id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Back-up/test_ds_v3 | Back-up | 2023-11-14T14:57:49Z | 0 | 0 | null | [
"region:us"
] | 2023-11-14T14:57:49Z | 2023-11-14T14:57:46.000Z | 2023-11-14T14:57:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: response
struct:
- name: response
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: instruction
dtype: string
- name: prompt_name
dtype: string
- name: metadata
struct:
- name: max_ratio
dtype: float64
- name: paragraph_similar
dtype: string
- name: start_index
dtype: int64
splits:
- name: train
num_bytes: 21511788
num_examples: 7597
download_size: 8245485
dataset_size: 21511788
---
# Dataset Card for "test_ds_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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NomeIncrivel/Phineas | NomeIncrivel | 2023-11-14T15:13:26Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-14T15:13:26Z | 2023-11-14T15:12:30.000Z | 2023-11-14T15:12:30 | ---
license: openrail
---
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Sherinpatel5/Embeddings | Sherinpatel5 | 2023-11-14T15:13:21Z | 0 | 0 | null | [
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scherrmann/adhoc_quad | scherrmann | 2023-11-16T09:24:41Z | 0 | 0 | null | [
"arxiv:2311.08793",
"region:us"
] | 2023-11-16T09:24:41Z | 2023-11-14T15:13:31.000Z | 2023-11-14T15:13:31 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 10365360
num_examples: 6659
- name: validation
num_bytes: 1157605
num_examples: 748
download_size: 3088466
dataset_size: 11522965
---
# Dataset Card for "adhoc_quad"
## Dataset Summary
The German Ad-Hoc Question Answering Dataset (AdHocQuAD) is a reading comprehension dataset for German financial texts. It is a machine generated dataset, where ChatGPT (Version 3.5) is used to ask questions on a set of German Ad-Hoc announcements. The answer to every question is a segment of text, or span, from the corresponding reading passage.
## 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 texts in the dataset are in German (de).
# Dataset Structure
## Data Instances
A sample from the training set is provided below:
{
"context": "This is a test context with eight words.",
"id": "1",
"question": "How many words contains the context?",
"answers": {
"answer_start": [28],
"text": ["eight"]
}
}
## 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.
# Additional Information
## Details on the Generation of the Ad-Hoc QuAD Database
To construct the ad-hoc QuAD database, I use 9,132 German ad-hoc announcements as context strings. Announcements exceeding 15 sentences are truncated to ensure compatibility with BERT's input limitations in subsequent applications.
After that, there is a need to identify questions and appropriate answers that reference the given ad-hoc announcements. Given that manual generation of questions and answers is both resource-intensive and time-consuming, I employ the OpenAI's ChatGPT model (gpt-3.5-turbo).
In a first step, I ask ChatGPT to generate three suitable questions for a given announcement. The prompt looks as follows:
Create three questions for the following text.
It should be possible to answer the question with a substring of the input text.
The questions should ask for different aspects of the input.
The questions should be in German.
Text: <<context>>
Question:
In the pursuit of creating an extractive QuAD task, it is imperative to instruct the model such that every question can be answered using a substring from the provided announcement. This strategy aims to prevent the model from generating open-ended questions or those requiring external knowledge not present in the announcement. Additionally, the model is directed to address various aspects of the announcement to minimize question redundancy. Notably, despite the context strings being in German, ChatGPT occasionally formulates questions in English. To counteract this, explicit instructions are given to ensure questions are posed in German. Employing this methodology yields 9,132 unique context-question pairs.
In a second step, I use ChatGPT again to extract the substring that answers to question to a specific context string. The respective prompt is given by:
You have given a text and a question to that text. Find the answer as a substring of the input text.
It is crucial that the answer is contained exactly as a substring in the input text, even if this implies that the answer