of an argumentative text. For instance, there exists an [extensive debate](https://plato.stanford.edu/entries/descartes-epistemology/) about how to interpret **Descartes' Cogito**, and scholars have advanced rival interpretation of the argument. An alternative reconstruction of the much simpler **Pro Censorship** might read:
```argdown
(1) Legislating against incitement to racial hatred is valuable.
(2) Legislating against incitement to racial hatred is an instance of censorship.
(3) If some instance of censorship is valuable, censorship is not wrong in
principle.
-----
(4) Censorship is not wrong in principle.
(5) Censorship is wrong in principle only if and only if freedom of speech
is an absolute right.
-----
(4) Freedom of speech is not an absolute right.
(5) Freedom of speech is an absolute right or an aspiration.
--with disjunctive syllogism--
(6) Freedom of speech is an aspiration.
```
What are the main reasons for this kind of underdetermination?
* **Incompleteness.** Many relevant parts of an argument (statements, their function in the argument, inference rules, argumentative goals) are not stated in its informal presentation. The argument analyst must infer the missing parts.
* **Additional material.** Over and above what is strictly part of the argument, informal presentations contain typically further material: relevant premises are repeated in slightly different ways, further examples are added to illustrate a point, statements are contrasted with views by opponents, etc. etc. It's argument analyst to choice which of the presented material is really part of the argument.
* **Errors.** Authors may err in the presentation of an argument, confounding, e.g., necessary and sufficient conditions in stating a premise. Following the principle of charity, benevolent argument analysts correct such errors and have to choose on of the different ways for how to do so.
* **Linguistic indeterminacy.** One and the same statement can be interpreted -- regarding its logical form -- in different ways.
* **Equivalence.** There are different natural language expressions for one and the same proposition.
AAAC datasets provide logical reconstructions of informal argumentative texts: Each record contains a source text to-be-reconstructed and further fields which describe an internally consistent interpretation of the text, notwithstanding the fact that there might be alternative interpretations of this very text.
### Construction of the Synthetic Data
Argument analysis starts with a text and reconstructs its argument (cf. [Motivation and Background](#curation-rationale)). In constructing our synthetic data, we inverse this direction: We start by sampling a complete argument, construct an informal presentation, and provide further info that describes both logical reconstruction and informal presentation. More specifically, the construction of the data involves the following steps:
1. [Generation of valid symbolic inference schemes](#step-1-generation-of-symbolic-inference-schemes)
2. [Assembling complex ("multi-hop") argument schemes from symbolic inference schemes](#step-2-assembling-complex-multi-hop-argument-schemes-from-symbolic-inference-schemes)
3. [Creation of (precise and informal) natural-language argument](#step-3-creation-of-precise-and-informal-natural-language-argument-schemes)
4. [Substitution of placeholders with domain-specific predicates and names](#step-4-substitution-of-placeholders-with-domain-specific-predicates-and-names)
5. [Creation of the argdown-snippet](#step-5-creation-of-the-argdown-snippet)
7. [Paraphrasing](#step-6-paraphrasing)
6. [Construction of a storyline for the argument source text](#step-7-construction-of-a-storyline-for-the-argument-source-text)
8. [Assembling the argument source text](#step-8-assembling-the-argument-source-text)
9. [Linking the precise reconstruction and the informal argumentative text](#step-9-linking-informal-presentation-and-formal-reconstruction)
#### Step 1: Generation of symbolic inference schemes
We construct the set of available inference schemes by systematically transforming the following 12 base schemes (6 from propositional and another 6 from predicate logic):
* modus ponens: `['Fa -> Gb', 'Fa', 'Gb']`
* chain rule: `['Fa -> Gb', 'Gb -> Hc', 'Fa -> Hc']`
* adjunction: `['Fa', 'Gb', 'Fa & Gb']`
* case analysis: `['Fa v Gb', 'Fa -> Hc', 'Gb -> Hc', 'Hc']`
* disjunctive syllogism: `['Fa v Gb', '¬Fa', 'Gb']`
* biconditional elimination: `['Fa <-> Gb', 'Fa -> Gb']`
* instantiation: `['(x): Fx -> Gx', 'Fa -> Ga']`
* hypothetical syllogism: `['(x): Fx -> Gx', '(x): Gx -> Hx', '(x): Fx -> Hx']`
* generalized biconditional elimination: `['(x): Fx <-> Gx', '(x): Fx -> Gx']`
* generalized adjunction: `['(x): Fx -> Gx', '(x): Fx -> Hx', '(x): Fx -> (Gx & Hx)']`
* generalized dilemma: `['(x): Fx -> (Gx v Hx)', '(x): Gx -> Ix', '(x): Hx -> Ix', '(x): Fx -> Ix']`
* generalized disjunctive syllogism: `['(x): Fx -> (Gx v Hx)', '(x): Fx -> ¬Gx', '(x): Fx -> Hx']`
(Regarding the propositional schemes, we allow for `a`=`b`=`c`.)
Further symbolic inference schemes are generated by applying the following transformations to each of these base schemes:
* *negation*: replace all occurrences of an atomic formula by its negation (for any number of such atomic sentences)
* *transposition*: transpose exactly one (generalized) conditional
* *dna*: simplify by applying duplex negatio affirmat
* *complex predicates*: replace all occurrences of a given atomic formula by a complex formula consisting in the conjunction or disjunction of two atomic formulas
* *de morgan*: apply de Morgan's rule once
These transformations are applied to the base schemes in the following order:
> **{base_schemes}** > negation_variants > transposition_variants > dna > **{transposition_variants}** > complex_predicates > negation_variants > dna > **{complex_predicates}** > de_morgan > dna > **{de_morgan}**
All transformations, except *dna*, are monotonic, i.e. simply add further schemes to the ones generated in the previous step. Results of bold steps are added to the list of valid inference schemes. Each inference scheme is stored with information about which transformations were used to create it. All in all, this gives us 5542 schemes.
#### Step 2: Assembling complex ("multi-hop") argument schemes from symbolic inference schemes
The complex argument *scheme*, which consists in multiple inferences, is assembled recursively by adding inferences that support premises of previously added inferences, as described by the following pseudocode:
```
argument = []
intermediary_conclusion = []
inference = randomly choose from list of all schemes
add inference to argument
for i in range(number_of_sub_arguments - 1):
target = randomly choose a premise which is not an intermediary_conclusion
inference = randomly choose a scheme whose conclusion is identical with target
add inference to argument
add target to intermediary_conclusion
return argument
```
The complex arguments we create are hence trees, with a root scheme.
Let's walk through this algorithm by means of an illustrative example and construct a symbolic argument scheme with two sub-arguments. First, we randomly choose some inference scheme (random sampling is controlled by weights that compensate for the fact that the list of schemes mainly contains, for combinatorial reasons, complex inferences), say:
```json
{
"id": "mp",
"base_scheme_group": "modus ponens",
"scheme_variant": ["complex_variant"],
"scheme": [
["${A}${a} -> (${B}${a} & ${C}${a})",
{"A": "${F}", "B": "${G}", "C": "${H}", "a": "${a}"}],
["${A}${a}", {"A": "${F}", "a": "${a}"}],
["${A}${a} & ${B}${a}", {"A": "${G}", "B": "${H}", "a": "${a}"}]
],
"predicate-placeholders": ["F", "G", "H"],
"entity-placeholders": ["a"]
}
```
Now, the target premise (= intermediary conclusion) of the next subargument is chosen, say: premise 1 of the already added root scheme. We filter the list of schemes for schemes whose conclusion structurally matches the target, i.e. has the form `${A}${a} -> (${B}${a} v ${C}${a})`. From this filtered list of suitable schemes, we randomly choose, for example
```json
{
"id": "bicelim",
"base_scheme_group": "biconditional elimination",
"scheme_variant": [complex_variant],
"scheme": [
["${A}${a} <-> (${B}${a} & ${C}${a})",
{"A": "${F}", "B": "${G}", "C": "${H}", "a": "${a}"}],
["${A}${a} -> (${B}${a} & ${C}${a})",
{"A": "${F}", "B": "${G}", "C": "${H}", "a": "${a}"}]
],
"predicate-placeholders": ["F", "G", "H"],
"entity-placeholders": []
}
```
So, we have generated this 2-step symbolic argument scheme with two premises, one intermediary and one final conclusion:
```
(1) Fa <-> Ga & Ha
--
with biconditional elimination (complex variant) from 1
--
(2) Fa -> Ga & Ha
(3) Fa
--
with modus ponens (complex variant) from 2,3
--
(4) Ga & Ha
```
General properties of the argument are now determined and can be stored in the dataset (its `domain` is randomly chosen):
```json
"steps":2, // number of inference steps
"n_premises":2,
"base_scheme_groups":[
"biconditional elimination",
"modus ponens"
],
"scheme_variants":[
"complex variant"
],
"domain_id":"consumers_personalcare",
"domain_type":"persons"
```
#### Step 3: Creation of (precise and informal) natural-language argument schemes
In step 3, the *symbolic and formal* complex argument scheme is transformed into a *natural language* argument scheme by replacing symbolic formulas (e.g., `${A}${a} v ${B}${a}`) with suitable natural language sentence schemes (such as, `${a} is a ${A}, and ${a} is a ${B}` or `${a} is a ${A} and a ${B}`). Natural language sentence schemes which translate symbolic formulas are classified according to whether they are precise, informal, or imprecise.
For each symbolic formula, there are many (partly automatically, partly manually generated) natural-language sentence scheme which render the formula in more or less precise way. Each of these natural-language "translations" of a symbolic formula is labeled according to whether it presents the logical form in a "precise", "informal", or "imprecise" way. e.g.
|type|form|
|-|-|
|symbolic|`(x): ${A}x -> ${B}x`|
|precise|`If someone is a ${A}, then they are a ${B}.`|
|informal|`Every ${A} is a ${B}.`|
|imprecise|`${A} might be a ${B}.`|
The labels "precise", "informal", "imprecise" are used to control the generation of two natural-language versions of the argument scheme, a **precise** one (for creating the argdown snippet) and an **informal** one (for creating the source text). Moreover, the natural-language "translations" are also chosen in view of the domain (see below) of the to-be-generated argument, specifically in view of whether it is quantified over persons ("everyone", "nobody") or objects ("something, nothing").
So, as a **precise** rendition of our symbolic argument scheme, we may obtain:
```
(1) If, and only if, a is a F, then a is G and a is a H.
--
with biconditional elimination (complex variant) from 1
--
(2) If a is a F, then a is a G and a is a H.
(3) a is a F.
--
with modus ponens (complex variant) from 3,2
--
(4) a is G and a is a H.
```
Likewise, an **informal** rendition may be:
```
(1) a is a F if a is both a G and a H -- and vice versa.
--
with biconditional elimination (complex variant) from 1
--
(2) a is a G and a H, provided a is a F.
(3) a is a F.
--
with modus ponens (complex variant) from 3,2
--
(4) a is both a G and a H.
```
#### Step 4: Substitution of placeholders with domain-specific predicates and names
Every argument falls within a domain. A domain provides
* a list of `subject names` (e.g., Peter, Sarah)
* a list of `object names` (e.g., New York, Lille)
* a list of `binary predicates` (e.g., [subject is an] admirer of [object])
These domains are manually created.
Replacements for the placeholders are sampled from the corresponding domain. Substitutes for entity placeholders (`a`, `b` etc.) are simply chosen from the list of `subject names`. Substitutes for predicate placeholders (`F`, `G` etc.) are constructed by combining `binary predicates` with `object names`, which yields unary predicates of the form "___ stands in some relation to some object". This combinatorial construction of unary predicates drastically increases the number of replacements available and hence the variety of generated arguments.
Assuming that we sample our argument from the domain `consumers personal care`, we may choose and construct the following substitutes for placeholders in our argument scheme:
* `F`: regular consumer of Kiss My Face soap
* `G`: regular consumer of Nag Champa soap
* `H`: occasional purchaser of Shield soap
* `a`: Orlando
#### Step 5: Creation of the argdown-snippet
From the **precise rendition** of the natural language argument scheme ([step 3](#step-3-creation-of-precise-and-informal-natural-language-argument-schemes)) and the replacements for its placeholders ([step 4](#step-4-substitution-of-placeholders-with-domain-specific-predicates-and-names)), we construct the `argdown-snippet` by simple substitution and formatting the complex argument in accordance with [argdown syntax](https://argdown.org).
This yields, for our example from above:
```argdown
(1) If, and only if, Orlando is a regular consumer of Kiss My Face soap,
then Orlando is a regular consumer of Nag Champa soap and Orlando is
a occasional purchaser of Shield soap.
--
with biconditional elimination (complex variant) from 1
--
(2) If Orlando is a regular consumer of Kiss My Face soap, then Orlando
is a regular consumer of Nag Champa soap and Orlando is a occasional
purchaser of Shield soap.
(3) Orlando is a regular consumer of Kiss My Face soap.
--
with modus ponens (complex variant) from 3,2
--
(4) Orlando is a regular consumer of Nag Champa soap and Orlando is a
occasional purchaser of Shield soap.
```
That's the `argdown_snippet`. By construction of such a synthetic argument (from formal schemes, see [step 2](#step-2-assembling-complex-multi-hop-argument-schemes-from-symbolic-inference-schemes)), we already know its conclusions and their formalization (the value of the field `explicit` will be determined later).
```json
"conclusion":[
{
"ref_reco":4,
"text":"Orlando is a regular consumer of Nag Champa
soap and Orlando is a occasional purchaser of
Shield soap.",
"explicit": TBD
}
],
"conclusion_formalized":[
{
"ref_reco":4,
"form":"(${F2}${a1} & ${F3}${a1})"
}
],
"intermediary_conclusions":[
{
"ref_reco":2,
"text":"If Orlando is a regular consumer of Kiss My
Face soap, then Orlando is a regular consumer of
Nag Champa soap and Orlando is a occasional
purchaser of Shield soap.",
"explicit": TBD
}
]
"intermediary_conclusions_formalized":[
{
"ref_reco":2,
"text":"${F1}${a1} -> (${F2}${a1} & ${F3}${a1})"
}
],
```
... and the corresponding keys (see [step 4](#step-4-substitution-of-placeholders-with-domain-specific-predicates-and-names))):
```json
"plcd_subs":{
"a1":"Orlando",
"F1":"regular consumer of Kiss My Face soap",
"F2":"regular consumer of Nag Champa soap",
"F3":"occasional purchaser of Shield soap"
}
```
#### Step 6: Paraphrasing
From the **informal rendition** of the natural language argument scheme ([step 3](#step-3-creation-of-precise-and-informal-natural-language-argument-schemes)) and the replacements for its placeholders ([step 4](#step-4-substitution-of-placeholders-with-domain-specific-predicates-and-names)), we construct an informal argument (argument tree) by substitution.
The statements (premises, conclusions) of the informal argument are individually paraphrased in two steps
1. rule-based and in a domain-specific way,
2. automatically by means of a specifically fine-tuned T5 model.
Each domain (see [step 4](#step-4-substitution-of-placeholders-with-domain-specific-predicates-and-names)) provides rules for substituting noun constructs ("is a supporter of X", "is a product made of X") with verb constructs ("supports x", "contains X"). These rules are applied whenever possible.
Next, each sentence is -- with a probability specified by parameter `lm_paraphrasing` -- replaced with an automatically generated paraphrase, using a [T5 model fine-tuned on the Google PAWS dataset](https://huggingface.co/Vamsi/T5_Paraphrase_Paws) and filtering for paraphrases with acceptable _cola_ and sufficiently high _STSB_ value (both as predicted by T5).
| |AAAC01|AAAC02|
|-|-|-|
|`lm_paraphrasing`|0.2|0.|
#### Step 7: Construction of a storyline for the argument source text
The storyline determines in which order the premises, intermediary conclusions and final conclusions are to be presented in the text paragraph to-be-constructed (`argument-source`). The storyline is constructed from the paraphrased informal complex argument (see [step 6](#step-6-paraphrasing))).
Before determining the order of presentation (storyline), the informal argument tree is pre-processed to account for:
* implicit premises,
* implicit intermediary conclusions, and
* implicit final conclusion,
which is documented in the dataset record as
```json
"presentation_parameters":{
"resolve_steps":[1],
"implicit_conclusion":false,
"implicit_premise":true,
"...":"..."
}
```
In order to make an intermediary conclusion *C* implicit, the inference to *C* is "resolved" by re-assigning all premisses *from* which *C* is directly inferred *to* the inference to the (final or intermediary) conclusion which *C* supports.
Original tree:
```
P1 ... Pn
—————————
C Q1 ... Qn
—————————————
C'
```
Tree with resolved inference and implicit intermediary conclusion:
```
P1 ... Pn Q1 ... Qn
———————————————————
C'
```
The original argument tree in our example reads:
```
(1)
———
(2) (3)
———————
(4)
```
This might be pre-processed (by resolving the first inference step and dropping the first premise) to:
```
(3)
———
(4)
```
Given such a pre-processed argument tree, a storyline, which determines the order of presentation, can be constructed by specifying the direction of presentation and a starting point. The **direction** is either
* forward (premise AND ... AND premise THEREFORE conclusion)
* backward (conclusion SINCE premise AND ... AND premise)
Any conclusion in the pre-processed argument tree may serve as starting point. The storyline is now constructed recursively, as illustrated in Figure~1. Integer labels of the nodes represent the order of presentation, i.e. the storyline. (Note that the starting point is not necessarily the statement which is presented first according to the storyline.)
So as to introduce redundancy, the storyline may be post-processed by repeating a premiss that has been stated previously. The likelihood that a single premise is repeated is controlled by the presentation parameters:
```json
"presentation_parameters":{
"redundancy_frequency":0.1,
}
```
Moreover, **distractors**, i.e. arbitrary statements sampled from the argument's very domain, may be inserted in the storyline.
#### Step 8: Assembling the argument source text
The `argument-source` is constructed by concatenating the statements of the informal argument ([step 6](#step-6-paraphrasing)) according to the order of the storyline ([step 7](#step-7-construction-of-a-storyline-for-the-argument-source-text)). In principle, each statement is prepended by a conjunction. There are four types of conjunction:
* THEREFORE: left-to-right inference
* SINCE: right-to-left inference
* AND: joins premises with similar inferential role
* MOREOVER: catch all conjunction
Each statement is assigned a specific conjunction type by the storyline.
For every conjunction type, we provide multiple natural-language terms which may figure as conjunctions when concatenating the statements, e.g. "So, necessarily,", "So", "Thus,", "It follows that", "Therefore,", "Consequently,", "Hence,", "In consequence,", "All this entails that", "From this follows that", "We may conclude that" for THEREFORE. The parameter
```json
"presentation_parameters":{
"drop_conj_frequency":0.1,
"...":"..."
}
```
determines the probability that a conjunction is omitted and a statement is concatenated without prepending a conjunction.
With the parameters given above we obtain the following `argument_source` for our example:
> Orlando is a regular consumer of Nag Champa soap and Orlando is a occasional purchaser of Shield soap, since Orlando is a regular consumer of Kiss My Face soap.
#### Step 9: Linking informal presentation and formal reconstruction
We can identify all statements _in the informal presentation_ (`argument_source`), categorize them according to their argumentative function GIVEN the logical reconstruction and link them to the corresponding statements in the `argdown_snippet`. We distinguish `reason_statement` (AKA REASONS, correspond to premises in the reconstruction) and `conclusion_statement` (AKA CONJECTURES, correspond to conclusion and intermediary conclusion in the reconstruction):
```json
"reason_statements":[ // aka reasons
{
"text":"Orlando is a regular consumer of Kiss My Face soap",
"starts_at":109,
"ref_reco":3
}
],
"conclusion_statements":[ // aka conjectures
{
"text":"Orlando is a regular consumer of Nag Champa soap and
Orlando is a occasional purchaser of Shield soap",
"starts_at":0,
"ref_reco":4
}
]
```
Moreover, we are now able to classify all premises in the formal reconstruction (`argdown_snippet`) according to whether they are implicit or explicit given the informal presentation:
```json
"premises":[
{
"ref_reco":1,
"text":"If, and only if, Orlando is a regular consumer of Kiss
My Face soap, then Orlando is a regular consumer of Nag
Champa soap and Orlando is a occasional purchaser of
Shield soap.",
"explicit":False
},
{
"ref_reco":3,
"text":"Orlando is a regular consumer of Kiss My Face soap. ",
"explicit":True
}
],
"premises_formalized":[
{
"ref_reco":1,
"form":"${F1}${a1} <-> (${F2}${a1} & ${F3}${a1})"
},
{
"ref_reco":3,
"form":"${F1}${a1}"
}
]
```
#### Initial Data Collection and Normalization
N.A.
#### Who are the source language producers?
N.A.
### Annotations
#### Annotation process
N.A.
#### Who are the annotators?
N.A.
### Personal and Sensitive Information
N.A.
## Considerations for Using the Data
### Social Impact of Dataset
None
### Discussion of Biases
None
### Other Known Limitations
See [Betz and Richardson 2021](https://arxiv.org/abs/2110.01509).
## Additional Information
### Dataset Curators
Gregor Betz, Kyle Richardson
### Licensing Information
Creative Commons cc-by-sa-4.0
### Citation Information
```
@misc{betz2021deepa2,
title={DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models},
author={Gregor Betz and Kyle Richardson},
year={2021},
eprint={2110.01509},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
<!--Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.-->
|
debatelab | null | null | null | false | 334 | false | debatelab/deepa2 | 2022-11-01T08:54:18.000Z | null | false | c82f0d25f7495aa2f25db7fca1febd64f5b4869d | [] | [
"arxiv:2110.01509",
"language_creators:other",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:unknown",
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_ids:text-simplification",
"task_ids:parsing",
"tags:argument-mining",
"tags:summar... | https://huggingface.co/datasets/debatelab/deepa2/resolve/main/README.md | ---
annotations_creators: []
language_creators:
- other
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-retrieval
- text-generation
task_ids:
- text-simplification
- parsing
pretty_name: deepa2
tags:
- argument-mining
- summarization
- conditional-text-generation
- structure-prediction
---
# `deepa2` Datasets Collection
## Table of Contents
- [`deepa2` Datasets Collection](#deepa2-datasets-collection)
- [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)
- [Sub-Datasets](#sub-datasets)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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:** [blog post](https://debatelab.github.io/journal/deepa2.html)
- **Repository:** [github](https://github.com/debatelab/deepa2)
- **Paper:** [arxiv](https://arxiv.org/abs/2110.01509)
- **Point of Contact:** [Gregor Betz](gregor.betz@kit.edu)
### Dataset Summary
This is a growing, curated collection of `deepa2` datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises:
* datasets that are built from existing NLP datasets by means of the [`deepa2 bake`](https://github.com/debatelab/deepa2) tool.
* original `deepa2` datasets specifically created for this collection.
The tool [`deepa2 serve`](https://github.com/debatelab/deepa2#integrating-deepa2-into-your-training-pipeline) may be used to render the data in this collection as text2text examples.
### Supported Tasks and Leaderboards
For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
- `conditional-text-generation`: The dataset can be used to train models to generate a fully reconstruction of an argument from a source text, making, e.g., its implicit assumptions explicit.
- `structure-prediction`: The dataset can be used to train models to formalize sentences.
- `text-retrieval`: The dataset can be used to train models to extract reason statements and conjectures from a given source text.
### Languages
English. Will be extended to cover other languages in the futures.
## Dataset Structure
### Sub-Datasets
This collection contains the following `deepa2` datasets:
* `esnli`: created from e-SNLI with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/esnli.md).
* `enbank` (`task_1`, `task_2`): created from Entailment Bank with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/enbank.md).
* `argq`: created from IBM-ArgQ with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/argq.md).
* `argkp`: created from IBM-KPA with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/argkp.md).
* `aifdb` (`moral-maze`, `us2016`, `vacc-itc`): created from AIFdb with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/aifdb.md).
* `aaac` (`aaac01` and `aaac02`): original, machine-generated contribution; based on an an improved and extended algorithm that backs https://huggingface.co/datasets/debatelab/aaac.
### Data Instances
see: https://github.com/debatelab/deepa2/tree/main/docs
### Data Fields
see: https://github.com/debatelab/deepa2/tree/main/docs
|feature|esnli|enbank|aifdb|aaac|argq|argkp|
|--|--|--|--|--|--|--|
| `source_text` | x | x | x | x | x | x |
| `title` | | x | | x | | |
| `gist` | x | x | | x | | x |
| `source_paraphrase` | x | x | x | x | | |
| `context` | | x | | x | | x |
| `reasons` | x | x | x | x | x | |
| `conjectures` | x | x | x | x | x | |
| `argdown_reconstruction` | x | x | | x | | x |
| `erroneous_argdown` | x | | | x | | |
| `premises` | x | x | | x | | x |
| `intermediary_conclusion` | | | | x | | |
| `conclusion` | x | x | | x | | x |
| `premises_formalized` | x | | | x | | x |
| `intermediary_conclusion_formalized` | | | | x | | |
| `conclusion_formalized` | x | | | x | | x |
| `predicate_placeholders` | | | | x | | |
| `entity_placeholders` | | | | x | | |
| `misc_placeholders` | x | | | x | | x |
| `plchd_substitutions` | x | | | x | | x |
### Data Splits
Each sub-dataset contains three splits: `train`, `validation`, and `test`.
## Dataset Creation
### Curation Rationale
Many NLP datasets focus on tasks that are relevant for logical analysis and argument reconstruction. This collection is the attempt to unify these resources in a common framework.
### Source Data
See: [Sub-Datasets](#sub-datasets)
## Additional Information
### Dataset Curators
Gregor Betz, KIT; Kyle Richardson, Allen AI
### Licensing Information
We re-distribute the the imported sub-datasets under their original license:
|Sub-dataset|License|
|--|--|
|esnli|MIT|
|aifdb|free for academic use ([TOU](https://arg-tech.org/index.php/research/argument-corpora/))|
|enbank|CC BY 4.0|
|aaac|CC BY 4.0|
|argq|CC BY SA 4.0|
|argkp|Apache|
### Citation Information
```
@article{betz2021deepa2,
title={DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models},
author={Gregor Betz and Kyle Richardson},
year={2021},
eprint={2110.01509},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--If the dataset has a [DOI](https://www.doi.org/), please provide it here.-->
|
deepset | null | @misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | We take GermanQuAD as a starting point and add hard negatives from a dump of the full German Wikipedia following the approach of the DPR authors (Karpukhin et al., 2020). The format of the dataset also resembles the one of DPR. GermanDPR comprises 9275 question/answer pairs in the training set and 1025 pairs in the test set. For each pair, there are one positive context and three hard negative contexts. | false | 393 | false | deepset/germandpr | 2022-10-25T09:07:41.000Z | null | false | 32259c8039d961cd370ed45ed148d296476b2dbc | [] | [
"arxiv:2104.12741",
"language:de",
"multilinguality:monolingual",
"source_datasets:original",
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"thumbnail:https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/re... | https://huggingface.co/datasets/deepset/germandpr/resolve/main/README.md | ---
language:
- de
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
---
# Dataset Card for germandpr
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://deepset.ai/germanquad
- **Repository:** https://github.com/deepset-ai/haystack
- **Paper:** https://arxiv.org/abs/2104.12741
### Dataset Summary
We take GermanQuAD as a starting point and add hard negatives from a dump of the full German Wikipedia following the approach of the DPR authors (Karpukhin et al., 2020). The format of the dataset also resembles the one of DPR. GermanDPR comprises 9275 question/answerpairs in the training set and 1025 pairs in the test set. For eachpair, there are one positive context and three hard negative contexts.
### Supported Tasks and Leaderboards
- `open-domain-qa`, `text-retrieval`: This dataset is intended to be used for `open-domain-qa` and text retrieval tasks.
### Languages
The sentences in the dataset are in German (de).
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
"question": "Wie viele christlichen Menschen in Deutschland glauben an einen Gott?",
"answers": [
"75 % der befragten Katholiken sowie 67 % der Protestanten glaubten an einen Gott (2005: 85 % und 79 %)"
],
"positive_ctxs": [
{
"title": "Gott",
"text": "Gott\
=== Demografie ===
Eine Zusammenfassung von Umfrageergebnissen aus verschiedenen Staaten ergab im Jahr 2007, dass es weltweit zwischen 505 und 749 Millionen Atheisten und Agnostiker gibt. Laut der Encyclopædia Britannica gab es 2009 weltweit 640 Mio. Nichtreligiöse und Agnostiker (9,4 %), und weitere 139 Mio. Atheisten (2,0 %), hauptsächlich in der Volksrepublik China.\\\\\\\\
Bei einer Eurobarometer-Umfrage im Jahr 2005 wurde festgestellt, dass 52 % der damaligen EU-Bevölkerung glaubt, dass es einen Gott gibt. Eine vagere Frage nach dem Glauben an „eine andere spirituelle Kraft oder Lebenskraft“ wurde von weiteren 27 % positiv beantwortet. Bezüglich der Gottgläubigkeit bestanden große Unterschiede zwischen den einzelnen europäischen Staaten. Die Umfrage ergab, dass der Glaube an Gott in Staaten mit starkem kirchlichen Einfluss am stärksten verbreitet ist, dass mehr Frauen (58 %) als Männer (45 %) an einen Gott glauben und dass der Gottglaube mit höherem Alter, geringerer Bildung und politisch rechtsgerichteten Ansichten korreliert.\\\\\\\\
Laut einer Befragung von 1003 Personen in Deutschland im März 2019 glauben 55 % an einen Gott; 2005 waren es 66 % gewesen. 75 % der befragten Katholiken sowie 67 % der Protestanten glaubten an einen Gott (2005: 85 % und 79 %). Unter Konfessionslosen ging die Glaubensquote von 28 auf 20 % zurück. Unter Frauen (60 %) war der Glauben 2019 stärker ausgeprägt als unter Männern (50 %), in Westdeutschland (63 %) weiter verbreitet als in Ostdeutschland (26 %).",
"passage_id": ""
}
],
"negative_ctxs": [],
"hard_negative_ctxs": [
{
"title": "Christentum",
"text": "Christentum\
\
=== Ursprung und Einflüsse ===\
Die ersten Christen waren Juden, die zum Glauben an Jesus Christus fanden. In ihm erkannten sie den bereits durch die biblische Prophetie verheißenen Messias (hebräisch: ''maschiach'', griechisch: ''Christos'', latinisiert ''Christus''), auf dessen Kommen die Juden bis heute warten. Die Urchristen übernahmen aus der jüdischen Tradition sämtliche heiligen Schriften (den Tanach), wie auch den Glauben an einen Messias oder Christus (''christos'': Gesalbter). Von den Juden übernommen wurden die Art der Gottesverehrung, das Gebet der Psalmen u. v. a. m. Eine weitere Gemeinsamkeit mit dem Judentum besteht in der Anbetung desselben Schöpfergottes. Jedoch sehen fast alle Christen Gott als ''einen'' dreieinigen Gott an: den Vater, den Sohn (Christus) und den Heiligen Geist. Darüber, wie der dreieinige Gott konkret gedacht werden kann, gibt es unter den christlichen Konfessionen und Gruppierungen unterschiedliche Auffassungen bis hin zur Ablehnung der Dreieinigkeit Gottes (Antitrinitarier). Der Glaube an Jesus Christus führte zu Spannungen und schließlich zur Trennung zwischen Juden, die diesen Glauben annahmen, und Juden, die dies nicht taten, da diese es unter anderem ablehnten, einen Menschen anzubeten, denn sie sahen in Jesus Christus nicht den verheißenen Messias und erst recht nicht den Sohn Gottes. Die heutige Zeitrechnung wird von der Geburt Christi aus gezählt. Anno Domini (A. D.) bedeutet „im Jahr des Herrn“.",
"passage_id": ""
},
{
"title": "Noachidische_Gebote",
"text": "Noachidische_Gebote\
\
=== Die kommende Welt ===\
Der Glaube an eine ''Kommende Welt'' (Olam Haba) bzw. an eine ''Welt des ewigen Lebens'' ist ein Grundprinzip des Judentums. Dieser jüdische Glaube ist von dem christlichen Glauben an das ''Ewige Leben'' fundamental unterschieden. Die jüdische Lehre spricht niemandem das Heil dieser kommenden Welt ab, droht aber auch nicht mit Höllenstrafen im Jenseits. Juden glauben schlicht, dass allen Menschen ein Anteil der kommenden Welt zuteilwerden kann. Es gibt zwar viele Vorstellungen der kommenden Welt, aber keine kanonische Festlegung ihrer Beschaffenheit; d. h., das Judentum kennt keine eindeutige Antwort darauf, was nach dem Tod mit uns geschieht. Die Frage nach dem Leben nach dem Tod wird auch als weniger wesentlich angesehen, als Fragen, die das Leben des Menschen auf Erden und in der Gesellschaft betreffen.\
Der jüdische Glaube an eine kommende Welt bedeutet nicht, dass Menschen, die nie von der Tora gehört haben, böse oder sonst minderwertige Menschen sind. Das Judentum lehrt den Glauben, dass alle Menschen mit Gott verbunden sind. Es gibt im Judentum daher keinen Grund, zu missionieren. Das Judentum lehrt auch, dass alle Menschen sich darin gleichen, dass sie weder prinzipiell gut noch böse sind, sondern eine Neigung zum Guten wie zum Bösen haben. Während des irdischen Lebens sollte sich der Mensch immer wieder für das Gute entscheiden.",
"passage_id": ""
},
{
"title": "Figuren_und_Schauplätze_der_Scheibenwelt-Romane",
"text": "Figuren_und_Schauplätze_der_Scheibenwelt-Romane\
\
=== Herkunft ===\
Es gibt unzählig viele Götter auf der Scheibenwelt, die so genannten „geringen Götter“, die überall sind, aber keine Macht haben. Erst wenn sie durch irgendein Ereignis Gläubige gewinnen, werden sie mächtiger. Je mehr Glauben, desto mehr Macht. Dabei nehmen sie die Gestalt an, die die Menschen ihnen geben (zum Beispiel Offler). Wenn ein Gott mächtig genug ist, erhält er Einlass in den Cori Celesti, den Berg der Götter, der sich in der Mitte der Scheibenwelt erhebt. Da Menschen wankelmütig sind, kann es auch geschehen, dass sie den Glauben verlieren und einen Gott damit entmachten (s. „Einfach Göttlich“).",
"passage_id": ""
}
]
},
```
### Data Fields
- `positive_ctxs`: a dictionary feature containing:
- `title`: a `string` feature.
- `text`: a `string` feature.
- `passage_id`: a `string` feature.
- `negative_ctxs`: a dictionary feature containing:
- `title`: a `string` feature.
- `text`: a `string` feature.
- `passage_id`: a `string` feature.
- `hard_negative_ctxs`: a dictionary feature containing:
- `title`: a `string` feature.
- `text`: a `string` feature.
- `passage_id`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a list feature containing:
- a `string` feature.
### Data Splits
The dataset is split into a training set and a test set.
The final GermanDPR dataset comprises 9275
question/answer pairs in the training set and 1025
pairs in the test set. For each pair, there are one
positive context and three hard negative contexts.
| |questions|answers|positive contexts|hard negative contexts|
|------|--------:|------:|----------------:|---------------------:|
|train|9275| 9275|9275|27825|
|test|1025| 1025|1025|3075|
## Additional Information
### Dataset Curators
The dataset was initially created by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at deepset.ai
### Citation Information
```
@misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
deepset | null | @misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set.
The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects. We combine the strengths of SQuAD, such as high out-of-domain performance, with self-sufficient questions that contain all relevant information for open-domain QA as in the NaturalQuestions dataset. Our training and test datasets do not overlap like other popular datasets and include complex questions that cannot be answered with a single entity or only a few words. | false | 354 | false | deepset/germanquad | 2022-08-04T10:20:23.000Z | null | false | 0bf850d12abd098da61a0c6793729db0ad994446 | [] | [
"arxiv:2104.12741",
"thumbnail:https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg",
"language:de",
"multilinguality:monolingual",
"source_datasets:original",
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:extrac... | https://huggingface.co/datasets/deepset/germanquad/resolve/main/README.md | ---
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
language:
- de
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
- open-domain-qa
train-eval-index:
- config: plain_text
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: test
col_mapping:
context: context
question: question
answers.text: answers.text
answers.answer_start: answers.answer_start
---
# Dataset Card for germanquad
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://deepset.ai/germanquad
- **Repository:** https://github.com/deepset-ai/haystack
- **Paper:** https://arxiv.org/abs/2104.12741
### Dataset Summary
In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set.
The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects. We combine the strengths of SQuAD, such as high out-of-domain performance, with self-sufficient questions that contain all relevant information for open-domain QA as in the NaturalQuestions dataset. Our training and test datasets do not overlap like other popular datasets and include complex questions that cannot be answered with a single entity or only a few words.
### Supported Tasks and Leaderboards
- `extractive-qa`, `closed-domain-qa`, `open-domain-qa`, `text-retrieval`: This dataset is intended to be used for `open-domain-qa`, but can also be used for information retrieval tasks.
### Languages
The sentences in the dataset are in German (de).
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
"paragraphs": [
{
"qas": [
{
"question": "Von welchem Gesetzt stammt das Amerikanische ab? ",
"id": 51870,
"answers": [
{
"answer_id": 53778,
"document_id": 43958,
"question_id": 51870,
"text": "britischen Common Laws",
"answer_start": 146,
"answer_category": "SHORT"
}
],
"is_impossible": false
}
],
"context": "Recht_der_Vereinigten_Staaten\
\
=== Amerikanisches Common Law ===\
Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des britischen Common Laws sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, in dem sich das amerikanische Recht unabhängig vom Britischen entwickelt hat. Entsprechend schauen die Gerichte in den Vereinigten Staaten bei der Analyse von eventuell zutreffenden britischen Rechtsprinzipien im Common Law gewöhnlich nur bis ins frühe 19. Jahrhundert.\
Während es in den Commonwealth-Staaten üblich ist, dass Gerichte sich Entscheidungen und Prinzipien aus anderen Commonwealth-Staaten importieren, ist das in der amerikanischen Rechtsprechung selten. Ausnahmen bestehen hier nur, wenn sich überhaupt keine relevanten amerikanischen Fälle finden lassen, die Fakten nahezu identisch sind und die Begründung außerordentlich überzeugend ist. Frühe amerikanische Entscheidungen zitierten oft britische Fälle, solche Zitate verschwanden aber während des 19. Jahrhunderts, als die Gerichte eindeutig amerikanische Lösungen zu lokalen Konflikten fanden. In der aktuellen Rechtsprechung beziehen sich fast alle Zitate auf amerikanische Fälle.\
Einige Anhänger des Originalismus und der strikten Gesetzestextauslegung (''strict constructionism''), wie zum Beispiel der verstorbene Bundesrichter am Obersten Gerichtshof, Antonin Scalia, vertreten die Meinung, dass amerikanische Gerichte ''nie'' ausländische Fälle überprüfen sollten, die nach dem Unabhängigkeitskrieg entschieden wurden, unabhängig davon, ob die Argumentation überzeugend ist oder nicht. Die einzige Ausnahme wird hier in Fällen gesehen, die durch die Vereinigten Staaten ratifizierte völkerrechtliche Verträge betreffen. Andere Richter, wie zum Beispiel Anthony Kennedy und Stephen Breyer vertreten eine andere Ansicht und benutzen ausländische Rechtsprechung, sofern ihre Argumentation für sie überzeugend, nützlich oder hilfreich ist.",
"document_id": 43958
}
]
},
```
### Data Fields
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
The dataset is split into a one-way annotated training set and a three-way annotated test set of German Wikipedia passages (paragraphs). Each passage is
from a different article.
| |passages|questions|answers|
|----------|----:|---------:|---------:|
|train|2540| 11518|11518|
|test|474| 2204|6536|
## Additional Information
### Dataset Curators
The dataset was initially created by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at deepset.ai
### Citation Information
```
@misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
dennlinger | null | null | null | false | 564 | false | dennlinger/klexikon | 2022-10-25T15:03:56.000Z | klexikon | false | a24a4e46e38e652b9ac7a43c53c1f90eead22eea | [] | [
"arxiv:2201.07198",
"annotations_creators:found",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"language:de",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categor... | https://huggingface.co/datasets/dennlinger/klexikon/resolve/main/README.md | ---
annotations_creators:
- found
- expert-generated
language_creators:
- found
- machine-generated
language:
- de
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
task_ids:
- text-simplification
paperswithcode_id: klexikon
pretty_name: Klexikon
tags:
- conditional-text-generation
- simplification
- document-level
---
# Dataset Card for the Klexikon Dataset
## Table of Contents
- [Version History](#version-history)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Version History
- **v0.3** (2022-09-01): Removing some five samples from the dataset due to duplication conflicts with other samples.
- **v0.2** (2022-02-28): Updated the files to no longer contain empty sections and removing otherwise empty lines at the end of files. Also removing lines with some sort of coordinate.
- **v0.1** (2022-01-19): Initial data release on Huggingface datasets.
## Dataset Description
- **Homepage:** [N/A]
- **Repository:** [Klexikon repository](https://github.com/dennlinger/klexikon)
- **Paper:** [Klexikon: A German Dataset for Joint Summarization and Simplification](https://arxiv.org/abs/2201.07198)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Dennis Aumiller](mailto:dennis.aumiller@gmail.com)
### Dataset Summary
The Klexikon dataset is a German resource of document-aligned texts between German Wikipedia and the children's lexicon "Klexikon". The dataset was created for the purpose of joint text simplification and summarization, and contains almost 2900 aligned article pairs.
Notably, the children's articles use a simpler language than the original Wikipedia articles; this is in addition to a clear length discrepancy between the source (Wikipedia) and target (Klexikon) domain.
### Supported Tasks and Leaderboards
- `summarization`: The dataset can be used to train a model for summarization. In particular, it poses a harder challenge than some of the commonly used datasets (CNN/DailyMail), which tend to suffer from positional biases in the source text. This makes it very easy to generate high (ROUGE) scoring solutions, by simply taking the leading 3 sentences. Our dataset provides a more challenging extraction task, combined with the additional difficulty of finding lexically appropriate simplifications.
- `simplification`: While not currently supported by the HF task board, text simplification is concerned with the appropriate representation of a text for disadvantaged readers (e.g., children, language learners, dyslexic,...).
For scoring, we ran preliminary experiments based on [ROUGE](https://huggingface.co/metrics/rouge), however, we want to cautiously point out that ROUGE is incapable of accurately depicting simplification appropriateness.
We combined this with looking at Flesch readability scores, as implemented by [textstat](https://github.com/shivam5992/textstat).
Note that simplification metrics such as [SARI](https://huggingface.co/metrics/sari) are not applicable here, since they require sentence alignments, which we do not provide.
### Languages
The associated BCP-47 code is `de-DE`.
The text of the articles is in German. Klexikon articles are further undergoing a simple form of peer-review before publication, and aim to simplify language for 8-13 year old children. This means that the general expected text difficulty for Klexikon articles is lower than Wikipedia's entries.
## Dataset Structure
### Data Instances
One datapoint represents the Wikipedia text (`wiki_text`), as well as the Klexikon text (`klexikon_text`).
Sentences are separated by newlines for both datasets, and section headings are indicated by leading `==` (or `===` for subheadings, `====` for sub-subheading, etc.).
Further, it includes the `wiki_url` and `klexikon_url`, pointing to the respective source texts. Note that the original articles were extracted in April 2021, so re-crawling the texts yourself will likely change some content.
Lastly, we include a unique identifier `u_id` as well as the page title `title` of the Klexikon page.
Sample (abridged texts for clarity):
```
{
"u_id": 0,
"title": "ABBA",
"wiki_url": "https://de.wikipedia.org/wiki/ABBA",
"klexikon_url": "https://klexikon.zum.de/wiki/ABBA",
"wiki_sentences": [
"ABBA ist eine schwedische Popgruppe, die aus den damaligen Paaren Agnetha Fältskog und Björn Ulvaeus sowie Benny Andersson und Anni-Frid Lyngstad besteht und sich 1972 in Stockholm formierte.",
"Sie gehört mit rund 400 Millionen verkauften Tonträgern zu den erfolgreichsten Bands der Musikgeschichte.",
"Bis in die 1970er Jahre hatte es keine andere Band aus Schweden oder Skandinavien gegeben, der vergleichbare Erfolge gelungen waren.",
"Trotz amerikanischer und britischer Dominanz im Musikgeschäft gelang der Band ein internationaler Durchbruch.",
"Sie hat die Geschichte der Popmusik mitgeprägt.",
"Zu ihren bekanntesten Songs zählen Mamma Mia, Dancing Queen und The Winner Takes It All.",
"1982 beendeten die Gruppenmitglieder aufgrund privater Differenzen ihre musikalische Zusammenarbeit.",
"Seit 2016 arbeiten die vier Musiker wieder zusammen an neuer Musik, die 2021 erscheinen soll.",
],
"klexikon_sentences": [
"ABBA war eine Musikgruppe aus Schweden.",
"Ihre Musikrichtung war die Popmusik.",
"Der Name entstand aus den Anfangsbuchstaben der Vornamen der Mitglieder, Agnetha, Björn, Benny und Anni-Frid.",
"Benny Andersson und Björn Ulvaeus, die beiden Männer, schrieben die Lieder und spielten Klavier und Gitarre.",
"Anni-Frid Lyngstad und Agnetha Fältskog sangen."
]
},
```
### Data Fields
* `u_id` (`int`): A unique identifier for each document pair in the dataset. 0-2349 are reserved for training data, 2350-2623 for testing, and 2364-2897 for validation.
* `title` (`str`): Title of the Klexikon page for this sample.
* `wiki_url` (`str`): URL of the associated Wikipedia article. Notably, this is non-trivial, since we potentially have disambiguated pages, where the Wikipedia title is not exactly the same as the Klexikon one.
* `klexikon_url` (`str`): URL of the Klexikon article.
* `wiki_text` (`List[str]`): List of sentences of the Wikipedia article. We prepare a pre-split document with spacy's sentence splitting (model: `de_core_news_md`). Additionally, please note that we do not include page contents outside of `<p>` tags, which excludes lists, captions and images.
* `klexikon_text` (`List[str]`): List of sentences of the Klexikon article. We apply the same processing as for the Wikipedia texts.
### Data Splits
We provide a stratified split of the dataset, based on the length of the respective Wiki article/Klexikon article pair (according to number of sentences).
The x-axis represents the length of the Wikipedia article, and the y-axis the length of the Klexikon article.
We segment the coordinate systems into rectangles of shape `(100, 10)`, and randomly sample a split of 80/10/10 for training/validation/test from each rectangle to ensure stratification. In case of rectangles with less than 10 entries, we put all samples into training.
The final splits have the following size:
* 2350 samples for training
* 274 samples for validation
* 274 samples for testing
## Dataset Creation
### Curation Rationale
As previously described, the Klexikon resource was created as an attempt to bridge the two fields of text summarization and text simplification. Previous datasets suffer from either one or more of the following shortcomings:
* They primarily focus on input/output pairs of similar lengths, which does not reflect longer-form texts.
* Data exists primarily for English, and other languages are notoriously understudied.
* Alignments exist for sentence-level, but not document-level.
This dataset serves as a starting point to investigate the feasibility of end-to-end simplification systems for longer input documents.
### Source Data
#### Initial Data Collection and Normalization
Data was collected from [Klexikon](klexikon.zum.de), and afterwards aligned with corresponding texts from [German Wikipedia](de.wikipedia.org).
Specifically, the collection process was performed in April 2021, and 3145 articles could be extracted from Klexikon back then. Afterwards, we semi-automatically align the articles with Wikipedia, by looking up articles with the same title.
For articles that do not exactly match, we manually review their content, and decide to match to an appropriate substitute if the content can be matched by at least 66% of the Klexikon paragraphs.
Similarly, we proceed to manually review disambiguation pages on Wikipedia.
We extract only full-text content, excluding figures, captions, and list elements from the final text corpus, and only retain articles for which the respective Wikipedia document consists of at least 15 paragraphs after pre-processing.
#### Who are the source language producers?
The language producers are contributors to Klexikon and Wikipedia. No demographic information was available from the data sources.
### Annotations
#### Annotation process
Annotations were performed by manually reviewing the URLs of the ambiguous article pairs. No annotation platforms or existing tools were used in the process.
Otherwise, articles were matched based on the exact title.
#### Who are the annotators?
The manually aligned articles were reviewed by the dataset author (Dennis Aumiller).
### Personal and Sensitive Information
Since Klexikon and Wikipedia are public encyclopedias, no further personal or sensitive information is included. We did not investigate to what extent information about public figures is included in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
Accessibility on the web is still a big issue, particularly for disadvantaged readers.
This dataset has the potential to strengthen text simplification systems, which can improve the situation.
In terms of language coverage, this dataset also has a beneficial impact on the availability of German data.
Potential negative biases include the problems of automatically aligned articles. The alignments may never be 100% perfect, and can therefore cause mis-aligned articles (or associations), despite the best of our intentions.
### Discussion of Biases
We have not tested whether any particular bias towards a specific article *type* (i.e., "person", "city", etc.) exists.
Similarly, we attempted to present an unbiased (stratified) split for validation and test set, but given that we only cover around 2900 articles, it is possible that these articles represent a particular focal lense on the overall distribution of lexical content.
### Other Known Limitations
Since the articles were written independently of each other, it is not guaranteed that there exists an exact coverage of each sentence in the simplified article, which could also stem from the fact that sometimes Wikipedia pages have separate article pages for aspects (e.g., the city of "Aarhus" has a separate page for its art museum (ARoS). However, Klexikon lists content and description for ARoS on the page of the city itself.
## Additional Information
### Dataset Curators
The dataset was curated only by the author of this dataset, Dennis Aumiller.
### Licensing Information
Klexikon and Wikipedia make their textual contents available under the CC BY-SA license, which will be inherited for this dataset.
### Citation Information
If you use our dataset or associated code, please cite our paper:
```
@inproceedings{aumiller-gertz-2022-klexikon,
title = "Klexikon: A {G}erman Dataset for Joint Summarization and Simplification",
author = "Aumiller, Dennis and
Gertz, Michael",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.288",
pages = "2693--2701"
}
```
|
dfgvhxfgv | null | null | null | false | 166 | false | dfgvhxfgv/fghghj | 2021-05-01T13:05:41.000Z | null | false | aaf7320012e1c0f34ed6792b11d23009d5d8df9f | [] | [] | https://huggingface.co/datasets/dfgvhxfgv/fghghj/resolve/main/README.md | |
DFKI-SLT | null | @inproceedings{ding2021few,
title={Few-NERD: A Few-Shot Named Entity Recognition Dataset},
author={Ding, Ning and Xu, Guangwei and Chen, Yulin, and Wang, Xiaobin and Han, Xu and Xie,
Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan},
booktitle={ACL-IJCNLP},
year={2021}
} | Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset,
which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities
and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the
other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER). | false | 1,009 | false | DFKI-SLT/few-nerd | 2022-10-24T06:32:21.000Z | few-nerd | false | dbf9dd35a495b0fc829c5bb485754dd1d2b3afd4 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"task_categories:other",
"task_ids:named-entity-recognition",
"tags:structure-prediction"
] | https://huggingface.co/datasets/DFKI-SLT/few-nerd/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- other
task_ids:
- named-entity-recognition
paperswithcode_id: few-nerd
pretty_name: Few-NERD
tags:
- structure-prediction
---
# Dataset Card for "Few-NERD"
## 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://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
- **Repository:** [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD)
- **Paper:** [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/)
- **Point of Contact:** See [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
### Dataset Summary
This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/.
Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)).
NER tags use the `IO` tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered.
For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/.
### Supported Tasks and Leaderboards
- **Tasks:** Named Entity Recognition, Few-shot NER
- **Leaderboards:**
- https://ningding97.github.io/fewnerd/
- named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup
- other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra
- other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter
### Languages
English
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:**
- `super`: 14.6 MB
- `intra`: 11.4 MB
- `inter`: 11.5 MB
- **Size of the generated dataset:**
- `super`: 116.9 MB
- `intra`: 106.2 MB
- `inter`: 106.2 MB
- **Total amount of disk used:** 366.8 MB
An example of 'train' looks as follows.
```json
{
'id': '1',
'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'],
'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0],
'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `art` (1), `building` (2), `event` (3), `location` (4), `organization` (5), `other`(6), `person` (7), `product` (8)
- `fine_ner_tags`: a `list` of fine-grained classification labels, with possible values including `O` (0), `art-broadcastprogram` (1), `art-film` (2), ...
### Data Splits
| Task | Train | Dev | Test |
| ----- | ------ | ----- | ---- |
| SUP | 131767 | 18824 | 37648 |
| INTRA | 99519 | 19358 | 44059 |
| INTER | 130112 | 18817 | 14007 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{ding-etal-2021-nerd,
title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset",
author = "Ding, Ning and
Xu, Guangwei and
Chen, Yulin and
Wang, Xiaobin and
Han, Xu and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.248",
doi = "10.18653/v1/2021.acl-long.248",
pages = "3198--3213",
}
```
### Contributions
|
DFKI-SLT | null | @inproceedings{hennig-etal-2021-mobie,
title = "{M}ob{IE}: A {G}erman Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain",
author = "Hennig, Leonhard and
Truong, Phuc Tran and
Gabryszak, Aleksandra",
booktitle = "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)",
month = "6--9 " # sep,
year = "2021",
address = {D{\"u}sseldorf, Germany},
publisher = "KONVENS 2021 Organizers",
url = "https://aclanthology.org/2021.konvens-1.22",
pages = "223--227",
} | MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks. | false | 320 | false | DFKI-SLT/mobie | 2022-10-24T06:32:09.000Z | mobie | false | 6b1bef2a9b7718d9a345d086ad9750123fa380b4 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:de",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:other",
"task_ids:named-entity-recognition",
"tags:structure-prediction"
] | https://huggingface.co/datasets/DFKI-SLT/mobie/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids:
- named-entity-recognition
paperswithcode_id: mobie
pretty_name: MobIE
tags:
- structure-prediction
---
# Dataset Card for "MobIE"
## 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/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Repository:** [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Paper:** [https://aclanthology.org/2021.konvens-1.22/](https://aclanthology.org/2021.konvens-1.22/)
- **Point of Contact:** See [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Size of downloaded dataset files:** 7.8 MB
- **Size of the generated dataset:** 1.9 MB
- **Total amount of disk used:** 9.7 MB
### Dataset Summary
This script is for loading the MobIE dataset from https://github.com/dfki-nlp/mobie.
MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks.
This version of the dataset loader provides NER tags only. NER tags use the `BIO` tagging scheme.
For more details see https://github.com/dfki-nlp/mobie and https://aclanthology.org/2021.konvens-1.22/.
### Supported Tasks and Leaderboards
- **Tasks:** Named Entity Recognition
- **Leaderboards:**
### Languages
German
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 7.8 MB
- **Size of the generated dataset:** 1.9 MB
- **Total amount of disk used:** 9.7 MB
An example of 'train' looks as follows.
```json
{
'id': 'http://www.ndr.de/nachrichten/verkehr/index.html#2@2016-05-04T21:02:14.000+02:00',
'tokens': ['Vorsicht', 'bitte', 'auf', 'der', 'A28', 'Leer', 'Richtung', 'Oldenburg', 'zwischen', 'Zwischenahner', 'Meer', 'und', 'Neuenkruge', 'liegen', 'Gegenstände', '!'],
'ner_tags': [0, 0, 0, 0, 19, 13, 0, 13, 0, 11, 12, 0, 11, 0, 0, 0]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-date` (1), `I-date` (2), `B-disaster-type` (3), `I-disaster-type` (4), ...
### Data Splits
| | Train | Dev | Test |
| ----- | ------ | ----- | ---- |
| MobIE | 4785 | 1082 | 1210 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{hennig-etal-2021-mobie,
title = "{M}ob{IE}: A {G}erman Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain",
author = "Hennig, Leonhard and
Truong, Phuc Tran and
Gabryszak, Aleksandra",
booktitle = "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)",
month = "6--9 " # sep,
year = "2021",
address = {D{\"u}sseldorf, Germany},
publisher = "KONVENS 2021 Organizers",
url = "https://aclanthology.org/2021.konvens-1.22",
pages = "223--227",
}
```
### Contributions
|
dgknrsln | null | null | null | false | 165 | false | dgknrsln/Yorumsepeti | 2021-05-28T14:03:01.000Z | null | false | 346d5602792e17123e02ab4a712c469704ece8f1 | [] | [] | https://huggingface.co/datasets/dgknrsln/Yorumsepeti/resolve/main/README.md | |
dispenst | null | null | null | false | 164 | false | dispenst/jhghdghfd | 2021-03-28T15:24:20.000Z | null | false | 03da9bf8c82e6ebb3ed7cd09afaf1566fdd6320f | [] | [] | https://huggingface.co/datasets/dispenst/jhghdghfd/resolve/main/README.md | <a href="https://jobs.acm.org/jobs/watch-godzilla-vs-kong-2021-full-1818658-cd">.</a>
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<a href="https://sites.google.com/view/mortalkombat8/">.</a>
<a href="https://sites.google.com/view/mortalkombat9/">.</a>
<a href="https://sites.google.com/view/mortalkombat10/">.</a>
<a href="https://sites.google.com/view/watch-mort-tal-kombat/">.</a>
<a href="https://sites.google.com/view/free-watch-mort-tal-kombat/">.</a>
<a href="https://sites.google.com/view/watch-mort-tal-kombatfree-/">.</a>
<a href="https://sites.google.com/view/full-watch-mortal-kombat/">.</a>
<a href="https://sites.google.com/view/watch-mortal-kombat-2021-/">.</a>
<a href="https://sites.google.com/view/watch-free-mortal-kombat-2021/">.</a>
<a href="https://sites.google.com/view/full-watch-mortal-kombat-/">.</a>
<a href="https://sites.google.com/view/watch-mortal-kombat-g-drive/">.</a>
<a href="https://sites.google.com/view/g-docs-mortalkombat-g-drive/">.</a>
<a href="https://sites.google.com/view/mortal-kombat-2021-full-free/">.</a>
<a href="https://sites.google.com/view/mortal-kombat-2021-full-free-o/">.</a>
<a href="https://sites.google.com/view/mortal-kombat-2021-full-free-o/">.</a>
<a href="https://paiza.io/projects/56xFAEq61pSSn8VnKnHO6Q">.</a>
<a href="https://www.posts123.com/post/1450667/mariners-announce-spring-training">.</a>
<a href="https://sites.google.com/view/sfdjgkdfghdkfgjherghkkdfjg/home">.</a>
<a href="https://dskfjshdkjfewhgf.blogspot.com/2021/03/sdkjfhwekjhfjdherjgfdjg.html">.</a>
<a href="https://grahmaulidia.wordpress.com/2021/03/28/mariners-announce-spring-training-roster-moves/">.</a>
<a href="https://4z5v6wq7a.medium.com/a-letter-to-nationals-fans-from-mark-d-lerner-f83a9ea92f89">.</a>
<a href="https://4z5v6wq7a.medium.com/a-letter-to-nationals-fans-from-mark-d-lerner1-b2847091ff9f">.</a>
<a href="https://4z5v6wq7a.medium.com/a-letter-to-nationals-fans-from-mark-d-lerner2-df35041eec3a">.</a>
<a href="https://4z5v6wq7a.medium.com">.</a>
<a href="https://onlinegdb.com/BJaH8WR4O">.</a> |
diwank | null | null | Raw merged dump of Hinglish (hi-EN) datasets. | false | 50 | false | diwank/hinglish-dump | 2022-03-05T14:28:55.000Z | null | false | 4bc6bb8acfa2b1b370b89138f7af792c36712de1 | [] | [
"license:mit"
] | https://huggingface.co/datasets/diwank/hinglish-dump/resolve/main/README.md | ---
license: mit
---
# Hinglish Dump
Raw merged dump of Hinglish (hi-EN) datasets.
## Subsets and features
Subsets:
- crowd_transliteration
- hindi_romanized_dump
- hindi_xlit
- hinge
- hinglish_norm
- news2018
```
_FEATURE_NAMES = [
"target_hinglish",
"source_hindi",
"parallel_english",
"annotations",
"raw_input",
"alternates",
]
```
|
diwank | null | null | Merged and simplified dialog act datasets from the silicone collection. | false | 478 | false | diwank/silicone-merged | 2022-03-06T11:30:57.000Z | null | false | 8ac729015e92e4f02f1ad60e9c595fbeca504e36 | [] | [
"license:mit"
] | https://huggingface.co/datasets/diwank/silicone-merged/resolve/main/README.md | ---
license: mit
---
# diwank/silicone-merged
> Merged and simplified dialog act datasets from the [silicone collection](https://huggingface.co/datasets/silicone/)
All of the subsets of the original collection have been filtered (for errors and ambiguous classes), merged together and grouped into pairs of dialog turns. It is hypothesized that training dialog act classifier by including the previous utterance can help models pick up additional contextual cues and be better at inference esp if an utterance pair is provided.
## Example training script
```python
from datasets import load_dataset
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
# Get data
silicone_merged = load_dataset("diwank/silicone-merged")
train_df = silicone_merged["train"]
eval_df = silicone_merged["validation"]
model_args = ClassificationArgs(
num_train_epochs=8,
model_type="deberta",
model_name="microsoft/deberta-large",
use_multiprocessing=False,
evaluate_during_training=True,
)
# Create a ClassificationModel
model = ClassificationModel("deberta", "microsoft/deberta-large", args=model_args, num_labels=11) # 11 labels in this dataset
# Train model
model.train_model(train_df, eval_df=eval_df)
```
## Balanced variant of the training set
**Note**: This dataset is highly imbalanced and it is recommended to use a library like [imbalanced-learn](https://imbalanced-learn.org/stable/) before proceeding with training.
Since, balancing can be complicated and resource-intensive, we have shared a balanced variant of the train set that was created via oversampling using the _imbalanced-learn_ library. The balancing used the `SMOTEN` algorithm to deal with categorical data clustering and was resampled on a 16-core, 60GB RAM machine. You can access it using:
```load_dataset("diwank/silicone-merged", "balanced")```
## Feature description
- `text_a`: The utterance prior to the utterance being classified. (Say for dialog with turns 1-2-3, if we are trying to find the dialog act for 2, text_a is 1)
- `text_b`: The utterance to be classified
- `labels`: Dialog act label (as integer between 0-10, as mapped below)
## Labels map
```python
[
(0, 'acknowledge')
(1, 'answer')
(2, 'backchannel')
(3, 'reply_yes')
(4, 'exclaim')
(5, 'say')
(6, 'reply_no')
(7, 'hold')
(8, 'ask')
(9, 'intent')
(10, 'ask_yes_no')
]
```
*****
## Appendix
### How the original datasets were mapped:
```python
mapping = {
"acknowledge": {
"swda": [
"aap_am",
"b",
"bk"
],
"mrda": [],
"oasis": [
"ackn",
"accept",
"complete"
],
"maptask": [
"acknowledge",
"align"
],
"dyda_da": [
"commissive"
]
},
"answer": {
"swda": [
"bf",
],
"mrda": [],
"oasis": [
"answ",
"informCont",
"inform",
"answElab",
"directElab",
"refer"
],
"maptask": [
"reply_w",
"explain"
],
"dyda_da": [
"inform"
]
},
"backchannel": {
"swda": [
"ad",
"bh",
"bd",
"b^m"
],
"mrda": [
"b"
],
"oasis": [
"backch",
"selfTalk",
"init"
],
"maptask": ["ready"],
"dyda_da": []
},
"reply_yes": {
"swda": [
"na",
"aa"
],
"mrda": [],
"oasis": [
"confirm"
],
"maptask": [
"reply_y"
],
"dyda_da": []
},
"exclaim": {
"swda": [
"ft",
"fa",
"fc",
"fp"
],
"mrda": [],
"oasis": [
"appreciate",
"bye",
"exclaim",
"greet",
"thank",
"pardon",
"thank-identitySelf",
"expressRegret"
],
"maptask": [],
"dyda_da": []
},
"say": {
"swda": [
"qh",
"sd"
],
"mrda": ["s"],
"oasis": [
"expressPossibility",
"expressOpinion",
"suggest"
],
"maptask": [],
"dyda_da": []
},
"reply_no": {
"swda": [
"nn",
"ng",
"ar"
],
"mrda": [],
"oasis": [
"refuse",
"negate"
],
"maptask": [
"reply_n"
],
"dyda_da": []
},
"hold": {
"swda": [
"^h",
"t1"
],
"mrda": [
"f"
],
"oasis": [
"hold"
],
"maptask": [],
"dyda_da": []
},
"ask": {
"swda": [
"qw",
"qo",
"qw^d",
"br",
"qrr"
],
"mrda": [
"q"
],
"oasis": [
"reqInfo",
"reqDirect",
"offer"
],
"maptask": [
"query_w"
],
"dyda_da": [
"question"
]
},
"intent": {
"swda": [],
"mrda": [],
"oasis": [
"informIntent",
"informIntent-hold",
"expressWish",
"direct",
"raiseIssue",
"correct"
],
"maptask": [
"instruct",
"clarify"
],
"dyda_da": [
"directive"
]
},
"ask_yes_no": {
"swda": [
"qy^d",
"^g"
],
"mrda": [],
"oasis": [
"reqModal"
],
"maptask": [
"query_yn",
"check"
],
"dyda_da": []
}
}
``` |
dk-crazydiv | null | \ | Metadata information of all the models available on HuggingFace's modelhub | false | 323 | false | dk-crazydiv/huggingface-modelhub | 2021-06-20T14:09:58.000Z | null | false | 5b6f20f66d73f38078bc1e543ee4ee0fe68e2865 | [] | [] | https://huggingface.co/datasets/dk-crazydiv/huggingface-modelhub/resolve/main/README.md | ## Summary
Metadata information of all the models uploaded on [HuggingFace modelhub](https://huggingface.co/models)
Dataset was last updated on 15th June 2021. Contains information on 10,354 models (v1).
Only `train` dataset is provided
#### Update: v1.0.2: Added downloads_last_month and library data
Same dataset is available in [kaggle](https://www.kaggle.com/crazydiv/huggingface-modelhub)
## Loading data
```python
from datasets import load_dataset
modelhub_dataset = load_dataset("dk-crazydiv/huggingface-modelhub")
```
### Useful commands:
```python
modelhub_dataset["train"] # Access train subset (the only subset available)
modelhub_dataset["train"][0] # Access the dataset elements by index
modelhub_dataset["train"].features # Get the columns present in the dataset.
```
### Sample dataset:
```json
{
"downloads_last_month": 7474,
"files": [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"with-prefix-tf_model.h5"
],
"lastModified": "2021-01-13T15:08:24.000Z",
"library": "transformers",
"modelId": "albert-base-v1",
"pipeline_tag": "fill-mask",
"publishedBy": "huggingface",
"tags": [
"pytorch",
"tf",
"albert",
"masked-lm",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"fill-mask"
],
"modelCard": "Readme sample data..."
}
```
## Bugs:
Please report any bugs/improvements to me on [twitter](https://twitter.com/kartik_godawat) |
dlb | null | @misc{Gomes2020,
author = {GOMES, J. R. S.},
title = {Portuguese Language Understanding Evaluation},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/jubs12/PLUE}},
commit = {CURRENT_COMMIT}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
} | PLUE: Portuguese Language Understanding Evaluationis a Portuguese translation of
the GLUE benchmark and Scitail using OPUS-MT model and Google Cloud Translation. | false | 2,350 | false | dlb/plue | 2022-10-29T12:19:26.000Z | null | false | 589d0538b2c05ac37dad771f15b5736732468005 | [] | [
"annotations_creators:found",
"language_creators:machine-generated",
"language:pt",
"license:lgpl-3.0",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|glue",
"task_categories:text-classification",
"task_ids:acceptability-classi... | https://huggingface.co/datasets/dlb/plue/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- pt
license:
- lgpl-3.0
multilinguality:
- monolingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-scoring
- sentiment-classification
- text-scoring
pretty_name: PLUE (Portuguese Language Understanding Evaluation benchmark)
tags:
- paraphrase-identification
- qa-nli
- coreference-nli
---
# Dataset Card for PLUE
## 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
- **Repository:** https://github.com/ju-resplande/PLUE
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Portuguese translation of the <a href="https://gluebenchmark.com/">GLUE benchmark</a>, <a href=https://nlp.stanford.edu/projects/snli/>SNLI</a>, and <a href=https://allenai.org/data/scitail> Scitail</a> using <a href=https://github.com/Helsinki-NLP/OPUS-MT>OPUS-MT model</a> and <a href=https://cloud.google.com/translate/docs>Google Cloud Translation</a>.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language data in PLUE is Brazilian Portuguese (BCP-47 pt-BR)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@misc{Gomes2020,
author = {GOMES, J. R. S.},
title = {PLUE: Portuguese Language Understanding Evaluation},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jubs12/PLUE}},
commit = {CURRENT_COMMIT}
}
```
### Contributions
Thanks to [@ju-resplande](https://github.com/ju-resplande) for adding this dataset. |
dragosnicolae555 | null | null | null | false | 320 | false | dragosnicolae555/RoITD | 2022-10-25T09:07:43.000Z | null | false | def33e5a803a8618fba1fc4ba47f7239e53e7ddb | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:ro-RO",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/dragosnicolae555/RoITD/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ro-RO
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'RoITD: Romanian IT Question Answering Dataset'
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
## Dataset Summary
We introduce a Romanian IT Dataset (RoITD) resembling SQuAD 1.1. RoITD consists of 9575 Romanian QA pairs formulated by crowd workers. QA pairs are based on 5043 articles from Romanian Wikipedia articles describing IT and household products. Of the total number of questions, 5103 are possible (i.e. the correct answer can be found within the paragraph) and 4472 are not possible (i.e. the given answer is a "plausible answer" and not correct)
## Dataset Structure
The data structure follows the format of SQuAD, which contains several attributes such as **question**, **id**, **text**, `**answer_start**, **is_impossible** and **context**. The paragraph provided to crowd sourcing workers is stored in the field **context**. This incorporates manually-selected paragraphs from Wikipedia. The field **id** is comprised of a randomly assigned unique identification number for the answer-question pair. Only the numbers "0" and "1" are allowed in the **is_impossible** field. The category "A" is assigned the value "0", indicating that the answer is correct. The value "1" corresponds to the category "U", indicating a plausible answer. The question posed by the source crowd source worker is represented by the field **question**. The field **answer_start** keeps track of the character index marking the beginning of an answer.
|
dvilasuero | null | null | null | false | 320 | false | dvilasuero/ag_news_error_analysis | 2021-12-29T17:23:31.000Z | null | false | a059319d034bf46bf342c35a1a7d51091b5bcf88 | [] | [] | https://huggingface.co/datasets/dvilasuero/ag_news_error_analysis/resolve/main/README.md | This is a dataset created for testing purposes in the context of this tutorial: https://rubrix.readthedocs.io/en/master/tutorials/08-error_analysis_using_loss.html
You can find more details on section 5. of the tutorial and the corresponding dataset with corrected labels at https://huggingface.co/datasets/Recognai/ag_news_corrected_labels
|
dvilasuero | null | null | null | false | 319 | false | dvilasuero/ag_news_training_set_losses | 2021-09-21T10:10:25.000Z | null | false | 6b18798ac4b3520d0e6f8da8973490114b48fd8f | [] | [] | https://huggingface.co/datasets/dvilasuero/ag_news_training_set_losses/resolve/main/README.md | # AG News train losses
This dataset is part of an experiment using [Rubrix](https://github.com/recognai/rubrix), an open-source Python framework for human-in-the loop NLP data annotation and management. |
dynabench | null | null | Dynabench.DynaSent is a Sentiment Analysis dataset collected using a
human-and-model-in-the-loop. | false | 501 | false | dynabench/dynasent | 2021-04-29T11:30:24.000Z | null | false | d1e2d5e619bb78fb6dc4d548108c50cb65b8d78c | [] | [
"arxiv:2012.15349",
"arxiv:1803.09010",
"arxiv:1810.03993"
] | https://huggingface.co/datasets/dynabench/dynasent/resolve/main/README.md | # DynaSent: Dynamic Sentiment Analysis Dataset
DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. This dataset card is forked from the original [DynaSent Repository](https://github.com/cgpotts/dynasent).
## Contents
* [Citation](#Citation)
* [Dataset files](#dataset-files)
* [Quick start](#quick-start)
* [Data format](#data-format)
* [Models](#models)
* [Other files](#other-files)
* [License](#license)
## Citation
[Christopher Potts](http://web.stanford.edu/~cgpotts/), [Zhengxuan Wu](http://zen-wu.social), Atticus Geiger, and [Douwe Kiela](https://douwekiela.github.io). 2020. [DynaSent: A dynamic benchmark for sentiment analysis](https://arxiv.org/abs/2012.15349). Ms., Stanford University and Facebook AI Research.
```stex
@article{potts-etal-2020-dynasent,
title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus and Kiela, Douwe},
journal={arXiv preprint arXiv:2012.15349},
url={https://arxiv.org/abs/2012.15349},
year={2020}}
```
## Dataset files
The dataset is [dynasent-v1.1.zip](dynasent-v1.1.zip), which is included in this repository. `v1.1` differs from `v1` only in that `v1.1` has proper unique ids for Round 1 and corrects a bug that led to some non-unique ids in Round 2. There are no changes to the examples or other metadata.
The dataset consists of two rounds, each with a train/dev/test split:
### Round 1: Naturally occurring sentences
* `dynasent-v1.1-round01-yelp-train.jsonl`
* `dynasent-v1.1-round01-yelp-dev.jsonl`
* `dynasent-v1.1-round01-yelp-test.jsonl`
### Round 1: Sentences crowdsourced using Dynabench
* `dynasent-v1.1-round02-dynabench-train.jsonl`
* `dynasent-v1.1-round02-dynabench-dev.jsonl`
* `dynasent-v1.1-round02-dynabench-test.jsonl`
### SST-dev revalidation
The dataset also contains a version of the [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/) dev set in our format with labels from our validation task:
* `sst-dev-validated.jsonl`
## Quick start
This function can be used to load any subset of the files:
```python
import json
def load_dataset(*src_filenames, labels=None):
data = []
for filename in src_filenames:
with open(filename) as f:
for line in f:
d = json.loads(line)
if labels is None or d['gold_label'] in labels:
data.append(d)
return data
```
For example, to create a Round 1 train set restricting to examples with ternary gold labels:
```python
import os
r1_train_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round01-yelp-train.jsonl')
ternary_labels = ('positive', 'negative', 'neutral')
r1_train = load_dataset(r1_train_filename, labels=ternary_labels)
X_train, y_train = zip(*[(d['sentence'], d['gold_label']) for d in r1_train])
```
## Data format
### Round 1 format
```python
{'hit_ids': ['y5238'],
'sentence': 'Roto-Rooter is always good when you need someone right away.',
'indices_into_review_text': [0, 60],
'model_0_label': 'positive',
'model_0_probs': {'negative': 0.01173639390617609,
'positive': 0.7473671436309814,
'neutral': 0.24089649319648743},
'text_id': 'r1-0000001',
'review_id': 'IDHkeGo-nxhqX4Exkdr08A',
'review_rating': 1,
'label_distribution': {'positive': ['w130', 'w186', 'w207', 'w264', 'w54'],
'negative': [],
'neutral': [],
'mixed': []},
'gold_label': 'positive'}
```
Details:
* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
* `'sentence'`: The example text.
* `'indices_into_review_text':` indices of `'sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
* `'model_0_label'`: prediction of Model 0 as described in the paper. The possible values are `'positive'`, `'negative'`, and `'neutral'`.
* `'model_0_probs'`: probability distribution predicted by Model 0. The keys are `('positive', 'negative', 'neutral')` and the values are floats.
* `'text_id'`: unique identifier for this entry.
* `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'sentence'`.
* `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset). The possible values are `1`, `2`, `3`, `4`, and `5`.
* `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`.
Here is some code one could use to augment a dataset, as loaded by `load_dataset`, with a field giving the full review text from the [Yelp Academic Dataset](https://www.yelp.com/dataset):
```python
import json
def index_yelp_reviews(yelp_src_filename='yelp_academic_dataset_review.json'):
index = {}
with open(yelp_src_filename) as f:
for line in f:
d = json.loads(line)
index[d['review_id']] = d['text']
return index
yelp_index = index_yelp_reviews()
def add_review_text_round1(dataset, yelp_index):
for d in dataset:
review_text = yelp_index[d['text_id']]
# Check that we can find the sentence as expected:
start, end = d['indices_into_review_text']
assert review_text[start: end] == d['sentence']
d['review_text'] = review_text
return dataset
```
### Round 2 format
```python
{'hit_ids': ['y22661'],
'sentence': "We enjoyed our first and last meal in Toronto at Bombay Palace, and I can't think of a better way to book our journey.",
'sentence_author': 'w250',
'has_prompt': True,
'prompt_data': {'indices_into_review_text': [2093, 2213],
'review_rating': 5,
'prompt_sentence': "Our first and last meals in Toronto were enjoyed at Bombay Palace and I can't think of a better way to bookend our trip.",
'review_id': 'Krm4kSIb06BDHternF4_pA'},
'model_1_label': 'positive',
'model_1_probs': {'negative': 0.29140257835388184,
'positive': 0.6788994669914246,
'neutral': 0.029697999358177185},
'text_id': 'r2-0000001',
'label_distribution': {'positive': ['w43', 'w26', 'w155', 'w23'],
'negative': [],
'neutral': [],
'mixed': ['w174']},
'gold_label': 'positive'}
```
Details:
* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
* `'sentence'`: The example text.
* `'sentence_author'`: Anonymized MTurk id of the worker who wrote `'sentence'`. These are from the same family of ids as used in `'label_distribution'`, but this id is never one of the ids in `'label_distribution'` for this example.
* `'has_prompt'`: `True` if the `'sentence'` was written with a Prompt else `False`.
* `'prompt_data'`: None if `'has_prompt'` is False, else:
* `'indices_into_review_text'`: indices of `'prompt_sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
* `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset).
* `'prompt_sentence'`: The prompt text.
* `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'prompt_sentence'`.
* `'model_1_label'`: prediction of Model 1 as described in the paper. The possible values are `'positive'`, `'negative'`, and '`neutral'`.
* `'model_1_probs'`: probability distribution predicted by Model 1. The keys are `('positive', 'negative', 'neutral')` and the values are floats.
* `'text_id'`: unique identifier for this entry.
* `'label_distribution'`: response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`.
To add the review texts to the `'prompt_data'` field, one can extend the code above for Round 1 with the following function:
```python
def add_review_text_round2(dataset, yelp_index):
for d in dataset:
if d['has_prompt']:
prompt_data = d['prompt_data']
review_text = yelp_index[prompt_data['review_id']]
# Check that we can find the sentence as expected:
start, end = prompt_data['indices_into_review_text']
assert review_text[start: end] == prompt_data['prompt_sentence']
prompt_data['review_text'] = review_text
return dataset
```
### SST-dev format
```python
{'hit_ids': ['s20533'],
'sentence': '-LRB- A -RRB- n utterly charming and hilarious film that reminded me of the best of the Disney comedies from the 60s.',
'tree': '(4 (2 (1 -LRB-) (2 (2 A) (3 -RRB-))) (4 (4 (2 n) (4 (3 (2 utterly) (4 (3 (4 charming) (2 and)) (4 hilarious))) (3 (2 film) (3 (2 that) (4 (4 (2 (2 reminded) (3 me)) (4 (2 of) (4 (4 (2 the) (4 best)) (2 (2 of) (3 (2 the) (3 (3 Disney) (2 comedies))))))) (2 (2 from) (2 (2 the) (2 60s)))))))) (2 .)))',
'text_id': 'sst-dev-validate-0000437',
'sst_label': '4',
'label_distribution': {'positive': ['w207', 'w3', 'w840', 'w135', 'w26'],
'negative': [],
'neutral': [],
'mixed': []},
'gold_label': 'positive'}
```
Details:
* `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset.
* `'sentence'`: The example text.
* `'tree'`: The parsetree for the example as given in the SST distribution.
* `'text_id'`: A new identifier for this example.
* `'sst_label'`: The root-node label from the SST. Possible values `'0'`, `'1'` `'2'`, `'3'`, and `'4'`.
* `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset.
* `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`.
## Models
Model 0 and Model 1 from the paper are available here:
https://drive.google.com/drive/folders/1dpKrjNJfAILUQcJPAFc5YOXUT51VEjKQ?usp=sharing
This repository includes a Python module `dynasent_models.py` that provides a [Hugging Face](https://huggingface.co)-based wrapper around these ([PyTorch](https://pytorch.org)) models. Simple examples:
```python
import os
from dynasent_models import DynaSentModel
# `dynasent_model0` should be downloaded from the above Google Drive link and
# placed in the `models` directory. `dynasent_model1` works the same way.
model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin'))
examples = [
"superb",
"They said the experience would be amazing, and they were right!",
"They said the experience would be amazing, and they were wrong!"]
model.predict(examples)
```
This should return the list `['positive', 'positive', 'negative']`.
The `predict_proba` method provides access to the predicted distribution over the class labels; see the demo at the bottom of `dynasent_models.py` for details.
The following code uses `load_dataset` from above to reproduce the Round 2 dev-set report on Model 0 from the paper:
```python
import os
from sklearn.metrics import classification_report
from dynasent_models import DynaSentModel
dev_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round02-dynabench-dev.jsonl')
dev = load_dataset(dev_filename)
X_dev, y_dev = zip(*[(d['sentence'], d['gold_label']) for d in dev])
model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin'))
preds = model.predict(X_dev)
print(classification_report(y_dev, preds, digits=3))
```
For a fuller report on these models, see our paper and [our model card](dynasent_modelcard.md).
## Other files
### Analysis notebooks
The following notebooks reproduce the dataset statistics, figures, and random example selections from the paper:
* `analyses_comparative.ipynb`
* `analysis_round1.ipynb`
* `analysis_round2.ipynb`
* `analysis_sst_dev_revalidate.ipynb`
The Python module `dynasent_utils.py` contains functions that support those notebooks, and `dynasent.mplstyle` helps with styling the plots.
### Datasheet
The [Datasheet](https://arxiv.org/abs/1803.09010) for our dataset:
* [dynasent_datasheet.md](dynasent_datasheet.md)
### Model Card
The [Model Card](https://arxiv.org/pdf/1810.03993.pdf) for our models:
* [dynasent_modelcard.md](dynasent_modelcard.md)
### Tests
The module `test_dataset.py` contains PyTest tests for the dataset. To use it, run
```
py.test -vv test_dataset.py
```
in the root directory of this repository.
### Validation HIT code
The file `validation-hit-contents.html` contains the HTML/Javascript used in the validation task. It could be used directly on Amazon Mechanical Turk, by simply pasting its contents into the usual HIT creation window.
## License
DynaSent has a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). |
dynabench | null | null | Dynabench.QA is a Reading Comprehension dataset collected using a human-and-model-in-the-loop. | false | 789 | false | dynabench/qa | 2022-07-02T20:17:58.000Z | null | false | 3c4dbdd9119ff5dfeafe06f06f9ae7a6824e02ae | [] | [
"arxiv:2002.00293",
"arxiv:1606.05250",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
... | https://huggingface.co/datasets/dynabench/qa/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
---
# Dataset Card for Dynabench.QA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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:** [Dynabench.QA](https://dynabench.org/tasks/2#overall)
- **Paper:** [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293)
- **Leaderboard:** [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall)
- **Point of Contact:** [Max Bartolo](max.bartolo@ucl.ac.uk)
### Dataset Summary
Dynabench.QA is an adversarially collected Reading Comprehension dataset spanning over multiple rounds of data collect.
For round 1, it is identical to the [adversarialQA dataset](https://adversarialqa.github.io/), where we have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods.
### Supported Tasks and Leaderboards
`extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
Data is provided in the same format as SQuAD 1.1. An example is shown below:
```
{
"data": [
{
"title": "Oxygen",
"paragraphs": [
{
"context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.",
"qas": [
{
"id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3",
"question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?",
"answers": [
{
"answer_start": 36,
"text": "organic compounds"
}
]
},
{
"id": "4240a8e708c703796347a3702cf1463eed05584a",
"question": "What letter does the abbreviation for acid anhydrides both begin and end in?",
"answers": [
{
"answer_start": 244,
"text": "R"
}
]
},
{
"id": "0681a0a5ec852ec6920d6a30f7ef65dced493366",
"question": "Which of the organic compounds, in the article, contains nitrogen?",
"answers": [
{
"answer_start": 262,
"text": "amides"
}
]
},
{
"id": "2990efe1a56ccf81938fa5e18104f7d3803069fb",
"question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?",
"answers": [
{
"answer_start": 262,
"text": "amides"
}
]
}
]
}
]
}
]
}
```
### Data Fields
- title: the title of the Wikipedia page from which the context is sourced
- context: the context/passage
- id: a string identifier for each question
- answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text
### Data Splits
For round 1, the dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples.
## Dataset Creation
### Curation Rationale
This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models.
### Source Data
#### Initial Data Collection and Normalization
The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250).
#### Who are the source language producers?
The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions.
### Annotations
#### Annotation process
The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model.
#### Who are the annotators?
The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation.
### Personal and Sensitive Information
No annotator identifying details are provided.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better question answering systems.
A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question.
It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application.
### Discussion of Biases
The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol.
### Other Known Limitations
N/a
## Additional Information
### Dataset Curators
This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL).
### Licensing Information
This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
### Citation Information
```
@article{bartolo2020beat,
author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus},
title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension},
journal = {Transactions of the Association for Computational Linguistics},
volume = {8},
number = {},
pages = {662-678},
year = {2020},
doi = {10.1162/tacl\_a\_00338},
URL = { https://doi.org/10.1162/tacl_a_00338 },
eprint = { https://doi.org/10.1162/tacl_a_00338 },
abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). }
}
```
### Contributions
Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset. |
ebrigham | null | null | null | false | 165 | false | ebrigham/asr_files | 2022-01-03T11:29:38.000Z | null | false | 5d7c462f99263b16b72306f21f3f87b2ecdf83ea | [] | [] | https://huggingface.co/datasets/ebrigham/asr_files/resolve/main/README.md | asr files |
echarlaix | null | @inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
author={Hudson, Drew A and Manning, Christopher D},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6700--6709},
year={2019}
} | GQA is a new dataset for real-world visual reasoning and compositional question answering,
seeking to address key shortcomings of previous visual question answering (VQA) datasets. | false | 323 | false | echarlaix/gqa-lxmert | 2022-02-09T23:39:45.000Z | null | false | fbeeed5fdfe4f226299f5fa26fda176cb260f333 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/echarlaix/gqa-lxmert/resolve/main/README.md | ---
license: apache-2.0
---
|
echarlaix | null | @inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
author={Hudson, Drew A and Manning, Christopher D},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6700--6709},
year={2019}
} | GQA is a new dataset for real-world visual reasoning and compositional question answering,
seeking to address key shortcomings of previous visual question answering (VQA) datasets. | false | 320 | false | echarlaix/gqa | 2022-02-01T10:44:11.000Z | null | false | 5a76297440a02f78d9b6dbd0fea87d62d132676b | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/echarlaix/gqa/resolve/main/README.md | ---
license: apache-2.0
---
|
echarlaix | null | @inproceedings{antol2015vqa,
title={Vqa: Visual question answering},
author={Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C Lawrence and Parikh, Devi},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={2425--2433},
year={2015}
} | VQA is a new dataset containing open-ended questions about images.
These questions require an understanding of vision, language and commonsense knowledge to answer. | false | 323 | false | echarlaix/vqa-lxmert | 2022-02-09T23:41:22.000Z | null | false | 54f2ecab65bd61d27cc66597f7abb8305cfe9a28 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/echarlaix/vqa-lxmert/resolve/main/README.md | ---
license: apache-2.0
---
|
echarlaix | null | @inproceedings{antol2015vqa,
title={Vqa: Visual question answering},
author={Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C Lawrence and Parikh, Devi},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={2425--2433},
year={2015}
} | VQA is a new dataset containing open-ended questions about images.
These questions require an understanding of vision, language and commonsense knowledge to answer. | false | 320 | false | echarlaix/vqa | 2022-02-01T10:45:13.000Z | null | false | 28994091cc52fbeb166d4bd5eb870e9642b5baef | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/echarlaix/vqa/resolve/main/README.md | ---
license: apache-2.0
---
|
edbeeching | null | null | null | false | 168 | false | edbeeching/decision_transformer_atari_dqn_replay | 2022-02-09T13:37:13.000Z | null | false | 687ca55702c1d34eea60167502fa1d20e18eefca | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/edbeeching/decision_transformer_atari_dqn_replay/resolve/main/README.md | ---
license: apache-2.0
---
|
edbeeching | null | null | A subset of the D4RL dataset, used for training Decision Transformers | false | 1,967 | false | edbeeching/decision_transformer_gym_replay | 2022-04-20T12:39:58.000Z | null | false | 4441c97718b1f7e03d05f430226b57f658cc156d | [] | [
"arxiv:2004.07219",
"license:apache-2.0"
] | https://huggingface.co/datasets/edbeeching/decision_transformer_gym_replay/resolve/main/README.md | ---
license: apache-2.0
pretty_name: D4RL-gym
---
# Dataset Card for D4RL-gym
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://sites.google.com/view/d4rl/home/
- **Repository:** https://github.com/rail-berkeley/d4rl*
- **Paper:** D4RL: Datasets for Deep Data-Driven Reinforcement Learning https://arxiv.org/abs/2004.07219
### Dataset Summary
D4RL is an open-source benchmark for offline reinforcement learning. It provides standardized environments and datasets for training and benchmarking algorithms.
We host here a subset of the dataset, used for the training of Decision Transformers : https://github.com/kzl/decision-transformer
There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator.
## Dataset Structure
### Data Instances
A data point comprises tuples of sequences of (observations, actions, reward, dones):
```
{
"observations":datasets.Array2D(),
"actions":datasets.Array2D(),
"rewards":datasets.Array2D(),
"dones":datasets.Array2D(),
}
```
### Data Fields
- `observations`: An Array2D containing 1000 observations from a trajectory of an evaluated agent.
- `actions`: An Array2D containing 1000 actions from a trajectory of an evaluated agent.
- `rewards`: An Array2D containing 1000 rewards from a trajectory of an evaluated agent.
- `dones`: An Array2D containing 1000 terminal state flags from a trajectory of an evaluated agent.
### Data Splits
There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator.
## Additional Information
### Dataset Curators
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine
### Licensing Information
MIT Licence
### Citation Information
```
@misc{fu2021d4rl,
title={D4RL: Datasets for Deep Data-Driven Reinforcement Learning},
author={Justin Fu and Aviral Kumar and Ofir Nachum and George Tucker and Sergey Levine},
year={2021},
eprint={2004.07219},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
### Contributions
Thanks to [@edbeeching](https://github.com/edbeeching) for adding this dataset. |
edbeeching | null | null | null | false | 322 | false | edbeeching/github-issues | 2022-02-11T14:20:42.000Z | null | false | 2a081d71c7613e86fea6a2b80c74326896b3e892 | [] | [] | https://huggingface.co/datasets/edbeeching/github-issues/resolve/main/README.md | annotations_creators:
- other
language_creators:
- crowdsourced
languages:
- en-US
licenses:
- other-my-license
multilinguality:
- monolingual
pretty_name: HuggingFace Github Issues
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
- document-retrieval |
edsas | null | null | null | false | 166 | false | edsas/fgrdtgrdtdr | 2021-05-06T01:33:59.000Z | null | false | 0ea0800152e4bb1635be7e4f8030919b994cafcf | [] | [] | https://huggingface.co/datasets/edsas/fgrdtgrdtdr/resolve/main/README.md | |
edsas | null | null | null | false | 165 | false | edsas/grttyi | 2021-05-06T01:37:07.000Z | null | false | 9c064b25bc35189d83db7d6d6aa5ec66a2175dec | [] | [] | https://huggingface.co/datasets/edsas/grttyi/resolve/main/README.md | |
ehcalabres | null | null | null | false | 165 | false | ehcalabres/ravdess_speech | 2022-10-24T15:51:41.000Z | null | false | 9e426939f02e1980603736a1413d5aefc0dd3d93 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:audio-classification",
"task_ids:speech-emotion-recognition"
] | https://huggingface.co/datasets/ehcalabres/ravdess_speech/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- speech-emotion-recognition
---
# Dataset Card for ravdess_speech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://zenodo.org/record/1188976#.YUS4MrozZdS
- **Paper:** https://doi.org/10.1371/journal.pone.0196391
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** ravdess@gmail.com
### Dataset Summary
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. The conditions of the audio files are: 16bit, 48kHz .wav.
### Supported Tasks and Leaderboards
- audio-classification: The dataset can be used to train a model for Audio Classification tasks, which consists in predict the latent emotion presented on the audios.
### Languages
The audios available in the dataset are in English spoken by actors in a neutral North American accent.
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0
Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our license fee page, or contact us at ravdess@gmail.com.
### Citation Information
Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. https://doi.org/10.1371/journal.pone.0196391. |
emre | null | null | null | false | 317 | false | emre/Open_SLR108_Turkish_10_hours | 2022-01-24T14:48:50.000Z | null | false | 7d6af439a45c190e18c437d3525f1965fe9e44e2 | [] | [
"arxiv:2103.16193",
"license:Creative Commons Attribution 4.0 International License",
"tags:robust-speech-event",
"datasets:MediaSpeech"
] | https://huggingface.co/datasets/emre/Open_SLR108_Turkish_10_hours/resolve/main/README.md | ---
license: Creative Commons Attribution 4.0 International License
tags:
- robust-speech-event
datasets:
- MediaSpeech
---
MediaSpeech
Identifier: SLR108
Summary: French, Arabic, Turkish and Spanish media speech datasets
Category: Speech
License: dataset is distributed under the Creative Commons Attribution 4.0 International License.
About this resource:
MediaSpeech is a dataset of French, Arabic, Turkish and Spanish media speech built with the purpose of testing Automated Speech Recognition (ASR) systems performance. The dataset contains 10 hours of speech for each language provided.
The dataset consists of short speech segments automatically extracted from media videos available on YouTube and manually transcribed, with some pre- and post-processing.
Baseline models and wav version of the dataset can be found in the following git repository: https://github.com/NTRLab/MediaSpeech
@misc{mediaspeech2021,
title={MediaSpeech: Multilanguage ASR Benchmark and Dataset},
author={Rostislav Kolobov and Olga Okhapkina and Olga Omelchishina, Andrey Platunov and Roman Bedyakin and Vyacheslav Moshkin and Dmitry Menshikov and Nikolay Mikhaylovskiy},
year={2021},
eprint={2103.16193},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
|
emrecan | null | null | null | false | 331 | false | emrecan/stsb-mt-turkish | 2022-10-25T10:55:24.000Z | null | false | 79dd9aac442c9a88535865583a3ed4e75d7b47da | [] | [
"language_creators:machine-generated",
"language:tr",
"size_categories:1K<n<10K",
"source_datasets:extended|other-sts-b",
"task_categories:text-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring"
] | https://huggingface.co/datasets/emrecan/stsb-mt-turkish/resolve/main/README.md | ---
language_creators:
- machine-generated
language:
- tr
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification
task_ids:
- semantic-similarity-scoring
- text-scoring
---
# STSb Turkish
Semantic textual similarity dataset for the Turkish language. It is a machine translation (Azure) of the [STSb English](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) dataset. This dataset is not reviewed by expert human translators.
Uploaded from [this repository](https://github.com/emrecncelik/sts-benchmark-tr). |
enelpol | null | null | null | false | 317 | false | enelpol/czywiesz | 2022-10-25T09:07:45.000Z | null | false | 7c235e1da745ff8aef467b19ef6b155642ca8bcf | [] | [
"language:pl",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/enelpol/czywiesz/resolve/main/README.md | ---
language:
- pl
license:
- unknown
multilinguality:
- monolingual
pretty_name: Czywiesz
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
---
This is an extract of the original [Czywiesz](https://clarin-pl.eu/dspace/handle/11321/39) dataset. It contains the questions and the relevant Wikipedia
passages in format compatible with DPR training objective. It may be used to train a passage retriever. |
erwanlc | null | null | null | false | 318 | false | erwanlc/cocktails_recipe | 2022-10-25T09:17:00.000Z | null | false | 60a26b89257179967d48dc8de7c24c0c9df76c16 | [] | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:2M<n<3M",
"language_bcp47:en",
"language_bcp47:en-US"
] | https://huggingface.co/datasets/erwanlc/cocktails_recipe/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 2M<n<3M
source_datasets: []
task_categories: []
task_ids: []
pretty_name: cocktails_recipe
language_bcp47:
- en
- en-US
---
# Dataset Card for cocktails_recipe
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains a list of cocktails and how to do them.
### Languages
The language is english.
## Dataset Structure
### Data Fields
- Title: name of the cocktail
- Glass: type of glass to use
- Garnish: garnish to use for the glass
- Recipe: how to do the cocktail
- Ingredients: ingredients required
### Data Splits
Currently, there is no splits.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by scraping the Diffords cocktail website.
### Personal and Sensitive Information
It should not contain any personal or sensitive information.
### Contributions
Thanks to [@github-erwanlc](https://github.com/erwanlc) for adding this dataset. |
erwanlc | null | null | null | false | 317 | false | erwanlc/cocktails_recipe_no_brand | 2022-10-25T09:17:08.000Z | null | false | a33b63910d8c33675132dd3a8f285549ef8b4b7b | [] | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:2M<n<3M",
"language_bcp47:en",
"language_bcp47:en-US"
] | https://huggingface.co/datasets/erwanlc/cocktails_recipe_no_brand/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 2M<n<3M
source_datasets: []
task_categories: []
task_ids: []
pretty_name: cocktails_recipe_no_brand
language_bcp47:
- en
- en-US
---
# Dataset Card for cocktails_recipe
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains a list of cocktails and how to do them.
### Languages
The language is english.
## Dataset Structure
### Data Fields
- Title: name of the cocktail
- Glass: type of glass to use
- Garnish: garnish to use for the glass
- Recipe: how to do the cocktail
- Ingredients: ingredients required
- Raw Ingredients: ingredients mapped to their raw ingredients to remove the brand
### Data Splits
Currently, there is no splits.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by scraping the Diffords cocktail website.
### Personal and Sensitive Information
It should not contain any personal or sensitive information.
### Contributions
Thanks to [@github-erwanlc](https://github.com/erwanlc) for adding this dataset. |
espejelomar | null | null | null | false | 322 | false | espejelomar/code_search_net_python_10000_examples | 2022-02-20T03:42:13.000Z | null | false | d0551d78fbb13309bfbfdb942f01e58cbe41a472 | [] | [
"license:cc"
] | https://huggingface.co/datasets/espejelomar/code_search_net_python_10000_examples/resolve/main/README.md | ---
license: cc
---
|
eugenesiow | null | @inproceedings{martin2001database,
title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
volume={2},
pages={416--423},
year={2001},
organization={IEEE}
} | BSD is a dataset used frequently for image denoising and super-resolution.
BSD100 is the testing set of the Berkeley segmentation dataset BSD300. | false | 511 | false | eugenesiow/BSD100 | 2022-10-26T02:20:22.000Z | null | false | 7a20e0a3c51c5e5153a4416c8606a1476565fa74 | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"license:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:image-super-resolution"
] | https://huggingface.co/datasets/eugenesiow/BSD100/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: BSD100
tags:
- image-super-resolution
---
# Dataset Card for BSD100
## 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://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
- **Repository**: https://huggingface.co/datasets/eugenesiow/BSD100
- **Paper**: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
BSD is a dataset used frequently for image denoising and super-resolution. Of the subdatasets, BSD100 is aclassical image dataset having 100 test images proposed by [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655). The dataset is composed of a large variety of images ranging from natural images to object-specific such as plants, people, food etc. BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/BSD100', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_HR/3096.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_LR_x2/3096.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|
|-------|---:|
|bicubic_x2|100|
|bicubic_x3|100|
|bicubic_x4|100|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- **Original Authors**: [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655)
### Licensing Information
You are free to download a portion of the dataset for non-commercial research and educational purposes.
In exchange, we request only that you make available to us the results of running your segmentation or
boundary detection algorithm on the test set as described below. Work based on the dataset should cite
the [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655) paper.
### Citation Information
```bibtex
@inproceedings{martin2001database,
title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
volume={2},
pages={416--423},
year={2001},
organization={IEEE}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
|
eugenesiow | null | @InProceedings{Agustsson_2017_CVPR_Workshops,
author = {Agustsson, Eirikur and Timofte, Radu},
title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
month = {July},
year = {2017}
} | DIV2K dataset: DIVerse 2K resolution high quality images as used for the challenges @ NTIRE (CVPR 2017 and
CVPR 2018) and @ PIRM (ECCV 2018) | false | 2,630 | false | eugenesiow/Div2k | 2022-10-21T04:01:10.000Z | null | false | a6aa2cb45e33a4753d28a373bd1125a321a1c21d | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"license:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:other-image-super-resolution"
] | https://huggingface.co/datasets/eugenesiow/Div2k/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Div2k
tags:
- other-image-super-resolution
---
# Dataset Card for Div2k
## 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://data.vision.ee.ethz.ch/cvl/DIV2K/
- **Repository**: https://huggingface.co/datasets/eugenesiow/Div2k
- **Paper**: http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
DIV2K is a dataset of RGB images (2K resolution high quality images) with a large diversity of contents.
The DIV2K dataset is divided into:
- train data: starting from 800 high definition high resolution images we obtain corresponding low resolution images and provide both high and low resolution images for 2, 3, and 4 downscaling factors
- validation data: 100 high definition high resolution images are used for genereting low resolution corresponding images, the low res are provided from the beginning of the challenge and are meant for the participants to get online feedback from the validation server; the high resolution images will be released when the final phase of the challenge starts.
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for training and evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `train` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_HR/0801.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_LR_bicubic/X2/0801x2.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |train |validation|
|-------|-----:|---:|
|bicubic_x2|800|100|
|bicubic_x3|800|100|
|bicubic_x4|800|100|
|bicubic_x8|800|100|
|unknown_x2|800|100|
|unknown_x3|800|100|
|unknown_x4|800|100|
|realistic_mild_x4|800|100|
|realistic_difficult_x4|800|100|
|realistic_wild_x4|800|100|
## Dataset Creation
### Curation Rationale
Please refer to the [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) section.
### Source Data
#### Initial Data Collection and Normalization
**Resolution and quality**: All the images are 2K resolution, that is they have 2K pixels on at least one of
the axes (vertical or horizontal). All the images were processed using the same tools. For simplicity, since the most
common magnification factors in the recent SR literature are of ×2, ×3 and ×4 we cropped the images to multiple of
12 pixels on both axes. Most of the crawled images were originally above 20M pixels.
The images are of high quality both aesthetically and in the terms of small amounts of noise and other corruptions
(like blur and color shifts).
**Diversity**: The authors collected images from dozens of sites. A preference was made for sites with freely
shared high quality photography (such as https://www.pexels.com/ ). Note that we did not use images from Flickr,
Instagram, or other legally binding or copyright restricted images. We only seldom used keywords to assure the diversity
for our dataset. DIV2K covers a large diversity of contents, ranging from people, handmade objects and environments
(cities, villages), to flora and fauna, and natural sceneries including underwater and dim light conditions.
**Partitions**: After collecting the DIV2K 1000 images the authors computed image entropy, bit per pixel (bpp) PNG
compression rates and CORNIA scores (see Section 7.6) and applied bicubic downscaling ×3 and then upscaling ×3 with
bicubic interpolation (imresize Matlab function), ANR [47] and A+ [48] methods and default settings.
The authors randomly generated partitions of 800 train, 100 validation and 100 test images until they achieved a good
balance firstly in visual contents and then on the average entropy, average bpp, average number of pixels per
image (ppi), average CORNIA quality scores and also in the relative differences between the average PSNR scores of
bicubic, ANR and A+ methods.
Only the 800 train and 100 validation images are included in this dataset.
#### Who are the source language producers?
The authors manually crawled 1000 color RGB images from Internet paying special attention to the image quality,
to the diversity of sources (sites and cameras), to the image contents and to the copyrights.
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### Personal and Sensitive Information
All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images
belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset
immediately.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- **Original Author**: [Radu Timofte](http://people.ee.ethz.ch/~timofter/)
### Licensing Information
Please notice that this dataset is made available for academic research purpose only. All the images are
collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to
you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset
immediately.
### Citation Information
```bibtex
@InProceedings{Agustsson_2017_CVPR_Workshops,
author = {Agustsson, Eirikur and Timofte, Radu},
title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
month = {July},
year = {2017}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
|
eugenesiow | null | @misc{shoeiby2019pirm2018,
title={PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study},
author={Mehrdad Shoeiby and Antonio Robles-Kelly and Ran Wei and Radu Timofte},
year={2019},
eprint={1904.00540},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing.
These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc.
Images vary in size, and are typically ~300K pixels in resolution.
This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM
challenge on Perceptual Super-resolution, in conjunction with ECCV 2018. | false | 323 | false | eugenesiow/PIRM | 2022-10-21T04:01:16.000Z | null | false | 0fbc53ce3af34f8283a46d70ed353ccc67085237 | [] | [
"arxiv:1809.07517",
"annotations_creators:machine-generated",
"language_creators:found",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:other-image-super-resolution"
] | https://huggingface.co/datasets/eugenesiow/PIRM/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: PIRM
tags:
- other-image-super-resolution
---
# Dataset Card for PIRM
## 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://github.com/roimehrez/PIRM2018
- **Repository**: https://huggingface.co/datasets/eugenesiow/PIRM
- **Paper**: https://arxiv.org/abs/1809.07517
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing.
These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc.
Images vary in size, and are typically ~300K pixels in resolution.
This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM
challenge on Perceptual Super-resolution, in conjunction with ECCV 2018.
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/PIRM', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_HR/1.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_LR_x2/1.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|test|
|-------|---:|---:|
|bicubic_x2|100|100|
|bicubic_x3|100|100|
|bicubic_x4|100|100|
|unknown_x4|100|100|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- **Original Authors**: [Blau et al. (2018)](https://arxiv.org/abs/1809.07517)
### Licensing Information
This dataset is published under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
```bibtex
@misc{blau20192018,
title={The 2018 PIRM Challenge on Perceptual Image Super-resolution},
author={Yochai Blau and Roey Mechrez and Radu Timofte and Tomer Michaeli and Lihi Zelnik-Manor},
year={2019},
eprint={1809.07517},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
|
eugenesiow | null | @inproceedings{zeyde2010single,
title={On single image scale-up using sparse-representations},
author={Zeyde, Roman and Elad, Michael and Protter, Matan},
booktitle={International conference on curves and surfaces},
pages={711--730},
year={2010},
organization={Springer}
} | Set14 is an evaluation dataset with 14 RGB images for the image super resolution task. | false | 322 | false | eugenesiow/Set14 | 2022-10-21T04:00:31.000Z | null | false | 5afcf80d267dba61cdfa9a32b1a6fe4cca57b6d7 | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"license:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:other-image-super-resolution"
] | https://huggingface.co/datasets/eugenesiow/Set14/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Set14
tags:
- other-image-super-resolution
---
# Dataset Card for Set14
## 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://sites.google.com/site/romanzeyde/research-interests
- **Repository**: https://huggingface.co/datasets/eugenesiow/Set14
- **Paper**: http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
Set14 is an evaluation dataset with 14 RGB images for the image super resolution task. It was first used as the test set of the paper "On single image scale-up using sparse-representations" by [Zeyde et al. (2010)](http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf).
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/Set14', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/Set14_HR/baboon.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/Set14_LR_x2/baboon.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|
|-------|---:|
|bicubic_x2|14|
|bicubic_x3|14|
|bicubic_x4|14|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- **Original Authors**: [Zeyde et al.](http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf)
### Licensing Information
Academic use only.
### Citation Information
```bibtex
@inproceedings{zeyde2010single,
title={On single image scale-up using sparse-representations},
author={Zeyde, Roman and Elad, Michael and Protter, Matan},
booktitle={International conference on curves and surfaces},
pages={711--730},
year={2010},
organization={Springer}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
|
eugenesiow | null | @article{bevilacqua2012low,
title={Low-complexity single-image super-resolution based on nonnegative neighbor embedding},
author={Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line},
year={2012},
publisher={BMVA press}
} | Set5 is a evaluation dataset with 5 RGB images for the image super resolution task. | false | 334 | false | eugenesiow/Set5 | 2022-10-21T03:59:16.000Z | null | false | d8b579a20afde95b4d8ed6bf6383447d33027295 | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"license:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:other-image-super-resolution"
] | https://huggingface.co/datasets/eugenesiow/Set5/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Set5
tags:
- other-image-super-resolution
---
# Dataset Card for Set5
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage**: http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html
- **Repository**: https://huggingface.co/datasets/eugenesiow/Set5
- **Paper**: http://people.rennes.inria.fr/Aline.Roumy/publi/12bmvc_Bevilacqua_lowComplexitySR.pdf
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
Set5 is a evaluation dataset with 5 RGB images for the image super resolution task. The 5 images of the dataset are (“baby”, “bird”, “butterfly”, “head”, “woman”).
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/Set5', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/Set5_HR/baby.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/Set5_LR_x2/baby.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|
|-------|---:|
|bicubic_x2|5|
|bicubic_x3|5|
|bicubic_x4|5|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- **Original Authors**: [Bevilacqua et al.](http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html)
### Licensing Information
Academic use only.
### Citation Information
```bibtex
@article{bevilacqua2012low,
title={Low-complexity single-image super-resolution based on nonnegative neighbor embedding},
author={Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line},
year={2012},
publisher={BMVA press}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
|
eugenesiow | null | @inproceedings{martin2001database,
title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
volume={2},
pages={416--423},
year={2001},
organization={IEEE}
} | The Urban100 dataset contains 100 images of urban scenes.
It commonly used as a test set to evaluate the performance of super-resolution models. | false | 323 | false | eugenesiow/Urban100 | 2022-10-21T03:58:53.000Z | null | false | fb0d8a4c6b2471d32bd133de40bb8bb10dde69b9 | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:other-image-super-resolution"
] | https://huggingface.co/datasets/eugenesiow/Urban100/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Urban100
tags:
- other-image-super-resolution
---
# Dataset Card for Urban100
## 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://github.com/jbhuang0604/SelfExSR
- **Repository**: https://huggingface.co/datasets/eugenesiow/Urban100
- **Paper**: https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Single_Image_Super-Resolution_2015_CVPR_paper.html
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models. It was first published by [Huang et al. (2015)](https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Single_Image_Super-Resolution_2015_CVPR_paper.html) in the paper "Single Image Super-Resolution From Transformed Self-Exemplars".
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/Urban100', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/Urban100_HR/img_001.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/Urban100_LR_x2/img_001.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|
|-------|---:|
|bicubic_x2|100|
|bicubic_x3|100|
|bicubic_x4|100|
## Dataset Creation
### Curation Rationale
The authors have created Urban100 containing 100 HR images with a variety of real-world structures.
### Source Data
#### Initial Data Collection and Normalization
The authors constructed this dataset using images from Flickr (under CC license) using keywords such as urban, city, architecture, and structure.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- **Original Authors**: [Huang et al. (2015)](https://github.com/jbhuang0604/SelfExSR)
### Licensing Information
The dataset provided uses images from Flikr under the CC (CC-BY-4.0) license.
### Citation Information
```bibtex
@InProceedings{Huang_2015_CVPR,
author = {Huang, Jia-Bin and Singh, Abhishek and Ahuja, Narendra},
title = {Single Image Super-Resolution From Transformed Self-Exemplars},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
|
evageon | null | null | null | false | 320 | false | evageon/IADD | 2022-01-29T11:16:17.000Z | null | false | 288fa596f1a5ceb5c207c8ebdcebc92e15903ce7 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/evageon/IADD/resolve/main/README.md | ---
license: cc-by-4.0
---
# IADD
IADD is an Integrated Dataset for Arabic Dialect iDentification Dataset. It contains 136,317 texts representing 5 regions (Maghrebi (MGH) , Levantine (LEV), Egypt (EGY) , Iraq (IRQ) and Gulf (GLF)) and 9 countries (Algeria, Morocco, Tunisia, Palestine, Jordan, Syria, Lebanon, Egypt and Iraq).
IADD is created from the combination of subsets of five corpora: DART, SHAMI, TSAC, PADIC and AOC. The Dialectal ARabic Tweets dataset (DART) [1] has about 25,000 tweets that are annotated via crowdsourcing while the SHAMI dataset [2] consists of 117,805 sentences and covers levantine dialects spoken in Palestine, Jordan, Lebanon and Syria. TSAC [3] is a Tunisian dialect corpus of 17,000 comments collected mainly from Tunisian Facebook pages. Parallel Arabic Dialect Corpus (PADIC) [4] is made of sentences transcribed from recordings or translated from MSA. Finally, the Arabic Online Commentary (AOC) dataset [5] is based on reader commentary from the online versions of three Arabic newspapers, and it consists of 1.4M comments.
IADD is stored in a JSON-like format with the following keys:
- Sentence: contains the sentence/ text;
- Region: stores the corresponding dialectal region (MGH, LEV, EGY, IRQ, GLF or general);
- Country: specifies the corresponding country, if available (MAR, TUN, DZ, EGY, IRQ, SYR, JOR, PSE, LBN);
- DataSource: indicates the source of the data (PADIC, DART, AOC, SHAMI or TSAC).
[1] Alsarsour, I., Mohamed, E., Suwaileh, R., & Elsayed, T. (2018, May). Dart: A large dataset of dialectal arabic tweets. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
[2] Abu Kwaik, K., Saad, M. K., Chatzikyriakidis, S., & Dobnik, S. (2018). Shami: A corpus of levantine arabic dialects. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).
[3] Mdhaffar, S., Bougares, F., Esteve, Y., & Hadrich-Belguith, L. (2017, April). Sentiment analysis of tunisian dialects: Linguistic ressources and experiments. In Third Arabic Natural Language Processing Workshop (WANLP) (pp. 55-61).
[4] Meftouh, K., Harrat, S., Jamoussi, S., Abbas, M., & Smaili, K. (2015, October). Machine translation experiments on PADIC: A parallel Arabic dialect corpus. In The 29th Pacific Asia conference on language, information and computation.
[5] Zaidan, O., & Callison-Burch, C. (2011, June). The arabic online commentary dataset: an annotated dataset of informal arabic with high dialectal content. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 37-41).
|
ewdrtfwe | null | null | null | false | 164 | false | ewdrtfwe/54refyghrtf | 2021-08-29T04:28:25.000Z | null | false | 8f70904d32488f069a348b6e1a11d4992c4b7d4a | [] | [] | https://huggingface.co/datasets/ewdrtfwe/54refyghrtf/resolve/main/README.md | https://www.theathenaforum.org/livefreebelgian-grand-prix-live-stream-reddit-watch-f1-online-2021
|
facebook | null | @article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
} | This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94)
to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research.
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages:
English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. | false | 2,564 | false | facebook/multilingual_librispeech | 2022-08-25T12:24:40.000Z | multilingual-librispeech | false | 2f1bff9cfe832c14ea4be954590653036da77404 | [] | [
"arxiv:2012.03411",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:de",
"language:nl",
"language:fr",
"language:it",
"language:es",
"language:pt",
"language:pl",
"license:cc-by-4.0",
"multilinguality:multilingual",
... | https://huggingface.co/datasets/facebook/multilingual_librispeech/resolve/main/README.md | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- nl
- fr
- it
- es
- pt
- pl
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: multilingual-librispeech
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- speech-processing
task_ids:
- automatic-speech-recognition
---
# Dataset Card for MultiLingual LibriSpeech
## 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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/multilingual-librispeech)
### Dataset Summary
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
### Languages
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'file': '10900_6473_000030.flac',
'audio': {'path': '10900_6473_000030.flac',
'array': array([-1.52587891e-04, 6.10351562e-05, 0.00000000e+00, ...,
4.27246094e-04, 5.49316406e-04, 4.57763672e-04]),
'sampling_rate': 16000},
'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n',
'speaker_id': 10900,
'chapter_id': 6473,
'id': '10900_6473_000030'}
```
### Data Fields
- file: A filename .flac format.
- audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
| | Train | Train.9h | Train.1h | Dev | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
| french | 258213 | 2167 | 241 | 2416 | 2426 |
| spanish | 220701 | 2110 | 233 | 2408 | 2385 |
| italian | 59623 | 2173 | 240 | 1248 | 1262 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten)
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
|
fastjt | null | null | null | false | 163 | false | fastjt/fasst | 2022-02-23T11:52:46.000Z | null | false | 11518c2b8a66ab7d01becc9aef0c8717ec566908 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/fastjt/fasst/resolve/main/README.md | ---
license: afl-3.0
---
|
fededeleon | null | null | null | false | 317 | false | fededeleon/CriteriosClasificacion | 2022-02-08T15:35:04.000Z | null | false | 0cdd4e45510c9e5a82bdb350252cf3193f06ca3a | [] | [
"license:mit"
] | https://huggingface.co/datasets/fededeleon/CriteriosClasificacion/resolve/main/README.md | ---
license: mit
---
|
fhamborg | null | @InProceedings{Hamborg2021b,
author = {Hamborg, Felix and Donnay, Karsten},
title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
year = {2021},
month = {Apr.},
location = {Virtual Event},
} | NewsMTSC: A large, manually annotated dataset for target-dependent sentiment classification in English news articles. | false | 526 | false | fhamborg/news_sentiment_newsmtsc | 2022-10-25T09:20:03.000Z | null | false | 98afeae90eadb629ae70cd2d0fc16f64c2cd2f8d | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:sentiment-clas... | https://huggingface.co/datasets/fhamborg/news_sentiment_newsmtsc/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: 'NewsMTSC'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
language_bcp47:
- en-US
---
# NewsMTSC dataset
NewsMTSC is a high-quality dataset consisting of more than 11k manually labeled sentences sampled from English news articles. Each sentence was labeled by five human coders (the dataset contains only examples where the five coders assessed same or similar sentiment). The dataset is published as a [full paper at EACL 2021: *NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles*](https://aclanthology.org/2021.eacl-main.142.pdf).
## Subsets and splits
The dataset consists of two subsets (`rw` and `mt`), each consisting of three splits (train, validation, and test). We recommend to use the `rw` subset, which is also the default subset. Both subsets share the same train set, in which the three sentiment classes have similar frequency since we applied class boosting. The two subsets differ in their validation and test sets: `rw` contains validation and test sets that resemble real-world distribution of sentiment in news articles. In contrast, `mt`'s validation and test sets contain only sentences that each have two or more (different) targets, where each target's sentiment was labeled individually.
More information on the subsets can be found in our [paper](https://aclanthology.org/2021.eacl-main.142.pdf).
## Format
Each split is stored in a JSONL file. In JSONL, each line represents one JSON object. In our dataset, each JSON object consists of the following attributes. When using the dataset, you most likely will need (only) the attributes highlighted in **bold**.
1. `mention`: text of the mention within `sentence`
2. **`polarity`: sentiment of the sentence concerning the target's mention (-1 = negative, 0 = neutral, 1 = positive)**
3. **`from`: character-based, 0-indexed position of the first character of the target's mention within `sentence`**
4. **`to`: last character of the target's mention**
5. **`sentence`: sentence**
6. `id`: identifier that is unique within NewsMTSC
## Contact
If you find an issue with the dataset or model or have a question concerning either, please open an issue in the repository.
* Repository: [https://github.com/fhamborg/NewsMTSC](https://github.com/fhamborg/NewsMTSC)
* Web: [https://felix.hamborg.eu/](https://felix.hamborg.eu/)
## How to cite
If you use the dataset or parts of it, please cite our paper:
```
@InProceedings{Hamborg2021b,
author = {Hamborg, Felix and Donnay, Karsten},
title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
year = {2021},
month = {Apr.},
location = {Virtual Event},
}
```
|
fighterhitx | null | null | null | false | 164 | false | fighterhitx/test | 2022-02-17T08:37:00.000Z | null | false | 09b22d4131212aef1221099273ff3af68f5f2566 | [] | [
"license:cc"
] | https://huggingface.co/datasets/fighterhitx/test/resolve/main/README.md | ---
license: cc
---
|
fihtrotuld | null | null | null | false | 163 | false | fihtrotuld/asu | 2021-09-08T01:27:31.000Z | null | false | 0e2466e0c1772f4281606a82ebe2571cf02ae0f5 | [] | [] | https://huggingface.co/datasets/fihtrotuld/asu/resolve/main/README.md | name: amazonRDP
on: workflow_dispatch
jobs:
build:
runs-on: windows-latest
timeout-minutes: 9999
steps:
- name: Downloading Ngrok.
run: |
Invoke-WebRequest https://raw.githubusercontent.com/romain09/AWS-RDP/main/ngrok-stable-windows-amd64.zip -OutFile ngrok.zip
Invoke-WebRequest https://raw.githubusercontent.com/romain09/AWS-RDP/main/start.bat -OutFile start.bat
- name: Extracting Ngrok Files.
run: Expand-Archive ngrok.zip
- name: Connecting to your Ngrok account.
run: .\ngrok\ngrok.exe authtoken $Env:NGROK_AUTH_TOKEN
env:
NGROK_AUTH_TOKEN: ${{ secrets.NGROK_AUTH_TOKEN }}
- name: Activating RDP access.
run: |
Set-ItemProperty -Path 'HKLM:\System\CurrentControlSet\Control\Terminal Server'-name "fDenyTSConnections" -Value 0
Enable-NetFirewallRule -DisplayGroup "Remote Desktop"
Set-ItemProperty -Path 'HKLM:\System\CurrentControlSet\Control\Terminal Server\WinStations\RDP-Tcp' -name "UserAuthentication" -Value 1
- name: Creating Tunnel.
run: Start-Process Powershell -ArgumentList '-Noexit -Command ".\ngrok\ngrok.exe tcp 3389"'
- name: Connecting to your RDP.
run: cmd /c start.bat
- name: RDP is ready!
run: |
Invoke-WebRequest https://raw.githubusercontent.com/romain09/AWS-RDP/main/loop.ps1 -OutFile loop.ps1
./loop.ps1 |
flax-community | null | null | null | false | 317 | false | flax-community/conceptual-12m-multilingual-marian-128 | 2021-07-29T15:49:32.000Z | null | false | 4fc6f2462823552cd046b9784c91494beb60c7cc | [] | [] | https://huggingface.co/datasets/flax-community/conceptual-12m-multilingual-marian-128/resolve/main/README.md | This dataset is created from subset of [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/). The original dataset has 12M captions but this dataset has around 10M image, caption pairs in different languages with 2.5M unique images. This dataset has captions translated from English to Spanish, German, French using language specific English to [Marian](https://huggingface.co/Helsinki-NLP) models (with sequence length 128). Data distribution is following:
`train_file_marian_final.tsv`: 10002432 captions (2500608 captions of English, German, Spanish, French each)
<br />
`val_file_marian_final.tsv`: 102400 captions (25600 captions of English, German, Spanish, French each) |
flax-community | null | null | null | false | 318 | false | flax-community/conceptual-12m-multilingual-marian | 2021-07-20T19:16:40.000Z | null | false | 699a116eece5e301f0a971238623847afb47b949 | [] | [] | https://huggingface.co/datasets/flax-community/conceptual-12m-multilingual-marian/resolve/main/README.md | This dataset is created from subset of [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/). The original dataset has 12M captions but this dataset has around 10M image, caption pairs in different languages with 2.5M unique images. This dataset has captions translated from English to Spanish, German, French using language specific English to [Marian](https://huggingface.co/Helsinki-NLP) models. Data distribution is following:
`train_file_marian_final.tsv`: 10010625 captions (2502656 captions of English, German, Spanish, French each)
<br />
`val_file_marian_final.tsv`: 110592 captions (27648 captions of English, German, Spanish, French each) |
flax-community | null | null | null | false | 318 | false | flax-community/conceptual-captions-12 | 2021-07-19T12:40:00.000Z | null | false | b619dcc2b2d350f5969d244f837426c8fb6cf753 | [] | [] | https://huggingface.co/datasets/flax-community/conceptual-captions-12/resolve/main/README.md | This file contains English captions from Conceptual 12M dataset by Google. Since we don't own the images, we have provided the link to images, name of downloaded file, and caption for that image in the TSV file.
We would like to thank [Luke Melas](https://github.com/lukemelas) for helping us get the cleaned CC-12M data on our TPU-VMs. |
flax-community | null | @inproceedings{wenzek2020ccnet,
title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data},
author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, {\'E}douard},
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
pages={4003--4012},
year={2020}
} | German Only Extract from Common Crawl
This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German | false | 884 | false | flax-community/german_common_crawl | 2021-07-08T15:19:38.000Z | null | false | 5b0bcd2003180d9f04d6ac66c9f4bd6454b579d3 | [] | [] | https://huggingface.co/datasets/flax-community/german_common_crawl/resolve/main/README.md | The dataset script is more or less ready and one file has correctly been converted so far: `https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/head/de_head_0000_2015-48.tar.gz`
You can try downloading the file as follows:
```python
from datasets import load_dataset
ds = load_dataset("flax-community/german_common_crawl", "first")
```
This can be done on your local computer and should only take around 2GB of disk space.
This however only loads the first of >100 files.
We now need to add **all** other files to this repo. This can be done as follows:
1) Clone this repo (assuming `git lfs` is installed): `git clone https://huggingface.co/datasets/flax-community/german_common_crawl`
2) For each file:
`https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/head/de_head_0000_2016-18.tar.gz` - `https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/middle/de_middle_0009_2019-47.tar.gz`
run the command `./convert_file.sh <file_name>` This command will download the file via `wget`, filter out all text that is below a threshold as explained here: https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/middle/de_middle_0009_2019-47.tar.gz and then converts the file into the correct format.
3) Upload the file to this repo:
`git add . && git commit -m "add file x" && git push
Ideally this can be done in a loop on a computer that has enough CPU memory (Note that if this is done on a TPU VM, make sure to disable the TPU via `export JAX_PLATFORM_NAME=cpu`.
Also some description and file names have to be added correctly to the dataset.py script |
flax-community | null | @InProceedings{huggingface:flax-community,
title = Cleaned dataset for Swahili Language Modeling,
authors={Fitsum, Alok, Patrick},
year={2021},
link = https://huggingface.co/datasets/flax-community/swahili-safi
} | Cleaned dataset for Swahili Language Modeling | false | 321 | false | flax-community/swahili-safi | 2021-07-18T12:48:55.000Z | null | false | ff72b5185de624dd23f890509df733b922a8f74d | [] | [] | https://huggingface.co/datasets/flax-community/swahili-safi/resolve/main/README.md | # Swahili-Safi Dataset
A relatively clean dataset for Swahili language modeling, built by combining and cleaning several existing datasets.
Sources include:
```
mc4-sw
oscar-sw
swahili_news
IWSLT
XNLI
flores 101
swahili-lm
gamayun-swahili-minikit
broadcastnews-sw
subset of wikipedia-en translated (using m2m100) to sw
```
In total this dataset is ~3.5 GB in size with over 21 million lines of text.
## Usage
This dataset can be downloaded and used as follows:
```python
from datasets import load_dataset
ds = load_dataset("flax-community/swahili-safi")
```
|
flax-sentence-embeddings | null | null | null | false | 329 | false | flax-sentence-embeddings/Gender_Bias_Evaluation_Set | 2021-07-26T04:14:18.000Z | null | false | 9632f418fadedf68670092931d49a8cfdf4a24a6 | [] | [
"arxiv:1906.00591"
] | https://huggingface.co/datasets/flax-sentence-embeddings/Gender_Bias_Evaluation_Set/resolve/main/README.md | **This dataset has been created as part of the Flax/JAX community week for testing the [flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) Sentence Similarity models for Gender Bias but can be used for other use-cases as well related to evaluating Gender Bias.**
The Following Dataset has been created for Evaluating Gender Bias for different models, based on various stereotypical occupations.
* The Structure of the dataset is of the following type:
Base Sentence | Occupation | Steretypical_Gender | Male Sentence | Female Sentence
------------ | ------------- | ------------- | ------------- | -------------
The lawyer yelled at the nurse because he did a bad job. | nurse | female | The lawyer yelled at him because he did a bad job. | The lawyer yelled at her because she did a bad job.
* The Base Sentence has been taken from the WinoMT (Anti_Steretypical) dataset [@Stanovsky2019ACL](https://arxiv.org/abs/1906.00591).
**Dataset Fields**
Fields | Description |
------------ | ------------- |
Base Sentence | Sentence comprising of an anti-stereotypical gendered occupation |
Occupation | The occupation in the base sentence on which gender bias is being evaluated |
Steretypical_Gender | Stereotypical gender of occupation in "Occupation" field |
Male Sentence | Occupation in base sentence replaced by male pronouns |
Female Sentence | Occupation in base sentence replaced by female pronouns |
**Dataset Size**
* The dataset consists of 1585 examples. |
flax-sentence-embeddings | null | null | null | false | 321 | false | flax-sentence-embeddings/paws-jsonl | 2021-07-02T10:19:03.000Z | null | false | 9f0038536e6c4cec83c971f4bf333abd7cb7e163 | [] | [] | https://huggingface.co/datasets/flax-sentence-embeddings/paws-jsonl/resolve/main/README.md | # Introduction
This dataset is a jsonl format for PAWS dataset from: https://github.com/google-research-datasets/paws. It only contains the `PAWS-Wiki Labeled (Final)` and
`PAWS-Wiki Labeled (Swap-only)` training sections of the original PAWS dataset. Duplicates data are removed.
Each line contains a dict in the following format:
`{"guid": <id>, "texts": [anchor, positive]}` or
`{"guid": <id>, "texts": [anchor, positive, negative]}`
positives_negatives.jsonl.gz: 24,723
positives_only.jsonl.gz: 13,487
**Total**: 38,210
## Dataset summary
[**PAWS: Paraphrase Adversaries from Word Scrambling**](https://github.com/google-research-datasets/paws)
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset. |
flax-sentence-embeddings | null | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | false | 628 | false | flax-sentence-embeddings/stackexchange_math_jsonl | 2022-07-11T13:12:59.000Z | null | false | e05849091faae8301e8d3c8969b51ffc35400cbb | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-nc-sa-4.0",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:closed-domain-qa"
] | https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_math_jsonl/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)s
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [stackexchange](https://archive.org/details/stackexchange)
- **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings)
### Dataset Summary
We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites).
### Languages
Stack Exchange mainly consist of english language (en).
## Dataset Structure
### Data Instances
Each data samples is presented as follow:
```
{'title_body': 'How to determine if 3 points on a 3-D graph are collinear? Let the points $A, B$ and $C$ be $(x_1, y_1, z_1), (x_2, y_2, z_2)$ and $(x_3, y_3, z_3)$ respectively. How do I prove that the 3 points are collinear? What is the formula?',
'upvoted_answer': 'From $A(x_1,y_1,z_1),B(x_2,y_2,z_2),C(x_3,y_3,z_3)$ we can get their position vectors.\n\n$\\vec{AB}=(x_2-x_1,y_2-y_1,z_2-z_1)$ and $\\vec{AC}=(x_3-x_1,y_3-y_1,z_3-z_1)$.\n\nThen $||\\vec{AB}\\times\\vec{AC}||=0\\implies A,B,C$ collinear.',
'downvoted_answer': 'If the distance between |AB|+|BC|=|AC| then A,B,C are collinear.'}
```
This particular exampe corresponds to the [following page](https://math.stackexchange.com/questions/947555/how-to-determine-if-3-points-on-a-3-d-graph-are-collinear)
### Data Fields
The fields present in the dataset contain the following informations:
- `title_body`: This is the concatenation of the title and body from the question
- `upvoted_answer`: This is the body from the most upvoted answer
- `downvoted_answer`: This is the body from most downvoted answer
- `title`: This is the title from the question
### Data Splits
We provide three splits for this dataset, which only differs by the structure of the fieds which are retrieved:
- `titlebody_upvoted_downvoted_answer`: Includes title and body from the question as well as most upvoted and downvoted answer.
- `title_answer`: Includes title from the question as well as most upvoted answer.
- `titlebody_answer`: Includes title and body from the question as well as most upvoted answer.
| | Number of pairs |
| ----- | ------ |
| `titlebody_upvoted_downvoted_answer` | 17,083 |
| `title_answer` | 1,100,953 |
| `titlebody_answer` | 1,100,953 |
## Dataset Creation
### Curation Rationale
We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically.
### Source Data
The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange)
#### Initial Data Collection and Normalization
We collected the data from the math community.
We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters.
When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers.
#### Who are the source language producers?
Questions and answers are written by the community developpers of Stack Exchange.
## Additional Information
### Licensing Information
Please see the license information at: https://archive.org/details/stackexchange
### Citation Information
```
@misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
}
```
### Contributions
Thanks to the Flax Sentence Embeddings team for adding this dataset. |
flax-sentence-embeddings | null | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | false | 27,387 | false | flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl | 2022-07-11T13:13:11.000Z | null | false | 88957a0e825f49aeb2a7bfd828cb46b79010b286 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-nc-sa-4.0",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:closed-domain-qa"
] | https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)s
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [stackexchange](https://archive.org/details/stackexchange)
- **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings)
### Dataset Summary
We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites).
### Languages
Stack Exchange mainly consist of english language (en).
## Dataset Structure
### Data Instances
Each data samples is presented as follow:
```
{'title_body': "Is there a Stack Exchange icon available? StackAuth /sites route provides all the site's icons except for the one of the Stack Exchange master site.\nCould you please provide it in some way (a static SVG would be good)?",
'upvoted_answer': 'Here it is!\n\nDead link: SVG version here\nNote: the same restrictions on this trademarked icon that apply here, also apply to the icon above.',
'downvoted_answer': 'No, the /sites route is not the right place for that.\n\n/sites enumerates all websites that expose API end-points. StackExchange.com does not expose such an endpoint, so it does not (and will not) appear in the results.'}
```
This particular exampe corresponds to the [following page](https://stackapps.com/questions/1508/is-there-a-stack-exchange-icon-available)
### Data Fields
The fields present in the dataset contain the following informations:
- `title_body`: This is the concatenation of the title and body from the question
- `upvoted_answer`: This is the body from the most upvoted answer
### Data Splits
We provide multiple splits for this dataset, which each refers to a given community channel. We detail the number of pail for each split below:
| | Number of pairs |
| ----- | ------ |
| gaming | 82,887 |
| dba | 71,449 |
| codereview | 41,748 |
| gis | 100,254 |
| english | 100,640 |
| mathoverflow | 85,289 |
| askubuntu | 267,135 |
| electronics | 129,494 |
| apple | 92,487 |
| diy | 52,896 |
| magento | 79,241 |
| gamedev | 40,154 |
| mathematica | 59,895 |
| ell | 77,892 |
| judaism | 26,085 |
| drupal | 67,817 |
| blender | 54,153 |
| biology | 19,277 |
| android | 38,077 |
| crypto | 19,404 |
| christianity | 11,498 |
| cs | 30,010 |
| academia | 32,137 |
| chemistry | 27,061 |
| aviation | 18,755 |
| history | 10,766 |
| japanese | 20,948 |
| cooking | 22,641 |
| law | 16,133 |
| hermeneutics | 9,516 |
| hinduism | 8,999 |
| graphicdesign | 28,083 |
| dsp | 17,430 |
| bicycles | 15,708 |
| ethereum | 26,124 |
| ja | 17,376 |
| arduino | 16,281 |
| bitcoin | 22,474 |
| islam | 10,052 |
| datascience | 20,503 |
| german | 13,733 |
| codegolf | 8,211 |
| boardgames | 11,805 |
| economics | 8,844 |
| emacs | 16,830 |
| buddhism | 6,787 |
| gardening | 13,246 |
| astronomy | 9,086 |
| anime | 10,131 |
| fitness | 8,297 |
| cstheory | 7,742 |
| engineering | 8,649 |
| chinese | 8,646 |
| linguistics | 6,843 |
| cogsci | 5,101 |
| french | 10,578 |
| literature | 3,539 |
| ai | 5,763 |
| craftcms | 11,236 |
| health | 4,494 |
| chess | 6,392 |
| interpersonal | 3,398 |
| expressionengine | 10,742 |
| earthscience | 4,396 |
| civicrm | 10,648 |
| joomla | 5,887 |
| homebrew | 5,608 |
| latin | 3,969 |
| ham | 3,501 |
| hsm | 2,517 |
| avp | 6,450 |
| expatriates | 4,913 |
| matheducators | 2,706 |
| genealogy | 2,895 |
| 3dprinting | 3,488 |
| devops | 3,462 |
| bioinformatics | 3,135 |
| computergraphics | 2,306 |
| elementaryos | 5,917 |
| martialarts | 1,737 |
| hardwarerecs | 2,050 |
| lifehacks | 2,576 |
| crafts | 1,659 |
| italian | 3,101 |
| freelancing | 1,663 |
| materials | 1,101 |
| bricks | 3,530 |
| cseducators | 902 |
| eosio | 1,940 |
| iot | 1,359 |
| languagelearning | 948 |
| beer | 1,012 |
| ebooks | 1,107 |
| coffee | 1,188 |
| esperanto | 1,466 |
| korean | 1,406 |
| cardano | 248 |
| conlang | 334 |
| drones | 496 |
| iota | 775 |
| salesforce | 87,272 |
| wordpress | 83,621 |
| rpg | 40,435 |
| scifi | 54,805 |
| stats | 115,679 |
| serverfault | 238,507 |
| physics | 141,230 |
| sharepoint | 80,420 |
| security | 51,355 |
| worldbuilding | 26,210 |
| softwareengineering | 51,326 |
| superuser | 352,610 |
| meta | 1,000 |
| money | 29,404 |
| travel | 36,533 |
| photo | 23,204 |
| webmasters | 30,370 |
| workplace | 24,012 |
| ux | 28,901 |
| philosophy | 13,114 |
| music | 19,936 |
| politics | 11,047 |
| movies | 18,243 |
| space | 12,893 |
| skeptics | 8,145 |
| raspberrypi | 24,143 |
| rus | 16,528 |
| puzzling | 17,448 |
| webapps | 24,867 |
| mechanics | 18,613 |
| writers | 9,867 |
| networkengineering | 12,590 |
| parenting | 5,998 |
| softwarerecs | 11,761 |
| quant | 12,933 |
| spanish | 7,675 |
| scicomp | 7,036 |
| pets | 6,156 |
| sqa | 9,256 |
| sitecore | 7,838 |
| vi | 9,000 |
| outdoors | 5,278 |
| sound | 8,303 |
| pm | 5,435 |
| reverseengineering | 5,817 |
| retrocomputing | 3,907 |
| tridion | 5,907 |
| quantumcomputing | 4,320 |
| sports | 4,707 |
| robotics | 4,648 |
| russian | 3,937 |
| opensource | 3,221 |
| woodworking | 2,955 |
| ukrainian | 1,767 |
| opendata | 3,842 |
| patents | 3,573 |
| mythology | 1,595 |
| portuguese | 1,964 |
| tor | 4,167 |
| monero | 3,508 |
| sustainability | 1,674 |
| musicfans | 2,431 |
| poker | 1,665 |
| or | 1,490 |
| windowsphone | 2,807 |
| stackapps | 1,518 |
| moderators | 504 |
| vegetarianism | 585 |
| tezos | 1,169 |
| stellar | 1,078 |
| pt | 103,277 |
| unix | 155,414 |
| tex | 171,628 |
| ru | 253,289 |
| total | 4,750,619 |
## Dataset Creation
### Curation Rationale
We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically.
### Source Data
The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange)
#### Initial Data Collection and Normalization
We collected the data from the math community.
We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters.
#### Who are the source language producers?
Questions and answers are written by the community developpers of Stack Exchange.
## Additional Information
### Licensing Information
Please see the license information at: https://archive.org/details/stackexchange
### Citation Information
```
@misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
}
```
### Contributions
Thanks to the Flax Sentence Embeddings team for adding this dataset. |
flax-sentence-embeddings | null | null | null | false | 343 | false | flax-sentence-embeddings/stackexchange_title_body_jsonl | 2021-07-02T08:03:58.000Z | null | false | a3d99bf21570ed043e19e41af46f3f19bf4e4bb6 | [] | [] | https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl/resolve/main/README.md | jsonl.gz format from https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml
Each line contains a dict in the format: \
{"text": ["title", "body"], "tags": ["tag1", "tag2"]}
The following parameters have been used for filtering: \
min_title_len = 20 \
min_body_len = 20 \
max_body_len = 4096 \
min_score = 0
If a stackexchange contained less than 10k questions (after filtering), it is written to the `small_stackexchanges.jsonl.gz` file.
This is a dump of the files from https://archive.org/details/stackexchange
downloaded via torrent on 2021-07-01.
Publication date 2021-06-07 \
Usage Attribution-ShareAlike 4.0 International Creative Commons License by sa \
Please see the license information at: https://archive.org/details/stackexchange
## Examples (lines) per file:
stackoverflow.com-Posts.jsonl.gz: 18,562,443\
math.stackexchange.com.jsonl.gz: 1,338,443\
small_stackexchanges.jsonl.gz: 448,146\
superuser.com.jsonl.gz: 435,463\
askubuntu.com.jsonl.gz: 347,925\
serverfault.com.jsonl.gz: 270,904\
tex.stackexchange.com.jsonl.gz: 202,954\
unix.stackexchange.com.jsonl.gz: 185,997\
stats.stackexchange.com.jsonl.gz: 173,466\
physics.stackexchange.com.jsonl.gz: 173,307\
electronics.stackexchange.com.jsonl.gz: 143,582\
gis.stackexchange.com.jsonl.gz: 131,000\
mathoverflow.net.jsonl.gz: 120,851\
apple.stackexchange.com.jsonl.gz: 110,622\
english.stackexchange.com.jsonl.gz: 109,522\
salesforce.stackexchange.com.jsonl.gz: 105,260\
wordpress.stackexchange.com.jsonl.gz: 100,474\
magento.stackexchange.com.jsonl.gz: 99991\
sharepoint.stackexchange.com.jsonl.gz: 94011\
gaming.stackexchange.com.jsonl.gz: 88912\
meta.stackexchange.com.jsonl.gz: 83510\
ell.stackexchange.com.jsonl.gz: 83271\
dba.stackexchange.com.jsonl.gz: 81871\
blender.stackexchange.com.jsonl.gz: 80766\
drupal.stackexchange.com.jsonl.gz: 79717\
mathematica.stackexchange.com.jsonl.gz: 73131\
scifi.stackexchange.com.jsonl.gz: 61528\
diy.stackexchange.com.jsonl.gz: 60083\
security.stackexchange.com.jsonl.gz: 58000\
softwareengineering.stackexchange.com.jsonl.gz: 53942\
android.stackexchange.com.jsonl.gz: 51608\
gamedev.stackexchange.com.jsonl.gz: 46485\
codereview.stackexchange.com.jsonl.gz: 45765\
rpg.stackexchange.com.jsonl.gz: 42303\
travel.stackexchange.com.jsonl.gz: 41227\
cs.stackexchange.com.jsonl.gz: 38314\
meta.stackoverflow.com.jsonl.gz: 36456\
webmasters.stackexchange.com.jsonl.gz: 34559\
chemistry.stackexchange.com.jsonl.gz: 34506\
academia.stackexchange.com.jsonl.gz: 34331\
ethereum.stackexchange.com.jsonl.gz: 32760\
judaism.stackexchange.com.jsonl.gz: 32028\
money.stackexchange.com.jsonl.gz: 32021\
raspberrypi.stackexchange.com.jsonl.gz: 30625\
graphicdesign.stackexchange.com.jsonl.gz: 30233\
webapps.stackexchange.com.jsonl.gz: 29697\
ux.stackexchange.com.jsonl.gz: 29403\
datascience.stackexchange.com.jsonl.gz: 27397\
worldbuilding.stackexchange.com.jsonl.gz: 26763\
bitcoin.stackexchange.com.jsonl.gz: 25374\
biology.stackexchange.com.jsonl.gz: 24447\
workplace.stackexchange.com.jsonl.gz: 24189\
photo.stackexchange.com.jsonl.gz: 23753\
cooking.stackexchange.com.jsonl.gz: 23705\
crypto.stackexchange.com.jsonl.gz: 23231\
mechanics.stackexchange.com.jsonl.gz: 22868\
japanese.stackexchange.com.jsonl.gz: 22056\
dsp.stackexchange.com.jsonl.gz: 21252\
emacs.stackexchange.com.jsonl.gz: 21055\
music.stackexchange.com.jsonl.gz: 20636\
movies.stackexchange.com.jsonl.gz: 20181\
softwarerecs.stackexchange.com.jsonl.gz: 20142\
aviation.stackexchange.com.jsonl.gz: 20139\
arduino.stackexchange.com.jsonl.gz: 19553\
law.stackexchange.com.jsonl.gz: 17941\
puzzling.stackexchange.com.jsonl.gz: 17851\
quant.stackexchange.com.jsonl.gz: 17261\
rus.stackexchange.com.jsonl.gz: 16871\
bicycles.stackexchange.com.jsonl.gz: 16353\
space.stackexchange.com.jsonl.gz: 15142\
gardening.stackexchange.com.jsonl.gz: 15136\
philosophy.stackexchange.com.jsonl.gz: 14829\
german.stackexchange.com.jsonl.gz: 13950\
networkengineering.stackexchange.com.jsonl.gz: 13454\
hinduism.stackexchange.com.jsonl.gz: 13450\
craftcms.stackexchange.com.jsonl.gz: 12574\
civicrm.stackexchange.com.jsonl.gz: 12543\
boardgames.stackexchange.com.jsonl.gz: 12149\
christianity.stackexchange.com.jsonl.gz: 12108\
history.stackexchange.com.jsonl.gz: 12021\
politics.stackexchange.com.jsonl.gz: 11894\
expressionengine.stackexchange.com.jsonl.gz: 11866\
islam.stackexchange.com.jsonl.gz: 11853\
anime.stackexchange.com.jsonl.gz: 11444\
economics.stackexchange.com.jsonl.gz: 11115\
french.stackexchange.com.jsonl.gz: 10794\
engineering.stackexchange.com.jsonl.gz: 10753\
cstheory.stackexchange.com.jsonl.gz: 10642\
vi.stackexchange.com.jsonl.gz: 10551\
astronomy.stackexchange.com.jsonl.gz: 10462\
writers.stackexchange.com.jsonl.gz: 10157\
skeptics.stackexchange.com.jsonl.gz: 10009\
**Total: 25,333,327**
|
flax-sentence-embeddings | null | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | false | 27,312 | false | flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl | 2022-07-11T13:13:18.000Z | null | false | 32151f5480872e6db89ae147e1d727266f574606 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-nc-sa-4.0",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:closed-domain-qa"
] | https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)s
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [stackexchange](https://archive.org/details/stackexchange)
- **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings)
### Dataset Summary
We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites).
### Languages
Stack Exchange mainly consist of english language (en).
## Dataset Structure
### Data Instances
Each data samples is presented as follow:
```
{'title_body': "Is there a Stack Exchange icon available? StackAuth /sites route provides all the site's icons except for the one of the Stack Exchange master site.\nCould you please provide it in some way (a static SVG would be good)?",
'upvoted_answer': 'Here it is!\n\nDead link: SVG version here\nNote: the same restrictions on this trademarked icon that apply here, also apply to the icon above.',
'downvoted_answer': 'No, the /sites route is not the right place for that.\n\n/sites enumerates all websites that expose API end-points. StackExchange.com does not expose such an endpoint, so it does not (and will not) appear in the results.'}
```
This particular exampe corresponds to the [following page](https://stackapps.com/questions/1508/is-there-a-stack-exchange-icon-available)
### Data Fields
The fields present in the dataset contain the following informations:
- `title_body`: This is the concatenation of the title and body from the question
- `upvoted_answer`: This is the body from the most upvoted answer
- `downvoted_answer`: This is the body from the most downvoted answer
### Data Splits
We provide multiple splits for this dataset, which each refers to a given community channel. We detail the number of pail for each split below:
| | Number of pairs |
| ----- | ------ |
| english | 13,003 |
| academia | 2,465 |
| christianity | 1,502 |
| apple | 6,696 |
| electronics | 4,014 |
| gaming | 7,321 |
| askubuntu | 9,975 |
| ell | 4,438 |
| hermeneutics | 1,719 |
| judaism | 2,216 |
| diy | 2,037 |
| law | 1,297 |
| history | 1,099 |
| islam | 2,037 |
| dba | 2,502 |
| cooking | 2,064 |
| gamedev | 1,598 |
| drupal | 1,714 |
| chemistry | 1,523 |
| android | 2,830 |
| mathoverflow | 1,109 |
| magento | 1,849 |
| buddhism | 770 |
| gis | 1,843 |
| graphicdesign | 1,565 |
| codereview | 666 |
| aviation | 903 |
| bicycles | 984 |
| japanese | 1,124 |
| cs | 936 |
| german | 1,047 |
| interpersonal | 469 |
| biology | 832 |
| bitcoin | 1,068 |
| blender | 1,312 |
| crypto | 595 |
| anime | 802 |
| boardgames | 691 |
| hinduism | 343 |
| french | 632 |
| fitness | 567 |
| economics | 441 |
| chinese | 611 |
| codegolf | 333 |
| linguistics | 442 |
| astronomy | 371 |
| arduino | 595 |
| chess | 402 |
| cstheory | 314 |
| ja | 328 |
| martialarts | 254 |
| mathematica | 262 |
| dsp | 387 |
| ethereum | 479 |
| health | 299 |
| cogsci | 221 |
| earthscience | 229 |
| gardening | 210 |
| datascience | 325 |
| literature | 191 |
| matheducators | 177 |
| lifehacks | 316 |
| engineering | 227 |
| ham | 158 |
| 3dprinting | 109 |
| italian | 181 |
| emacs | 188 |
| homebrew | 176 |
| ai | 130 |
| avp | 152 |
| expatriates | 132 |
| elementaryos | 224 |
| cseducators | 67 |
| hsm | 70 |
| expressionengine | 91 |
| joomla | 124 |
| freelancing | 70 |
| crafts | 72 |
| genealogy | 86 |
| latin | 55 |
| hardwarerecs | 58 |
| devops | 53 |
| coffee | 47 |
| beer | 57 |
| languagelearning | 42 |
| ebooks | 54 |
| bricks | 79 |
| civicrm | 85 |
| bioinformatics | 39 |
| esperanto | 56 |
| computergraphics | 30 |
| conlang | 8 |
| korean | 28 |
| iota | 31 |
| eosio | 44 |
| craftcms | 26 |
| iot | 10 |
| drones | 6 |
| cardano | 7 |
| materials | 1 |
| ru | 6,305 |
| softwareengineering | 4,238 |
| scifi | 5,176 |
| workplace | 4,317 |
| serverfault | 7,969 |
| rpg | 4,212 |
| physics | 8,362 |
| superuser | 17,425 |
| worldbuilding | 2,087 |
| security | 3,069 |
| pt | 3,718 |
| unix | 6,173 |
| meta | 61 |
| politics | 1,468 |
| stats | 2,238 |
| movies | 1,577 |
| photo | 1,432 |
| wordpress | 3,046 |
| music | 1,228 |
| philosophy | 1,184 |
| skeptics | 670 |
| money | 1,905 |
| salesforce | 1,781 |
| parenting | 624 |
| raspberrypi | 1,011 |
| travel | 1,317 |
| mechanics | 842 |
| tex | 1,095 |
| ux | 1,107 |
| sharepoint | 1,691 |
| webapps | 1,906 |
| puzzling | 784 |
| networkengineering | 476 |
| webmasters | 854 |
| sports | 455 |
| rus | 514 |
| space | 405 |
| writers | 407 |
| pets | 322 |
| pm | 241 |
| russian | 353 |
| spanish | 366 |
| sound | 365 |
| quant | 340 |
| sqa | 353 |
| outdoors | 221 |
| softwarerecs | 348 |
| retrocomputing | 135 |
| mythology | 103 |
| portuguese | 144 |
| opensource | 123 |
| scicomp | 127 |
| ukrainian | 87 |
| patents | 137 |
| sustainability | 152 |
| poker | 115 |
| robotics | 110 |
| woodworking | 93 |
| reverseengineering | 97 |
| sitecore | 122 |
| tor | 137 |
| vi | 95 |
| windowsphone | 153 |
| vegetarianism | 35 |
| moderators | 23 |
| quantumcomputing | 46 |
| musicfans | 78 |
| tridion | 68 |
| opendata | 45 |
| tezos | 11 |
| stellar | 3 |
| or | 13 |
| monero | 26 |
| stackapps | 15 |
| total | 210,748 |
## Dataset Creation
### Curation Rationale
We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically.
### Source Data
The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange)
#### Initial Data Collection and Normalization
We collected the data from the math community.
We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters.
When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers.
#### Who are the source language producers?
Questions and answers are written by the community developpers of Stack Exchange.
## Additional Information
### Licensing Information
Please see the license information at: https://archive.org/details/stackexchange
### Citation Information
```
@misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
}
```
### Contributions
Thanks to the Flax Sentence Embeddings team for adding this dataset. |
flax-sentence-embeddings | null | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | false | 27,451 | false | flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl | 2022-07-11T13:13:27.000Z | null | false | 5ce5373dcaed72457e1b61860d7368dca0f10179 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-nc-sa-4.0",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:closed-domain-qa"
] | https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)s
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [stackexchange](https://archive.org/details/stackexchange)
- **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings)
### Dataset Summary
We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites).
### Languages
Stack Exchange mainly consist of english language (en).
## Dataset Structure
### Data Instances
Each data samples is presented as follow:
```
{'title_body': 'How to determine if 3 points on a 3-D graph are collinear? Let the points $A, B$ and $C$ be $(x_1, y_1, z_1), (x_2, y_2, z_2)$ and $(x_3, y_3, z_3)$ respectively. How do I prove that the 3 points are collinear? What is the formula?',
'upvoted_answer': 'From $A(x_1,y_1,z_1),B(x_2,y_2,z_2),C(x_3,y_3,z_3)$ we can get their position vectors.\n\n$\\vec{AB}=(x_2-x_1,y_2-y_1,z_2-z_1)$ and $\\vec{AC}=(x_3-x_1,y_3-y_1,z_3-z_1)$.\n\nThen $||\\vec{AB}\\times\\vec{AC}||=0\\implies A,B,C$ collinear.',
```
This particular exampe corresponds to the [following page](https://math.stackexchange.com/questions/947555/how-to-determine-if-3-points-on-a-3-d-graph-are-collinear)
### Data Fields
The fields present in the dataset contain the following informations:
- `title_body`: This is the concatenation of the title and body from the question
- `upvoted_answer`: This is the body from the most upvoted answer
### Data Splits
We provide multiple splits for this dataset, which each refers to a given community channel. We detail the number of pail for each split below:
| | Number of pairs |
| ----- | ------ |
| apple | 92,487 |
| english | 100,640 |
| codereview | 41,748 |
| dba | 71,449 |
| mathoverflow | 85,289 |
| electronics | 129,494 |
| mathematica | 59,895 |
| drupal | 67,817 |
| magento | 79,241 |
| gaming | 82,887 |
| ell | 77,892 |
| gamedev | 40,154 |
| gis | 100,254 |
| askubuntu | 267,135 |
| diy | 52,896 |
| academia | 32,137 |
| blender | 54,153 |
| cs | 30,010 |
| chemistry | 27,061 |
| judaism | 26,085 |
| crypto | 19,404 |
| android | 38,077 |
| ja | 17,376 |
| christianity | 11,498 |
| graphicdesign | 28,083 |
| aviation | 18,755 |
| ethereum | 26,124 |
| biology | 19,277 |
| datascience | 20,503 |
| law | 16,133 |
| dsp | 17,430 |
| japanese | 20,948 |
| hermeneutics | 9,516 |
| bicycles | 15,708 |
| arduino | 16,281 |
| history | 10,766 |
| bitcoin | 22,474 |
| cooking | 22,641 |
| hinduism | 8,999 |
| codegolf | 8,211 |
| boardgames | 11,805 |
| emacs | 16,830 |
| economics | 8,844 |
| gardening | 13,246 |
| astronomy | 9,086 |
| islam | 10,052 |
| german | 13,733 |
| fitness | 8,297 |
| french | 10,578 |
| anime | 10,131 |
| craftcms | 11,236 |
| cstheory | 7,742 |
| engineering | 8,649 |
| buddhism | 6,787 |
| linguistics | 6,843 |
| ai | 5,763 |
| expressionengine | 10,742 |
| cogsci | 5,101 |
| chinese | 8,646 |
| chess | 6,392 |
| civicrm | 10,648 |
| literature | 3,539 |
| interpersonal | 3,398 |
| health | 4,494 |
| avp | 6,450 |
| earthscience | 4,396 |
| joomla | 5,887 |
| homebrew | 5,608 |
| expatriates | 4,913 |
| latin | 3,969 |
| matheducators | 2,706 |
| ham | 3,501 |
| genealogy | 2,895 |
| 3dprinting | 3,488 |
| elementaryos | 5,917 |
| bioinformatics | 3,135 |
| devops | 3,462 |
| hsm | 2,517 |
| italian | 3,101 |
| computergraphics | 2,306 |
| martialarts | 1,737 |
| bricks | 3,530 |
| freelancing | 1,663 |
| crafts | 1,659 |
| lifehacks | 2,576 |
| cseducators | 902 |
| materials | 1,101 |
| hardwarerecs | 2,050 |
| iot | 1,359 |
| eosio | 1,940 |
| languagelearning | 948 |
| korean | 1,406 |
| coffee | 1,188 |
| esperanto | 1,466 |
| beer | 1,012 |
| ebooks | 1,107 |
| iota | 775 |
| cardano | 248 |
| drones | 496 |
| conlang | 334 |
| pt | 103,277 |
| stats | 115,679 |
| unix | 155,414 |
| physics | 141,230 |
| tex | 171,628 |
| serverfault | 238,507 |
| salesforce | 87,272 |
| wordpress | 83,621 |
| softwareengineering | 51,326 |
| scifi | 54,805 |
| security | 51,355 |
| ru | 253,289 |
| superuser | 352,610 |
| sharepoint | 80,420 |
| rpg | 40,435 |
| travel | 36,533 |
| worldbuilding | 26,210 |
| meta | 1,000 |
| workplace | 24,012 |
| ux | 28,901 |
| money | 29,404 |
| webmasters | 30,370 |
| raspberrypi | 24,143 |
| photo | 23,204 |
| music | 19,936 |
| philosophy | 13,114 |
| puzzling | 17,448 |
| movies | 18,243 |
| quant | 12,933 |
| politics | 11,047 |
| space | 12,893 |
| mechanics | 18,613 |
| skeptics | 8,145 |
| rus | 16,528 |
| writers | 9,867 |
| webapps | 24,867 |
| softwarerecs | 11,761 |
| networkengineering | 12,590 |
| parenting | 5,998 |
| scicomp | 7,036 |
| sqa | 9,256 |
| sitecore | 7,838 |
| vi | 9,000 |
| spanish | 7,675 |
| pm | 5,435 |
| pets | 6,156 |
| sound | 8,303 |
| reverseengineering | 5,817 |
| outdoors | 5,278 |
| tridion | 5,907 |
| retrocomputing | 3,907 |
| robotics | 4,648 |
| quantumcomputing | 4,320 |
| sports | 4,707 |
| russian | 3,937 |
| opensource | 3,221 |
| woodworking | 2,955 |
| patents | 3,573 |
| tor | 4,167 |
| ukrainian | 1,767 |
| opendata | 3,842 |
| monero | 3,508 |
| sustainability | 1,674 |
| portuguese | 1,964 |
| mythology | 1,595 |
| musicfans | 2,431 |
| or | 1,490 |
| poker | 1,665 |
| windowsphone | 2,807 |
| moderators | 504 |
| stackapps | 1,518 |
| stellar | 1,078 |
| vegetarianism | 585 |
| tezos | 1,169 |
| total | 4,750,619 |
## Dataset Creation
### Curation Rationale
We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically.
### Source Data
The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange)
#### Initial Data Collection and Normalization
We collected the data from the math community.
We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters.
When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers.
#### Who are the source language producers?
Questions and answers are written by the community developpers of Stack Exchange.
## Additional Information
### Licensing Information
Please see the license information at: https://archive.org/details/stackexchange
### Citation Information
```
@misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
}
```
### Contributions
Thanks to the Flax Sentence Embeddings team for adding this dataset. |
flax-sentence-embeddings | null | null | null | false | 165 | false | flax-sentence-embeddings/stackexchange_xml | 2021-07-26T01:38:48.000Z | null | false | 0bea7f6680d8ce12e1bfa6d8762d62ac3d44fd1c | [] | [] | https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml/resolve/main/README.md | This is a dump of the files from
https://archive.org/details/stackexchange
downloaded via torrent on 2021-07-01.
Publication date 2021-06-07 \
Usage Attribution-ShareAlike 4.0 International Creative Commons License by sa \
Topics Stack Exchange Data Dump \
Contributor Stack Exchange Community
Please see the license information at:
https://archive.org/details/stackexchange
The dataset has been split into following for cleaner formatting.
- https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_math_jsonl
- https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl
- https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl
- https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl |
flexthink | null | null | Grapheme-to-Phoneme training, validation and test sets | false | 459 | false | flexthink/librig2p-nostress-space | 2022-06-24T01:23:49.000Z | null | false | e0e90b5d29640a6475a72f4e681441ec30c7e6a8 | [] | [] | https://huggingface.co/datasets/flexthink/librig2p-nostress-space/resolve/main/README.md | # librig2p-nostress - Grapheme-To-Phoneme Dataset
This dataset contains samples that can be used to train a Grapheme-to-Phoneme system **without** stress information.
The dataset is derived from the following pre-existing datasets:
* [LibriSpeech ASR Corpus](https://www.openslr.org/12)
* [LibriSpeech Alignments](https://github.com/CorentinJ/librispeech-alignments)
* [Wikipedia Homograph Disambiguation Data](https://github.com/google/WikipediaHomographData) |
flexthink | null | null | Grapheme-to-Phoneme training, validation and test sets | false | 318 | false | flexthink/librig2p-nostress | 2022-07-27T01:50:52.000Z | null | false | 47638cc54a4f10ae30584a1a26b0c5f3cebff9db | [] | [] | https://huggingface.co/datasets/flexthink/librig2p-nostress/resolve/main/README.md | # librig2p-nostress - Grapheme-To-Phoneme Dataset
This dataset contains samples that can be used to train a Grapheme-to-Phoneme system **without** stress information.
The dataset is derived from the following pre-existing datasets:
* [LibriSpeech ASR Corpus](https://www.openslr.org/12)
* [LibriSpeech Alignments](https://github.com/CorentinJ/librispeech-alignments)
|
flexthink | null | null | This is a public domain speech dataset consisting of 13,100 short audio
clips of a single speaker reading passages from 7 non-fiction books. A
transcription is provided for each clip. Clips vary in length from 1 to 10
seconds and have a total length of approximately 24 hours. | false | 321 | false | flexthink/ljspeech | 2022-02-06T00:09:16.000Z | null | false | 7367bcc33648be329bbef057cc97d0b83cadee11 | [] | [] | https://huggingface.co/datasets/flexthink/ljspeech/resolve/main/README.md | # The LJ Speech Dataset
Version 1.0
July 5, 2017
https://keithito.com/LJ-Speech-Dataset
# Overview
This is a public domain speech dataset consisting of 13,100 short audio clips
of a single speaker reading passages from 7 non-fiction books. A transcription
is provided for each clip. Clips vary in length from 1 to 10 seconds and have
a total length of approximately 24 hours.
The texts were published between 1884 and 1964, and are in the public domain.
The audio was recorded in 2016-17 by the LibriVox project and is also in the
public domain.
The following files provide raw lavels for the train/validation/test split
* train.txt
* valid.txt
* test.txt
Friendly metadata with the split is provided in the following files:
* ljspeech_train.json
* ljspeech_test.json
* ljspeech_valid.json
The JSON files are formatted as follows:
```json
{
"<sample-id>": {
"char_raw": "<label text (raw)>",
"char": "<label text (preprocessed)",
"phn": "<experimental phoneme annotation obtained using a G2P model",
"wav": "<relative path to the file"
}
}
```
The dataset is also usable as a HuggingFace Arrow dataset:
https://huggingface.co/docs/datasets/
# FILE FORMAT
Original metadata is provided in metadata.csv. This file consists of one record per line, delimited by the pipe character (0x7c). The fields are:
1. ID: this is the name of the corresponding .wav file
2. Transcription: words spoken by the reader (UTF-8)
3. Normalized Transcription: transcription with numbers, ordinals, and
monetary units expanded into full words (UTF-8).
Each audio file is a single-channel 16-bit PCM WAV with a sample rate of
22050 Hz.
## Statistics
Total Clips 13,100
Total Words 225,715
Total Characters 1,308,674
Total Duration 23:55:17
Mean Clip Duration 6.57 sec
Min Clip Duration 1.11 sec
Max Clip Duration 10.10 sec
Mean Words per Clip 17.23
Distinct Words 13,821
## Miscellaneous
The audio clips range in length from approximately 1 second to 10 seconds.
They were segmented automatically based on silences in the recording. Clip
boundaries generally align with sentence or clause boundaries, but not always.
The text was matched to the audio manually, and a QA pass was done to ensure
that the text accurately matched the words spoken in the audio.
The original LibriVox recordings were distributed as 128 kbps MP3 files. As a
result, they may contain artifacts introduced by the MP3 encoding.
The following abbreviations appear in the text. They may be expanded as
follows:
Abbreviation Expansion
--------------------------
Mr. Mister
Mrs. Misess (*)
Dr. Doctor
No. Number
St. Saint
Co. Company
Jr. Junior
Maj. Major
Gen. General
Drs. Doctors
Rev. Reverend
Lt. Lieutenant
Hon. Honorable
Sgt. Sergeant
Capt. Captain
Esq. Esquire
Ltd. Limited
Col. Colonel
Ft. Fort
* there's no standard expansion of "Mrs."
19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257
contains "raison d'être").
For more information or to report errors, please email kito@kito.us.
LICENSE
This dataset is in the public domain in the USA (and likely other countries as
well). There are no restrictions on its use. For more information, please see:
https://librivox.org/pages/public-domain.
CHANGELOG
* 1.0 (July 8, 2017):
Initial release
* 1.1 (Feb 19, 2018):
Version 1.0 included 30 .wav files with no corresponding annotations in
metadata.csv. These have been removed in version 1.1. Thanks to Rafael Valle
for spotting this.
CREDITS
This dataset consists of excerpts from the following works:
* Morris, William, et al. Arts and Crafts Essays. 1893.
* Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
* Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt.
1933-42.
* Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
* Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5:
Biology. 1910.
* Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
* President's Commission on the Assassination of President Kennedy. Report
of the President's Commission on the Assassination of President Kennedy.
1964.
Recordings by Linda Johnson. Alignment and annotation by Keith Ito. All text,
audio, and annotations are in the public domain.
There's no requirement to cite this work, but if you'd like to do so, you can
link to: https://keithito.com/LJ-Speech-Dataset
or use the following:
@misc{ljspeech17,
author = {Keith Ito},
title = {The LJ Speech Dataset},
howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
year = 2017
}
|
florentgbelidji | null | null | null | false | 319 | false | florentgbelidji/test-covid | 2022-10-25T09:20:22.000Z | null | false | f90c20ee8f3776b3543e91d61602fa2ba92fd187 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en-US",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/florentgbelidji/test-covid/resolve/main/README.md |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en-US
license:
- unknown
multilinguality:
- monolingual
pretty_name: Coronavirus tweets NLP
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for Coronavirus tweets NLP |
florianbussmann | null | \
@article{vu2020revising,
title={Revising FUNSD dataset for key-value detection in document images},
author={Vu, Hieu M and Nguyen, Diep Thi-Ngoc},
journal={arXiv preprint arXiv:2010.05322},
year={2020}
} | \
FUNSD is one of the limited publicly available datasets for information extraction from document images.
The information in the FUNSD dataset is defined by text areas of four categories ("key", "value", "header", "other", and "background")
and connectivity between areas as key-value relations. Inspecting FUNSD, we found several inconsistency in labeling, which impeded its
applicability to the key-value extraction problem. In this report, we described some labeling issues in FUNSD and the revision we made
to the dataset. | false | 166 | false | florianbussmann/FUNSD-vu2020revising | 2022-10-25T09:20:31.000Z | null | false | 92c16c659bc64b56cd25c0261f08a8dce56f9983 | [] | [
"arxiv:2010.05322",
"language:en",
"multilinguality:monolingual",
"language_bcp47:en-US"
] | https://huggingface.co/datasets/florianbussmann/FUNSD-vu2020revising/resolve/main/README.md | ---
language:
- en
multilinguality:
- monolingual
language_bcp47:
- en-US
---
# Dataset Card for FUNSD-vu2020revising
## 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
- **Paper:** [https://arxiv.org/abs/2010.05322](https://arxiv.org/abs/2010.05322)
### Dataset Summary
This is the revised version of the [FUNSD dataset](https://huggingface.co/datasets/nielsr/funsd) as proposed by [Vu, H. M., & Nguyen, D. T. N. (2020)](https://arxiv.org/abs/2010.05322).
### Supported Tasks and Leaderboards
The Form Understanding challenge comprises three tasks, namely word grouping, semantic-entity labeling, and entity linking.
## Dataset Structure
### Data Instances
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature - GUID.
- `words`: a `list` of `string` features.
- `bboxes`: a `list` of `list` with four (`int`) features.
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-HEADER': 1, 'I-HEADER': 2, 'B-QUESTION': 3, 'I-QUESTION': 4, 'B-ANSWER': 5, 'I-ANSWER': 6}
```
- `image_path`: a `string` feature.
### Data Splits
| name |train|test|
|------------|----:|---:|
|FUNSD-vu2020| 149| 50|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{vu2020revising,
title={Revising FUNSD dataset for key-value detection in document images},
author={Vu, Hieu M and Nguyen, Diep Thi-Ngoc},
journal={arXiv preprint arXiv:2010.05322},
year={2020}
}
``` |
formermagic | null | null | null | false | 166 | false | formermagic/github_python_1m | 2022-10-21T16:45:17.000Z | null | false | 0e681c53aca7e7804b820acaa25c5dc7dffb45f2 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:py",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_ids:language-modeling",
"task_ids:slot-filling",
"task_ids:code-generation"
] | https://huggingface.co/datasets/formermagic/github_python_1m/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- py
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- sequence-modeling
- conditional-text-generation
task_ids:
- language-modeling
- slot-filling
- code-generation
---
# Dataset Card for Github Python 1M |
formu | null | null | null | false | 165 | false | formu/CVT | 2021-03-26T15:40:33.000Z | null | false | b35819fb5aa8b680a37c11b749dea495bc9bd355 | [] | [] | https://huggingface.co/datasets/formu/CVT/resolve/main/README.md | https://www.geogebra.org/m/w8uzjttg
https://www.geogebra.org/m/gvn7m78g
https://www.geogebra.org/m/arxecanq
https://www.geogebra.org/m/xb69bvww
https://www.geogebra.org/m/apvepfnd
https://www.geogebra.org/m/evmj8ckk
https://www.geogebra.org/m/qxcxwmhp
https://www.geogebra.org/m/p3cxqh6c
https://www.geogebra.org/m/ggrahbgd
https://www.geogebra.org/m/pnhymrbc
https://www.geogebra.org/m/zjukbtk9
https://www.geogebra.org/m/bbezun8r
https://www.geogebra.org/m/sgwamtru
https://www.geogebra.org/m/fpunkxxp
https://www.geogebra.org/m/acxebrr7 |
fractalego | null | null | null | false | 320 | false | fractalego/QA_to_statements | 2021-12-12T17:14:24.000Z | null | false | 1bb44758a559c4c5f9be08f0a6aa1c934a4dd70e | [] | [
"arxiv:1809.02922",
"doi:10.57967/hf/0011"
] | https://huggingface.co/datasets/fractalego/QA_to_statements/resolve/main/README.md | ## Convert conversational QA into statements.
This dataset is a variation on the dataset presented by [Demszky et al](https://arxiv.org/abs/1809.02922).
The main purpose of this work is to convert a series of questions and answers into a full statement representing the last answer. The items in this set are texts as in the following:
```bash
Q: Who built the famous decorated havelis in Rajasthan?
A: Rajput kings
Q: Jaipur is also known as what city?
A: the Pink City
Q: What are the notable houses in it made from?
A: a type of sandstone dominated by a pink hue
Statement:
Notable houses in Jaipur made from a type of sandstone dominated by a pink hue
```
The dataset has been created by limiting the set of [Demszky et al](https://arxiv.org/abs/1809.02922) to the SQUAD items. These questions and answers are made to appear as a conversation by artificially substituting some random entities (chosen from PERSON, GPE, ORG) with the relevant pronoun. For example, in the text above the last question contains "it" to indicate the city of Jaipur. |
frtna | null | null | null | false | 318 | false | frtna/es_it_Results-base-OPUS_Tatoeba | 2022-01-04T04:41:07.000Z | null | false | 23f3bc41eccc91a68a3d4c52125e8c1ec0e1045b | [] | [] | https://huggingface.co/datasets/frtna/es_it_Results-base-OPUS_Tatoeba/resolve/main/README.md | - Model: [OPUS-MT](https://huggingface.co/Helsinki-NLP/opus-mt-es-it)
- Tested on: [Tatoeba]()
<br>
- Metric:
- bleu(tensorflow),
- sacrebleu(github->mjpost),
- google_bleu(nltk),
- rouge(google-research),
- meteor(nltk),
- ter(university of Maryland)
<br>
- Retrieved from: [Huggingface](https://huggingface.co/metrics/) [metrics](https://github.com/huggingface/datasets/blob/master/metrics/)
- Script used for translation and testing: [https://gitlab.com/hmtkvs/machine_translation/-/tree/production-stable](https://gitlab.com/hmtkvs/machine_translation/-/tree/production-stable)
## Info
## mtdata-OPUS Tatoeba (length=14178, single reference)
**bleu** : 0.5228
<br>
**sacrebleu** : 0.5652
<br>
**google_bleu** : 0.5454
<br>
**rouge-mid** : precision=0.7792, recall=0.7899, f_measure=0.7796
<br>
**meteor** : 0.7557
<br>
**ter** : score=0.3003, num_edits= 24654, ref_length= 82079.0
## OPUS Tatoeba (length = 5000, multi references)
**bleu** : 0.5165
<br>
**sacrebleu** : 0.7098
<br>
**google_bleu** : 0.5397
<br>
**rouge-mid** : precision=0.9965, recall=0.5021, f_measure=0.6665
<br>
**meteor** : 0.3344
<br>
**ter** : score: 0.6703, 'num_edits': 38883, 'ref_length': 58000.0 |
frtna | null | @InProceedings{phd,
title = {Open Subtitles Machine Translation Dataset},
author={hmtkvs, Inc.
},
year={2021}
} | This new dataset is designed to be used in the scope of PhD project. | false | 317 | false | frtna/opensubtitles_mt | 2021-12-05T20:53:04.000Z | null | false | c2c0be202618bd1d4f9254c19607a00edd00174c | [] | [] | https://huggingface.co/datasets/frtna/opensubtitles_mt/resolve/main/README.md | annotations_creators:
- expert-generated
language_creators:
- crowdsourced
languages:
- es
- it
licenses:
- cc-by-4.0
multilinguality:
- multilingual
- translation
pretty_name: ''
source_datasets:
- original
task_categories:
- conditional-text-generation
task_ids:
- machine-translation |
fulai | null | null | null | false | 165 | false | fulai/DuReader | 2021-04-12T12:07:18.000Z | null | false | 42ad7b4f8e8e8bf31bea20a2d9b9f6fc6b9afd35 | [] | [] | https://huggingface.co/datasets/fulai/DuReader/resolve/main/README.md | 百度lic2020语言与智能信息竞赛数据集。 |
fuliucansheng | null | MiniNLP Data | MiniNLP Data | false | 320 | false | fuliucansheng/mininlp | 2021-06-30T04:44:01.000Z | null | false | 18b53dd97a3710f0a8621b69b23fb16f1b4fa176 | [] | [] | https://huggingface.co/datasets/fuliucansheng/mininlp/resolve/main/README.md |
# Dataset Card for "MiniNLP"
## Dataset Description
### Dataset Summary
This is a mini-nlp dataset for unitorch package.
### Data Instances
#### plain_text
An example of 'train' looks as follows.
```
{
"id": 1,
"num": 3,
"query": "Is this a test?",
"doc": "train test",
"label": "Good",
"score": 0.882
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `int32` feature.
- `num`: a `int32` feature.
- `query`: a `string` feature.
- `doc`: a `string` feature.
- `label`: a `string` feature.
- `score`: a `float32` feature.
### Data Splits Sample Size
| name |train|validation|test|
|----------|----:|---------:|---:|
|plain_text|15000| 1000 |1000|
|
gabtan99 | null | null | null | false | 322 | false | gabtan99/pex-conversations | 2022-10-20T19:34:29.000Z | null | false | fc71d4961071a67e78a9c856c3752c400f890d01 | [] | [
"language:tl",
"language:fil",
"license:unknown",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"tags:multi-turn"
] | https://huggingface.co/datasets/gabtan99/pex-conversations/resolve/main/README.md | ---
language:
- tl
- fil
license:
- unknown
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- dialogue-modeling
- language-modeling
pretty_name: PEx Conversations
tags:
- multi-turn
---
# PinoyExchange (PEx) Conversations Dataset
# Summary
PEx Conversations is a dataset composed of collected threads from PinoyExchange.com (Consisting of Tagalog, English, or Taglish responses).
The corpus consists of 45K total scraped threads from 8 subforums. The data only consists of the user message which means any images, videos, links, or any embdedded html are not collected in the scraping process. All characters have been transliterated to its closest ASCII representation, and unicode errors were fixed.
# Format
The data is categorized per category. The objects in the list is composed of:
* category - the category of the threads
* conversations - the list of threads
The threads inside conversations have recursive structure consisting of the following:
* text - This is the response/reply/prompt
* replies - This is a list of the replies to this prompt. The replies inside the list has a structure with the same text and replies component.
# Subforum percentages
The amount of data per subforum are as follows:
* Small Talk - 5K conversations with 1.16M utterances
* Food & Drinks - 8.2K conversations with 273K utterances
* Health & Wellness - 6.3K conversations with 93K utterances
* Body & Fitness - 3.9K conversations with 94K utterances
* Home & Garden - 3.6K conversations with 71K utterances
* Style & Fashion - 9.7K conversations with 197K utterances
* Travel & Leisure - 7.3K conversations with 431K utterances
* Visas & Immigration - 1.1K conversations with 99K utterances
# Model Research
[Tagalog DialoGPT](https://huggingface.co/gabtan99/dialogpt-tagalog-medium) |
gagan3012 | null | null | null | false | 166 | false | gagan3012/vizwiz | 2022-02-15T20:45:30.000Z | null | false | 8b7b1d394f41dce33618c2f73779e856fb54112c | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/gagan3012/vizwiz/resolve/main/README.md | ---
license: apache-2.0
---
|
gar1t | null | null | null | false | 322 | false | gar1t/test | 2021-09-15T17:55:27.000Z | null | false | ae8f1d6bbb8cc1ba94d97b6716507a38a140bf8f | [] | [] | https://huggingface.co/datasets/gar1t/test/resolve/main/README.md | # Test Dataset
Just a test - nothing to see here!
|
gcaillaut | null | null | French Wikipedia dataset for Entity Linking | false | 319 | false | gcaillaut/frwiki_good_pages_el | 2022-07-04T12:36:42.000Z | null | false | e6a41689f90a1148e18c639f2062ecb17fe84b55 | [] | [
"annotations_creators:machine-generated",
"language:fr-FR",
"language:fr",
"license:wtfpl",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other"
] | https://huggingface.co/datasets/gcaillaut/frwiki_good_pages_el/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators: []
language:
- fr-FR
- fr
license:
- wtfpl
multilinguality:
- monolingual
pretty_name: test
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
---
# Dataset Card for frwiki_good_pages_el
## Dataset Description
- Repository: [frwiki_good_pages_el](https://github.com/GaaH/frwiki_good_pages_el)
- Point of Contact: [Gaëtan Caillaut](mailto://g.caillaut@brgm.fr)
### Dataset Summary
This dataset contains _featured_ and _good_ articles from the French Wikipédia. Pages are downloaded, as HTML files, from the [French Wikipedia website](https://fr.wikipedia.org).
It is intended to be used to train Entity Linking (EL) systems. Links in articles are used to detect named entities.
### Languages
- French
## Dataset Structure
```
{
"title": "Title of the page",
"qid": "QID of the corresponding Wikidata entity",
"words": ["tokens"],
"wikipedia": ["Wikipedia description of each entity"],
"wikidata": ["Wikidata description of each entity"],
"labels": ["NER labels"],
"titles": ["Wikipedia title of each entity"],
"qids": ["QID of each entity"],
}
```
The `words` field contains the article’s text splitted on white-spaces. The other fields are list with same length as `words` and contains data only when the respective token in `words` is the __start of an entity__. For instance, if the _i-th_ token in `words` is an entity, then the _i-th_ element of `wikipedia` contains a description, extracted from Wikipedia, of this entity. The same applies for the other fields. If the entity spans multiple words, then only the index of the first words contains data.
The only exception is the `labels` field, which is used to delimit entities. It uses the IOB encoding: if the token is not part of an entity, the label is `"O"`; if it is the first word of a multi-word entity, the label is `"B"`; otherwise the label is `"I"`. |
german-nlp-group | null | @inproceedings{wenzek2020ccnet,
title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data},
author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, {\'E}douard},
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
pages={4003--4012},
year={2020}
} | German Only Extract from Common Crawl
This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German | false | 329 | false | german-nlp-group/german_common_crawl | 2021-02-09T13:32:27.000Z | null | false | e63bc83e816aa2e46836a1bd77fcd3c11f8b1d9e | [] | [] | https://huggingface.co/datasets/german-nlp-group/german_common_crawl/resolve/main/README.md |
# Dataset Card for GermanCommonCrawl
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/German-NLP-Group/german-transformer-training
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** philipp.reissel@rwth-aachen.de
### Dataset Summary
German Only Extract from Common Crawl
Stats:
Total Size after Deduplication: 142 Mio Pages / 194 GB (Gzipped)
Total Size before Deduplcation: 263 Mio Pages / 392 GB (Gzipped)
### Supported Tasks and Leaderboards
This Dataset is for pretraining a German Language Model (Unsupervised).
### Languages
German only (Sometimes websites are partially in another Language). One can filter these out through the `language_score` attribute.
## Dataset Structure
### Data Instances
```
{'url': 'http://my-shop.ru/shop/books/545473.html',
'date_download': '2016-10-20T19:38:58Z',
'digest': 'sha1:F62EMGYLZDIKF4UL5JZYU47KWGGUBT7T',
'length': 1155,
'nlines': 4,
'source_domain': 'my-shop.ru',
'title': 'Grammatikalische Liebeslieder. Methodische Vorschläge',
'raw_content': 'Grammatikalische Liebeslieder. [....]',
'cc_segment': 'crawl-data/CC-MAIN-2016-44/segments/1476988717783.68/wet/CC-MAIN-20161020183837-00354-ip-10-171-6-4.ec2.internal.warc.wet.gz',
'original_nlines': 99,
'original_length': 2672,
'language': 'de',
'language_score': 1.0,
'perplexity': 283.0,
'bucket': 'head'}"
```
### Data Fields
### Data Splits
Train only
## Dataset Creation
### Curation Rationale
Handling and Filtering of Common Crawl Data requires large scale Server Ressources at a location in the US (for downloading speed). The total computing time needed to create this dataset is above 100k CPU hours. To give others the opportunity to train models with this dataset easily we make it publicly available.
In most use cases you see an improved Model Performance when extending the pre-training Data so one can achieve highest accuracies as this is probably the largest available dataset.
### Source Data
It was filtered from the Common Crawl Snapshots of the following months:
1. 2015-48
2. 2016-18
3. 2016-44
4. 2017-33
5. 2017-30
6. 2017-30
7. 2017-39
8. 2017-51
9. 2018-09
10. 2018-17
11. 2018-30
12. 2018-39
13. 2018-51
14. 2019-09
15. 2019-18
16. 2019-30
17. 2019-47
18. 2020-10
#### Initial Data Collection and Normalization
Filtering and deduplication of each month seperalety was performed with [CC_Net](https://github.com/facebookresearch/cc_net). The current datasets only contains the best part (head part) with the highest text quality (see CC_Net Paper for more details). Middle and tail part may be uploaded soon as well, or are available on request.
Afterwards this Dataset was deduplicated again to filter out Websites which occur in multiple monthly snapshots. This deduplication removes all Websites which have either the same url or the same hash (this is to filter out websites which are accessible under multiple domains)
#### 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
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{wenzek2020ccnet,
title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data},
author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, {\'E}douard},
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
pages={4003--4012},
year={2020}
```
|
gfissore | null | null | null | false | 348 | false | gfissore/arxiv-abstracts-2021 | 2022-10-27T17:08:00.000Z | null | false | e4c5fbd4dec8e46a5dc869216fe1c94cc585757a | [] | [
"arxiv:1905.00075",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language:en",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"task_categories:summarization",
"task_categories:text-retrieval",
"task_categories:text2text-generation",
... | https://huggingface.co/datasets/gfissore/arxiv-abstracts-2021/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: arxiv-abstracts-2021
size_categories:
- 1M<n<10M
source_datasets: []
task_categories:
- summarization
- text-retrieval
- text2text-generation
task_ids:
- explanation-generation
- text-simplification
- document-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
---
# Dataset Card for arxiv-abstracts-2021
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Clement et al., 2019, On the Use of ArXiv as a Dataset, https://arxiv.org/abs/1905.00075](https://arxiv.org/abs/1905.00075)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Giancarlo Fissore](mailto:giancarlo.fissore@gmail.com)
### Dataset Summary
A dataset of metadata including title and abstract for all arXiv articles up to the end of 2021 (~2 million papers).
Possible applications include trend analysis, paper recommender engines, category prediction, knowledge graph construction and semantic search interfaces.
In contrast to [arxiv_dataset](https://huggingface.co/datasets/arxiv_dataset), this dataset doesn't include papers submitted to arXiv after 2021 and it doesn't require any external download.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
Here's an example instance:
```
{
"id": "1706.03762",
"submitter": "Ashish Vaswani",
"authors": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion\n Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin",
"title": "Attention Is All You Need",
"comments": "15 pages, 5 figures",
"journal-ref": null,
"doi": null,
"abstract": " The dominant sequence transduction models are based on complex recurrent or\nconvolutional neural
networks in an encoder-decoder configuration. The best\nperforming models also connect the encoder and decoder through
an attention\nmechanism. We propose a new simple network architecture, the Transformer, based\nsolely on attention
mechanisms, dispensing with recurrence and convolutions\nentirely. Experiments on two machine translation tasks show
these models to be\nsuperior in quality while being more parallelizable and requiring significantly\nless time to
train. Our model achieves 28.4 BLEU on the WMT 2014\nEnglish-to-German translation task, improving over the existing
best results,\nincluding ensembles by over 2 BLEU. On the WMT 2014 English-to-French\ntranslation task, our model
establishes a new single-model state-of-the-art\nBLEU score of 41.8 after training for 3.5 days on eight GPUs, a small
fraction\nof the training costs of the best models from the literature. We show that the\nTransformer generalizes well
to other tasks by applying it successfully to\nEnglish constituency parsing both with large and limited training
data.\n",
"report-no": null,
"categories": [
"cs.CL cs.LG"
],
"versions": [
"v1",
"v2",
"v3",
"v4",
"v5"
]
}
```
### Data Fields
These fields are detailed on the [arXiv](https://arxiv.org/help/prep):
- `id`: ArXiv ID (can be used to access the paper)
- `submitter`: Who submitted the paper
- `authors`: Authors of the paper
- `title`: Title of the paper
- `comments`: Additional info, such as number of pages and figures
- `journal-ref`: Information about the journal the paper was published in
- `doi`: [Digital Object Identifier](https://www.doi.org)
- `report-no`: Report Number
- `abstract`: The abstract of the paper
- `categories`: Categories / tags in the ArXiv system
### Data Splits
No splits
## Dataset Creation
### Curation Rationale
For about 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming, depth. In these times of unique global challenges, efficient extraction of insights from data is essential. The `arxiv-abstracts-2021` dataset aims at making the arXiv more easily accessible for machine learning applications, by providing important metadata (including title and abstract) for ~2 million papers.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The language producers are members of the scientific community at large, but not necessarily affiliated to any institution.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The full names of the papers' authors are included in the dataset.
## 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
The original data is maintained by [ArXiv](https://arxiv.org/)
### Licensing Information
The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
``` |
ghadeermobasher | null | @article{krallinger2015chemdner,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others},
journal={Journal of cheminformatics},
volume={7},
number={1},
pages={1--17},
year={2015},
publisher={BioMed Central}
} | \ | false | 468 | false | ghadeermobasher/BC5CDR-Chemical-Disease | 2022-01-25T10:31:51.000Z | null | false | 8f4deb948be91a72eefc1fff64f5e70d1c7dc1de | [] | [] | https://huggingface.co/datasets/ghadeermobasher/BC5CDR-Chemical-Disease/resolve/main/README.md | annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
paperswithcode_id: bc4chemd
pretty_name: BC4CHEMD
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
# Dataset Card for BC4CHEMD
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:**
https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/
- **Repository:** https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD
- **Paper:** BioCreative V CDR task corpus: a resource for chemical disease relation extraction
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Zhiyong Lu] (mailto: Zhiyong.Lu@nih.gov)
### Dataset Summary
A corpus for both named entity recognition and chemical-disease relations in the literature. A total of 1500 articles have been annotated with automated assistance from PubTator. Jaccard agreement results and corpus statistics verified the reliability of the corpus.
### Supported Tasks and Leaderboards
named-entity-recognition
### Languages
en
## Dataset Structure
### Data Instances
Instances of the dataset contain an array of `tokens`, `ner_tags` and an `id`. An example of an instance of the dataset:
{
'tokens': ['DPP6','as','a','candidate','gene','for','neuroleptic','-','induced','tardive','dyskinesia','.']
, 'ner_tags': [0,0,0,0,0,0,0,0,0,0,0,0],
'id': '0'
}
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a chemical and `2` the subsequent chemical tokens.
### Data Splits
The data is split into a train (3500 instances), validation (3500 instances) and test set (3000 instances).
## Dataset Creation
### Curation Rationale
The goal of the dataset consists on improving the state-of-the-art in chemical name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.
### Source Data
#### Initial Data Collection and Normalization
The dataset consists on abstracts extracted from PubMed.
#### Who are the source language producers?
The source language producers are the authors of publication abstracts hosted in PubMed.
### Annotations
#### Annotation process
The curators were trained to mark up the text according to the labels specified in the guidelines. The raw text was not tokenized prior to the annotation and only the title was distinguished from the PubMed abstract. The selection of text spans was done at the character level, they did not allow nested annotations and distinct entity mentions should not overlap. Each text span was selected according to the annotation guidelines and classified manually into one of the CEM classes.
#### Who are the annotators?
The group of curators used for preparing the annotations was composed mainly of organic chemistry postgraduates with an average experience of 3-4 years in the annotation of chemical names and chemical structures.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets.
### Discussion of Biases
The used CHEMDNER document set had to be representative and balanced in order to reflect the kind of documents that might mention the entity of interest.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] |
ghomasHudson | null | null | null | false | 330 | false | ghomasHudson/ao3_style_change | 2022-01-09T20:37:28.000Z | null | false | 448370989f17daccc03447dfe16cf588a0075e57 | [] | [] | https://huggingface.co/datasets/ghomasHudson/ao3_style_change/resolve/main/README.md | # AO3 Style Change
A Style Change detection dataset in the style of the PAN21 challenge but on much longer data (>10,000 tokens).
Warning: Due to the fanfiction source, this does contain some NSFW language. |
ghomasHudson | null | null | null | false | 322 | false | ghomasHudson/hotpotExtended | 2022-01-13T21:45:03.000Z | null | false | b8d98fb25c8aeda712dfc382c5875aee2c2da458 | [] | [] | https://huggingface.co/datasets/ghomasHudson/hotpotExtended/resolve/main/README.md | # HotpotQA-extended
> Version of the HotpotQA dataset with full Wikipedia articles.
The HotpotQA dataset consists of questions from crowd workers which require information from multiple Wikipedia articles in order to answer,thus testing the ability for models to perform multi-hop question answering. The data is commonly presented as a list of paragraphs containing relevant information plus a setting where the addition of ’distractor paragraphs’ fully test the ability of the model to comprehend which information is relevant to the question asked.
In this dataset, we increase the length of the inputs by expanding each paragraph with its full Wikipedia page as well as adding additional distractor articles from similar topics in order to meet the 10,000 token minimum length requirement for this benchmark. |
ghomasHudson | null | null | null | false | 322 | false | ghomasHudson/long_contra_pro | 2022-07-07T12:26:30.000Z | null | false | 41ad346644ee5f4284a280a6c001716b5e3d881b | [] | [] | https://huggingface.co/datasets/ghomasHudson/long_contra_pro/resolve/main/README.md | Filtered ContraPro dataset for long document translation. |
ghomasHudson | null | @misc{hudson2022muld,
title{MuLD: The Multitask Long Document Benchmark},
author={G Thomas Hudson, Noura Al Moubayed}
year={2022},
eprint={TODO},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Some of these datasets are directly based on existing datasets. Please cite these works. | MuLD: The Multitask Long Document Benchmark
A set of NLP tasks where each example is over 10,000 tokens long. | false | 808 | false | ghomasHudson/muld | 2022-11-02T12:55:17.000Z | null | false | eb92b66ad9d8b6a59cad50beccfc170346a013c8 | [] | [
"arxiv:2202.07362",
"annotations_creators:found",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:en",
"language:de",
"multilinguality:translation",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"source_datasets:extended|hotpot_qa... | https://huggingface.co/datasets/ghomasHudson/muld/resolve/main/README.md | ---
annotations_creators:
- found
- crowdsourced
language_creators:
- found
language:
- en
- de
license: []
multilinguality:
- translation
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|hotpot_qa
- extended|open_subtitles
task_categories:
- question-answering
- summarization
- text-generation
- translation
task_ids:
- abstractive-qa
pretty_name: The Multitask Long Document Benchmark
tags:
- conditional-text-generation
---
# MuLD
> The Multitask Long Document Benchmark
MuLD (Multitask Long Document Benchmark) is a set of 6 NLP tasks where the inputs consist of at least 10,000 words. The benchmark covers a wide variety of task types including translation, summarization, question answering, and classification. Additionally there is a range of output lengths from a single word classification label all the way up to an output longer than the input text.
- **Repository:** https://github.com/ghomasHudson/muld
- **Paper:** https://arxiv.org/abs/2202.07362
### Supported Tasks and Leaderboards
The 6 MuLD tasks consist of:
- **NarrativeQA** - A question answering dataset requiring an understanding of the plot of books and films.
- **HotpotQA** - An expanded version of HotpotQA requiring multihop reasoning between multiple wikipedia pages. This expanded version includes the full Wikipedia pages.
- **OpenSubtitles** - A translation dataset based on the OpenSubtitles 2018 dataset. The entire subtitles for each tv show is provided, one subtitle per line in both English and German.
- **VLSP (Very Long Scientific Papers)** - An expanded version of the Scientific Papers summarization dataset. Instead of removing very long papers (e.g. thesis), we explicitly include them removing any short papers.
- **AO3 Style Change Detection** - Consists of documents formed from the work of multiple [Archive of Our Own](ao3.org) authors, where the task is to predict the author for each paragraph.
- **Movie Character Types** - Predicting whether a named character is the Hero/Villain given a movie script.
### Dataset Structure
The data is presented in a text-to-text format where each instance contains a input string, output string and (optionally) json encoded metadata.
```
{'input: 'Who was wearing the blue shirt? The beginning...', 'output': ['John'], 'metadata': ''}
```
### Data Fields
- `input`: a string which has a differing structure per task but is presented in a unified format
- `output`: a list of strings where each is a possible answer. Most instances only have a single answer, but some such as narrativeQA and VLSP may have multiple.
- `metadata`: Additional metadata which may be helpful for evaluation. In this version, only the OpenSubtitles task contains metadata (for the ContraPro annotations).
### Data Splits
Each tasks contains different splits depending what was available in the source datasets:
| Task Name | Train | Validation | Test |
|----------------------------|----|----|-----|
| NarrativeQA | ✔️ | ✔️ | ✔️ |
| HotpotQA | ✔️ | ✔️ | |
| AO3 Style Change Detection | ✔️ | ✔️ | ✔️ |
| Movie Character Types | ✔️ | ✔️ | ✔️ |
| VLSP | | | ✔️ |
| OpenSubtitles | ✔️ | | ✔️ |
### Citation Information
```
@misc{hudson2022muld,
title={MuLD: The Multitask Long Document Benchmark},
author={G Thomas Hudson and Noura Al Moubayed},
year={2022},
eprint={2202.07362},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please also cite the papers directly used in this benchmark. |
ghomasHudson | null | """
_DESCRIPTION = | Very Long version of the scientific papers summarization dataset. Only includes theses over 10,000 tokens long. | false | 317 | false | ghomasHudson/vlsp | 2022-10-25T09:20:37.000Z | null | false | 0458b63225091d3bf55d72492c3aa60419fd6f4b | [] | [
"language:en"
] | https://huggingface.co/datasets/ghomasHudson/vlsp/resolve/main/README.md | ---
language:
- en
---
# Dataset Card for vlsp
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/ghomasHudson/very_long_scientific_papers
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Dataset following the methodology of the scientific_papers dataset, but specifically designed for very long documents (>10,000 words). This is gathered from arxiv.org by searching for theses.
The dataset has 2 features:
- article: the body of the document.
- abstract: the abstract of the document.
### Supported Tasks and Leaderboards
Summarization
### Languages
English
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
Only a test set is provided.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
|
gigant | null | \ | \
This corpus consists of approximately 22 hours of speech recordings. Transcripts are provided for all the recordings. The corpus can be divided into 3 parts:
1. Yaounde
Collected by a team from the U.S. Military Academy's Center for Technology Enhanced Language Learning (CTELL) in 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female.
2. CA16
This part was collected by a RDECOM Science Team who participated in the United Nations exercise Central Accord 16 (CA16) in Libreville, Gabon in June 2016. The Science Team included DARPA's Dr. Boyan Onyshkevich and Dr. Aaron Lawson (SRI International), as well as RDECOM scientists. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon.
3. Niger
This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015. These speakers were students in a course for officers and sergeants presented by Army trainers assigned to U.S. Army Africa. The data was collected by RDECOM Science & Technology Advisors Major Eddie Strimel and Mr. Bill Bergen. | false | 405 | false | gigant/african_accented_french | 2022-10-24T17:39:03.000Z | null | false | 643cc6391a43781f688022acd18b872d0789c309 | [] | [
"language:fr",
"license:cc",
"size_categories:10K<n<100K",
"task_categories:automatic-speech-recognition"
] | https://huggingface.co/datasets/gigant/african_accented_french/resolve/main/README.md | ---
language:
- fr
license: cc
size_categories:
fr:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: African Accented French
---
## Dataset Description
- **Homepage:** http://www.openslr.org/57/
### Dataset Summary
This corpus consists of approximately 22 hours of speech recordings. Transcripts are provided for all the recordings. The corpus can be divided into 3 parts:
1. Yaounde
Collected by a team from the U.S. Military Academy's Center for Technology Enhanced Language Learning (CTELL) in 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female.
2. CA16
This part was collected by a RDECOM Science Team who participated in the United Nations exercise Central Accord 16 (CA16) in Libreville, Gabon in June 2016. The Science Team included DARPA's Dr. Boyan Onyshkevich and Dr. Aaron Lawson (SRI International), as well as RDECOM scientists. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon.
3. Niger
This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015. These speakers were students in a course for officers and sergeants presented by Army trainers assigned to U.S. Army Africa. The data was collected by RDECOM Science & Technology Advisors Major Eddie Strimel and Mr. Bill Bergen.
### Languages
French
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called audio and its sentence.
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train and test.
The train split consists of 9401 audio clips and the related sentences.
The test split consists of 1985 audio clips and the related sentences.
### Contributions
[@gigant](https://huggingface.co/gigant) added this dataset. |
gigant | null | \ | \
The M-AILABS Speech Dataset is the first large dataset that we are providing free-of-charge, freely usable as training data for speech recognition and speech synthesis.
Most of the data is based on LibriVox and Project Gutenberg. The training data consist of nearly thousand hours of audio and the text-files in prepared format.
A transcription is provided for each clip. Clips vary in length from 1 to 20 seconds and have a total length of approximately shown in the list (and in the respective info.txt-files) below.
The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded by the LibriVox project and is also in the public domain – except for Ukrainian.
Ukrainian audio was kindly provided either by Nash Format or Gwara Media for machine learning purposes only (please check the data info.txt files for details). | false | 319 | false | gigant/m-ailabs_speech_dataset_fr | 2022-10-24T17:38:45.000Z | null | false | 71ec8b9e1b5351ea514cdf748c92592b13b14175 | [] | [
"language:fr",
"license:cc",
"size_categories:10K<n<100K",
"task_categories:automatic-speech-recognition"
] | https://huggingface.co/datasets/gigant/m-ailabs_speech_dataset_fr/resolve/main/README.md | ---
language:
- fr
license: cc
size_categories:
fr:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: M-AILABS Speech Dataset (French)
---
## Dataset Description
- **Homepage:** https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/
### Dataset Summary
The M-AILABS Speech Dataset is the first large dataset that we are providing free-of-charge, freely usable as training data for speech recognition and speech synthesis.
Most of the data is based on LibriVox and Project Gutenberg. The training data consist of nearly thousand hours of audio and the text-files in prepared format.
A transcription is provided for each clip. Clips vary in length from 1 to 20 seconds and have a total length of approximately shown in the list (and in the respective info.txt-files) below.
The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded by the LibriVox project and is also in the public domain – except for Ukrainian.
Ukrainian audio was kindly provided either by Nash Format or Gwara Media for machine learning purposes only (please check the data info.txt files for details).
### Languages
French
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called audio and its sentence.
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has not been subdivided into portions, everything is in the "train" split.
The train split consists of 82825 audio clips and the related sentences.
### Contributions
[@gigant](https://huggingface.co/gigant) added this dataset. |
gigant | null | null | null | false | 318 | false | gigant/ro_corpora_parliament_processed | 2022-02-02T15:29:18.000Z | null | false | 863b81ce584d8e6b20fc8ce509dd53d85f2cb4d7 | [] | [] | https://huggingface.co/datasets/gigant/ro_corpora_parliament_processed/resolve/main/README.md | |
gigant | null | \
@article{Stan2011442,
author = {Adriana Stan and Junichi Yamagishi and Simon King and
Matthew Aylett},
title = {The {R}omanian speech synthesis ({RSS}) corpus:
Building a high quality {HMM}-based speech synthesis
system using a high sampling rate},
journal = {Speech Communication},
volume = {53},
number = {3},
pages = {442--450},
note = {},
abstract = {This paper first introduces a newly-recorded high
quality Romanian speech corpus designed for speech
synthesis, called ''RSS'', along with Romanian
front-end text processing modules and HMM-based
synthetic voices built from the corpus. All of these
are now freely available for academic use in order to
promote Romanian speech technology research. The RSS
corpus comprises 3500 training sentences and 500 test
sentences uttered by a female speaker and was recorded
using multiple microphones at 96 kHz sampling
frequency in a hemianechoic chamber. The details of the
new Romanian text processor we have developed are also
given. Using the database, we then revisit some basic
configuration choices of speech synthesis, such as
waveform sampling frequency and auditory frequency
warping scale, with the aim of improving speaker
similarity, which is an acknowledged weakness of
current HMM-based speech synthesisers. As we
demonstrate using perceptual tests, these configuration
choices can make substantial differences to the quality
of the synthetic speech. Contrary to common practice in
automatic speech recognition, higher waveform sampling
frequencies can offer enhanced feature extraction and
improved speaker similarity for HMM-based speech
synthesis.},
doi = {10.1016/j.specom.2010.12.002},
issn = {0167-6393},
keywords = {Speech synthesis, HTS, Romanian, HMMs, Sampling
frequency, Auditory scale},
url = {http://www.sciencedirect.com/science/article/pii/S0167639310002074},
year = 2011
} | \
The Romanian speech synthesis (RSS) corpus was recorded in a hemianechoic chamber (anechoic walls and ceiling; floor partially anechoic) at the University of Edinburgh. We used three high quality studio microphones: a Neumann u89i (large diaphragm condenser), a Sennheiser MKH 800 (small diaphragm condenser with very wide bandwidth) and a DPA 4035 (headset-mounted condenser). Although the current release includes only speech data recorded via Sennheiser MKH 800, we may release speech data recorded via other microphones in the future. All recordings were made at 96 kHz sampling frequency and 24 bits per sample, then downsampled to 48 kHz sampling frequency. For recording, downsampling and bit rate conversion, we used ProTools HD hardware and software. We conducted 8 sessions over the course of a month, recording about 500 sentences in each session. At the start of each session, the speaker listened to a previously recorded sample, in order to attain a similar voice quality and intonation. | false | 320 | false | gigant/romanian_speech_synthesis_0_8_1 | 2022-10-24T17:38:35.000Z | null | false | b4dd8109d62276134bdc035cb274018825428582 | [] | [
"language:ro",
"license:unknown",
"size_categories:1K<n<10K",
"task_categories:automatic-speech-recognition"
] | https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1/resolve/main/README.md | ---
language:
- ro
license:
- unknown
size_categories:
ro:
- 1K<n<10K
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: Romanian Speech Synthesis
---
## Dataset Description
- **Homepage:** https://romaniantts.com/rssdb/
- **Paper:** https://www.sciencedirect.com/science/article/abs/pii/S0167639310002074
### Dataset Summary
The Romanian speech synthesis (RSS) corpus was recorded in a hemianechoic chamber (anechoic walls and ceiling; floor partially anechoic) at the University of Edinburgh. We used three high quality studio microphones: a Neumann u89i (large diaphragm condenser), a Sennheiser MKH 800 (small diaphragm condenser with very wide bandwidth) and a DPA 4035 (headset-mounted condenser). Although the current release includes only speech data recorded via Sennheiser MKH 800, we may release speech data recorded via other microphones in the future. All recordings were made at 96 kHz sampling frequency and 24 bits per sample, then downsampled to 48 kHz sampling frequency. For recording, downsampling and bit rate conversion, we used ProTools HD hardware and software. We conducted 8 sessions over the course of a month, recording about 500 sentences in each session. At the start of each session, the speaker listened to a previously recorded sample, in order to attain a similar voice quality and intonation.
### Languages
Romanian
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called audio and its sentence.
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train and test.
The train split consists of 3180 audio clips and the related sentences.
The test split consists of 536 audio clips and the related sentences.
### Citation Information
```
@article{Stan2011442,
author = {Adriana Stan and Junichi Yamagishi and Simon King and
Matthew Aylett},
title = {The {R}omanian speech synthesis ({RSS}) corpus:
Building a high quality {HMM}-based speech synthesis
system using a high sampling rate},
journal = {Speech Communication},
volume = {53},
number = {3},
pages = {442--450},
note = {},
abstract = {This paper first introduces a newly-recorded high
quality Romanian speech corpus designed for speech
synthesis, called ''RSS'', along with Romanian
front-end text processing modules and HMM-based
synthetic voices built from the corpus. All of these
are now freely available for academic use in order to
promote Romanian speech technology research. The RSS
corpus comprises 3500 training sentences and 500 test
sentences uttered by a female speaker and was recorded
using multiple microphones at 96 kHz sampling
frequency in a hemianechoic chamber. The details of the
new Romanian text processor we have developed are also
given. Using the database, we then revisit some basic
configuration choices of speech synthesis, such as
waveform sampling frequency and auditory frequency
warping scale, with the aim of improving speaker
similarity, which is an acknowledged weakness of
current HMM-based speech synthesisers. As we
demonstrate using perceptual tests, these configuration
choices can make substantial differences to the quality
of the synthetic speech. Contrary to common practice in
automatic speech recognition, higher waveform sampling
frequencies can offer enhanced feature extraction and
improved speaker similarity for HMM-based speech
synthesis.},
doi = {10.1016/j.specom.2010.12.002},
issn = {0167-6393},
keywords = {Speech synthesis, HTS, Romanian, HMMs, Sampling
frequency, Auditory scale},
url = {http://www.sciencedirect.com/science/article/pii/S0167639310002074},
year = 2011
}
```
### Contributions
[@gigant](https://huggingface.co/gigant) added this dataset. |
giganticode | null | null | null | false | 16 | false | giganticode/java-cmpx-v1 | 2022-07-01T20:32:52.000Z | null | false | 47ca07324dea12a571fa09411bba27e4ede64fa9 | [] | [
"language:java",
"license:mit",
"multilinguality:monolingual",
"size_categories:unknown",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/giganticode/java-cmpx-v1/resolve/main/README.md | ---
language:
- java
license:
- mit
multilinguality:
- monolingual
pretty_name:
- java-cmpx
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
--- |
giganticode | null | null | null | false | 14 | false | giganticode/java-cmpx | 2022-07-01T20:33:03.000Z | null | false | 0375da233f178717aa85164da93ebd223ba2dda0 | [] | [
"language:java",
"license:mit",
"multilinguality:monolingual",
"size_categories:unknown",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/giganticode/java-cmpx/resolve/main/README.md | ---
language:
- java
license:
- mit
multilinguality:
- monolingual
pretty_name:
- java-cmpx
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
|
gmnlp | null | @article{DBLP:journals/corr/abs-2007-01788,
author = {Antonios Anastasopoulos and
Alessandro Cattelan and
Zi{-}Yi Dou and
Marcello Federico and
Christian Federmann and
Dmitriy Genzel and
Francisco Guzm{\'{a}}n and
Junjie Hu and
Macduff Hughes and
Philipp Koehn and
Rosie Lazar and
William Lewis and
Graham Neubig and
Mengmeng Niu and
Alp {\"{O}}ktem and
Eric Paquin and
Grace Tang and
Sylwia Tur},
title = {{TICO-19:} the Translation Initiative for Covid-19},
journal = {CoRR},
volume = {abs/2007.01788},
year = {2020},
url = {https://arxiv.org/abs/2007.01788},
archivePrefix = {arXiv},
eprint = {2007.01788},
timestamp = {Thu, 08 Apr 2021 11:46:39 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2007-01788.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | In response to the on-going crisis, several academic (Carnegie Mellon University,
George Mason University, Johns Hopkins University) and industry (Amazon, Appen,
Facebook, Google, Microsoft, Translated) partners have partnered with the Translators
without Borders to prepare COVID-19 materials for a variety of the world’s languages
to be used by professional translators and for training state-of-the-art Machine
Translation (MT) models. The focus is on making emergency and crisis-related content
available in as many languages as possible. The collected, curated and translated
content across nearly 90 languages will be available to the professional translation
as well the MT research community. | false | 6,015 | false | gmnlp/tico19 | 2021-10-03T19:00:13.000Z | null | false | 55d70dc0b1d1d0b2151c5e22815d823fedac3f2f | [] | [] | https://huggingface.co/datasets/gmnlp/tico19/resolve/main/README.md | The TICO-19 evaluation set provides:
* Predefined dev and test splits. We provide English-XX translation files under both the `dev` and `test` directories.
* The dev set includes 971 sentences, and the test set includes 2100 sentences.
* The corresponding IDs are listed in the `dev.ids` and `test.ids` files.
The format of the files is:
~~~
{sourceLang}\t{targetLang}\t{sourceString}\t{targetString}\t{stringID}\t{sourceURL}\t{license}\t{translator_ID}
~~~
Currently available languages:
* Amharic (am)
* Arabic (ar)
* Bengali (bn)
* Kurdish Sorani (ckb)
* Latin American Spanish (es-LA)
* Farsi (fa)
* French (fr)
* Nigerian Fulfulde (fuv)
* Hausa (ha)
* Hindi (hi)
* Indonesian (id)
* Kurdish Kurmanji (ku)
* Lingala (ln)
* Luganda (lg)
* Marathi (mr)
* Malay (ms)
* Muanmar (my)
* Nepali (ne)
* Oromo (om)
* Dari (prs)
* Pashto (ps)
* Brazilian Portuguese (pt-BR)
* Russian (ru)
* Kinyarwanda (rw)
* Somali (so)
* kiSwahili (sw)
* Ethiopian Tigrinya (ti)
* Tagalog (tl)
* Urdu (ur)
* Chinese (Simplified) (zh)
* Zulu (zu)
All translations are released under a CC-0 license. |
gorkemgoknar | null | null | null | false | 322 | false | gorkemgoknar/tr_ted_talk_translated | 2022-01-13T09:14:54.000Z | null | false | 79987d1537e8f14b28d69214ec5f14704a9edc64 | [] | [
"language:tr",
"tags:dataset",
"tags:turkish",
"tags:ted-multi",
"tags:cleaned",
"license:apache-2.0",
"datasets:ted-multi"
] | https://huggingface.co/datasets/gorkemgoknar/tr_ted_talk_translated/resolve/main/README.md | ---
language:
- tr
thumbnail:
tags:
- dataset
- turkish
- ted-multi
- cleaned
license: apache-2.0
datasets:
- ted-multi
---
# Turkish Ted talk translations
# Created from ted-multi dataset
adding processing steps here if you want another language
```python
#using Turkish as target
target_lang="tr" # change to your target lang
from datasets import load_dataset
#ted-multi is a multiple language translated dataset
#fits for our case , not to big and curated but need a simple processing
dataset = load_dataset("ted_multi")
dataset.cleanup_cache_files()
#original from patrick's
#chars_to_ignore_regex = '[,?.!\-\;\:\"“%‘”�—’…–]' # change to the ignored characters of your fine-tuned model
#will use cahya/wav2vec2-base-turkish-artificial-cv
#checking inside model repository to find which chars removed (no run.sh)
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
import re
def extract_target_lang_entries(batch):
#specific mapping for ted_multi dataset
#need to find index of language in each translation as it can shift
try:
target_index_for_lang= batch["translations"]["language"].index(target_lang)
except ValueError:
#target not in list empty it for later processing
batch["text"] = None
return batch
#index_translation_pairs = zip(batch, target_index_for_batch)
text= batch["translations"]["translation"][target_index_for_lang]
batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
return batch
#this dataset has additional columns need to say it
cols_to_remove = ['translations', 'talk_name']
dataset = dataset.map(extract_target_lang_entries, remove_columns=cols_to_remove)
#on preocessing we tagged None for empty ones
dataset_cleaned = dataset.filter(lambda x: x['text'] is not None)
dataset_cleaned
from huggingface_hub import notebook_login
notebook_login()
dataset_cleaned.push_to_hub(f"{target_lang}_ted_talk_translated")
``` |
gsarti | null | @inproceedings{demattei-etal-2020-changeit,
author = {De Mattei, Lorenzo and Cafagna, Michele and Dell'Orletta, Felice and Nissim, Malvina and Gatt, Albert},
title = {{CHANGE-IT @ EVALITA 2020}: Change Headlines, Adapt News, GEnerate},
booktitle = {Proceedings of Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2020)},
editor = {Basile, Valerio and Croce, Danilo and Di Maro, Maria, and Passaro, Lucia C.},
publisher = {CEUR.org},
year = {2020},
address = {Online}
} | The CHANGE-IT dataset contains approximately 152,000 article-headline pairs, collected from two Italian
newspapers situated at opposite ends of the political spectrum, namely la Repubblica (left) and
Il Giornale (right), with the two newspapers equally represented. The dataset has been used in the context
of the CHANGE-IT task (https://sites.google.com/view/change-it) during the Evalita 2020 evaluation campaign
(http://www.evalita.it/2020). CHANGE-IT is a generation task for Italian – more specifically, a style transfer
task for headlines of Italian newspapers. Given a (collection of) headlines from one newspaper, namely
Il Giornale (G) or La Repubblica (R), it challenges automatic systems to change all G-headlines to headlines in
style R, and all R-headlines to headlines in style G. Although the task only concerns headline change, the dataset
comprehends both the headlines as well as their respective full articles. | false | 475 | false | gsarti/change_it | 2022-10-27T08:37:09.000Z | null | false | ceb0129e499ea5344dba1391c0a046222ddba631 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:it",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"task_categories:summarization",
"task_categories:text-generation",
"tags:conditional-text-generation",
... | https://huggingface.co/datasets/gsarti/change_it/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- it
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
- text-generation
task_ids: []
pretty_name: change-it
tags:
- conditional-text-generation
- style-transfer
---
# Dataset Card for CHANGE-IT
## Table of Contents
- [Dataset Card for CHANGE-IT](#dataset-card-for-change-it)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Style Transfer](#style-transfer)
- [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)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://live.european-language-grid.eu/catalogue/corpus/7373](https://live.european-language-grid.eu/catalogue/corpus/7373)
- **Repository:** [Github](https://github.com/michelecafagna26/CHANGE-IT)
- **Paper:** [CEUR-ws.org](http://ceur-ws.org/Vol-2765/paper169.pdf)
- **Video** [Vimeo](https://vimeo.com/484098874)
- **Point of Contact:** [Lorenzo De Mattei](lorenzo.demattei@gmail.com)
- **Size of downloaded dataset files:** 168.7 MB
- **Size of the generated dataset:** 411 MB
- **Total amount of disk used:** 579.7 MB
### Dataset Summary
The CHANGE-IT dataset contains approximately 152,000 article-headline pairs, collected from two Italian newspapers situated at opposite ends of the political spectrum, namely la Repubblica (left) and Il Giornale (right), with the two newspapers equally represented. The dataset has been used in the context
of the [CHANGE-IT task](https://sites.google.com/view/change-it) during the [Evalita 2020 evaluation campaign](http://www.evalita.it/2020). CHANGE-IT is a generation task for Italian – more specifically, a style transfer task for headlines of Italian newspapers. Given a (collection of) headlines from one newspaper, namely Il Giornale (G) or La Repubblica (R), it challenges automatic systems to change all G-headlines to headlines in style R, and all R-headlines to headlines in style G. Although the task only concerns headline change, the dataset comprehends both the headlines as well as their respective full articles.
**Disclaimer**: *The CHANGE-IT dataset is hosted by the [European Language Grid](https://live.european-language-grid.eu/) and licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). To use the dataset using* 🤗 *Datasets, download and unzip the folder from its [ELG page](https://live.european-language-grid.eu/catalogue/corpus/7373) and pass it to the* `load_dataset` *method as:* `datasets.load_dataset('gsarti/change_it', data_dir='path/to/unzipped/folder')`
### Supported Tasks and Leaderboards
#### Style Transfer
The following table is taken from Table 4 of the original paper, where a *pointer-network* architecture is used as a baseline to perform style transfer in two settings. In the **rep2gio** variant the system is trained to summarize Repubblica headlines from full texts (vice versa for **gio2rep**), and the style transfer is performed by summarizing full texts of the other newspaper in the source newspaper's headline style. **avg** is the average of the two settings.
| | HH| AH|Main|Compliancy|
|--------:|---:|---:|---:|---------:|
|`rep2gio`|.649|.876|.799| .449|
|`gio2rep`|.639|.871|.435| .240|
| `avg`|.644|.874|.616| .345|
Here **Main**, **HH** and **AH** are all BERT-base models trained to evaluate the quality of style transfer as follows:
- **Main**: the model is trained to classify a generated headline either as `ilgiornale` or `repubblica`, achieving ~80% F1 score on gold data. Tests whether the transfer has been successful.
- **Headline-Headline (HH)**: the model is trained to check the compatibility between original and generated headlines. Tests whether the generation is coherent with the reference.
- **Article-Headline (AH)**: the model is trained to check the compatibility between original fulltext article and generated headlines. Tests whether the generation is coherent with the source article.
The final metric, **Overall compliancy**, is a binary metric that is positive if the other three metrics match (**Main** decision is reversed, **HH** and **AH** predict match), and negative otherwise. Refer to Section 3 of the original paper for more details.
### Languages
The language data in CHANGE-IT is in Italian (BCP-47 `it`)
## Dataset Structure
### Data Instances
A sample from the `test` split of the `ilgiornale` config is provided below. The other configuration, `ilgiornale`, has the same structure.
```json
{
"id": 0,
"headline": "Ucraina, coalizione della Timoshenko denuncia irruzione nella sede",
"full_text": "Rimane alta la tensione in Ucraina , dove da giorni i manifestanti scendono in piazza per protestare contro la decisione del presidente Viktor Yanukovich, che ha deciso di congelare l'accordo di associazione con l'Unione Europea. Il momento è molto delicato. L'opposizione teme una repressione violenza della protesta, con le forze speciali che hanno costretto i manifestanti a Kiev ad allontanarsi dalla sede del governo, per ripiegare su piazza Indipendenza. Il leader d'opposizione Vitaly Klitschko ha invitato il presidente a non utilizzare la forza, se non vuole avere il sangue dei manifestanti sulle sue mani. Nel frattempo il presidente Yanukovich ha aperto alla possibilità di un dialogo, annunciando per domani un incontro con i suoi due predecessori, Leonid Kuchma e Viktor Yushchenko. Ieri un milioni di persone sono scese in piazza, scaduti i due giorni di ultimatum dati al governo per indire nuove elezioni, I manifestanti hanno rovesciato la grande statua di Lenin posta sul boulevard Shevchenko. Piazza Indipendenza (Maidan Nezalezhnosti) resta il punto più caldo della capitale. Qui sono state erette barricate davanti agli ingressi della metropolitana, nel tentativo di preparsi a un'azione della polizia, che al momento non ha però preso iniziative contro i dimostranti. In serata Batkivshcyna, la coalizione dell'ex premier Yulia Timoshenko , ha denunciato l'irruzione di almeno venti agenti della polizia antisommossa nel proprio quartier generale. Il portavoce della polizia, Olga Bilyk, ha smentito: \"Né la polizia di Kiev, né la Berkut - ha dichiarato - hanno condotto operazioni nella sede\".",
"alignment": "A2"
}
```
The text is provided as-is, without further preprocessing or tokenization.
### Data Fields
- `headline`: The original headline for the newspaper.
- `full_text`: The article full text associated to the respective headline.
- `alignment`: The alignment value used for the style transfer experiments. Values:
- `A1`: Top 5K pairs, highly aligned.
- `A2`: Test set, highly aligned.
- `A3`: 10K to 20K pairs, fairly aligned.
- `R`: Bottom ~50K pairs, weakly/not aligned.
### Data Splits
| config| train| test|
|---------:|-------------------------------------:|-----------:|
|`ilgiornale`|5'000 (A1) + 10'000 (A3) + 48'701 (R) | 5'000 (A2) |
|`repubblica`|5'000 (A1) + 10'000 (A3) + 48'701 (R) | 5'000 (A2) |
### Dataset Creation
Please refer to the original article [CHANGE-IT @ EVALITA 2020: Change Headlines, Adapt News, GEnerate](http://ceur-ws.org/Vol-2765/paper169.pdf) for additional information on dataset creation.
## Additional Information
### Dataset Curators
The organizers of the CHANGE-IT shared tasks are the curators of the original dataset. For problems or updates on the 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com).
### Licensing Information
Licensed with Creative Commons Attribution Non Commercial Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
Please cite the authors if you use these corpora in your work:
```
@inproceedings{demattei-etal-2020-changeit,
author = {De Mattei, Lorenzo and Cafagna, Michele and Dell'Orletta, Felice and Nissim, Malvina and Gatt, Albert},
title = {{CHANGE-IT @ EVALITA 2020}: Change Headlines, Adapt News, GEnerate},
booktitle = {Proceedings of Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2020)},
editor = {Basile, Valerio and Croce, Danilo and Di Maro, Maria, and Passaro, Lucia C.},
publisher = {CEUR.org},
year = {2020},
address = {Online}
}
|
gsarti | null | @article{JMLR:v21:20-074,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {140},
pages = {1-67},
url = {http://jmlr.org/papers/v21/20-074.html}
} | A thoroughly cleaned version of the Italian portion of the multilingual
colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning
detailed in the repository README file. | false | 1,065 | false | gsarti/clean_mc4_it | 2022-10-23T09:01:21.000Z | mc4 | false | 8281df3f5a2e765a5cc30e4feacac61e94ffdce4 | [] | [
"arxiv:1910.10683",
"arxiv:2203.03759",
"annotations_creators:no-annotation",
"language_creators:found",
"language:it",
"license:odc-by",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"size_categories:10M<n<100M",
"size_categories:100M<n<1B",
"source_datasets:extended",
"task_cate... | https://huggingface.co/datasets/gsarti/clean_mc4_it/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- it
license:
- odc-by
multilinguality:
- monolingual
size_categories:
tiny:
- 1M<n<10M
small:
- 10M<n<100M
medium:
- 10M<n<100M
large:
- 10M<n<100M
full:
- 100M<n<1B
source_datasets:
- extended
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: mc4
pretty_name: mC4_it
---
# Dataset Card for Clean Italian mC4 🇮🇹
## Table of Contents
- [Dataset Card for Clean](#dataset-card-for-mc4)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Preprocessing](#preprocessing)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [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
- **Original Homepage:** [HF Hub](https://huggingface.co/datasets/allenai/c4)
- **Paper:** [ArXiv](https://arxiv.org/abs/1910.10683)
### Dataset Summary
A thoroughly cleaned version of the Italian split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4). Based on the [Common Crawl dataset](https://commoncrawl.org). The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4), with subsequent preprocessing performed by [Gabriele Sarti](https://gsarti.com) following a standard procedure for all dataset shards.
### Preprocessing
The preprocessing of the dataset follows the procedure used by Yeb Havinga for training the model [`t5-base-dutch`](https://huggingface.co/flax-community/t5-base-dutch) on a portion of the cleaned Dutch split of mC4. The original code, that was adapted for Italian in this case, is available on [GitLab](https://gitlab.com/yhavinga/c4nlpreproc). In summary, the preprocessing procedure includes:
- Removing documents containing words from a selection of the [Italian and English List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words).
- Removing sentences containing:
- Less than 3 words.
- A word longer than 1000 characters.
- An end symbol not matching end-of-sentence punctuation.
- Strings associated to javascript code (e.g. `{`), lorem ipsum, policy information in Italian or English.
- Removing documents (after sentence filtering):
- Containing less than 5 sentences.
- Containing less than 500 or more than 50'000 characters.
- Not identified as prevalently Italian by the `LangDetect` package.
Using parallel processing with 96 CPU cores on a TPUv3 via Google Cloud to perform the complete clean of all the original Italian shards of mC4 (1024 of ~220Mb train, 8 of ~24Mb validation) required roughly 10 hours due to the demanding steps of sentence tokenization and language detection. The total size of compressed `.json.gz` files is roughly halved after the procedure.
## Dataset Structure
### Data Instances
An example from the dataset:
```
{
'timestamp': '2020-02-22T22:24:31Z',
'url': 'https://altreconomia.it/una-rotonda-sul-pane/',
'text': 'Per raggiungere il campo attraversiamo la striscia d’asfalto che porta verso la provinciale numero 13. Mettiamo a rischio la nostra incolumità in un territorio di auto e camion. Sullo sfondo, i profili della Grigna e del Resegone. Più vicini, quelli del solito ipermercato di provincia, e delle villette a schiera che avanzano tra le coltivazioni. È lo sprawling, l’avanzata del cemento.\\nDa questo lato dalla strada, invece, è ancora regno contadino. Almeno per ora. Torniamo a Caponago (Mb), Brianza pura, dove ha avuto i natali il progetto “Spiga e madia”. Ne parlammo su Ae nel gennaio 2009: in un territorio “spaesato”, il Comitato “verso il Distretto di economia solidale della Brianza” (Desbri) e la “Retina” dei gruppi di acquisto locali danno vita a un progetto di produzione di frumento, molitura, panificazione e distribuzione in un raggio di 20 chilometri. Si comincia da zero, nel 2007, senza alcun di finanziamento, quando una famiglia del [...]. Il giochino vale almeno 3 miliardi di euro all’anno. La misura, introdotta in via straordinaria con la finanziaria 2005, è stata prorogata anche con l’ultimo decreto “milleproroghe”.'
}
```
### Data Fields
The data contains the following fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp of extraction as a string
### Data Splits
To build mC4, the original authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. For Italian, the whole corpus of scraped text was divided in `1032` jsonl files, `1024` for training following the naming style `c4-it.tfrecord-0XXXX-of-01024.json.gz` and 8 for validation following the naming style `c4-it-validation.tfrecord-0000X-of-00008.json.gz`. The full set of preprocessed files takes roughly 215GB of disk space to download with Git LFS.
For ease of use under different storage capacities, the following incremental splits are available (sizes are estimates). **Important**: The sizes in GB represent the estimated weight for :
|split |train size (docs, words, download + preproc disk space)|validation size|
|:-----|------------------------------------------------------:|--------------:|
|tiny | 10M docs, 4B words (9 GB + 27 GB) | 12k docs |
|small | 20M docs, 8B words (18 GB + 54 GB) | 24k docs |
|medium| 50M docs, 20B words (47 GB + 135 GB) | 48k docs |
|large | 75M docs, 30B words (71 GB + 203 GB) | 72k docs |
|full | 103M docs, 41B words (109 GB + 279 GB) | 96k docs |
You can load any subset like this:
```python
from datasets import load_dataset
mc4_it_tiny = load_dataset("gsarti/clean_mc4_it", "tiny")
```
Since splits are quite large, you may want to traverse them using the streaming mode available starting from 🤗 Datasets v1.9.0:
```python
from datasets import load_dataset
mc4_it_full_stream = load_dataset("gsarti/clean_mc4_it", "full", split='train', streaming=True)
print(next(iter(mc4_it_full_stream))) # Prints the example presented above
```
## Dataset Creation
Refer to the original paper for more considerations regarding the choice of sources and the scraping process for creating `mC4`.
## Considerations for Using the Data
### Social Impact of Dataset
With more than 200GB of cleaned Italian text and more than 41B estimated words, this is by far the largest available corpus for the Italian language. The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 69GB in size for its deduplicated variant. Using this corpus for training language models with adequate computational resources will allow researchers to reach parity with the performances observed for the English language. This can in turn have important repercussions for the development of commercial language technology applications for the Italian language.
### Discussion of Biases
Despit the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will inevitably reflect biases present in blog articles and comments on the Internet. This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.
## Additional Information
### Dataset Curators
Authors at AllenAI are the original curators for the `mc4` corpus. For inquiries or requests regarding the Italian cleaned portion contained in this repository, please contact me at [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com)
### Licensing Information
AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
### Citation Information
If you use this dataset in your work, please cite us and the original mC4 authors as:
```
@article{sarti-nissim-2022-it5,
title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
@inproceedings{xue-etal-2021-mt5,
title = "m{T}5: A Massively Multilingual Pre-trained Text-to-Text Transformer",
author = "Xue, Linting and
Constant, Noah and
Roberts, Adam and
Kale, Mihir and
Al-Rfou, Rami and
Siddhant, Aditya and
Barua, Aditya and
Raffel, Colin",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.41",
doi = "10.18653/v1/2021.naacl-main.41",
pages = "483--498",
}
```
### Contributions
Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
|
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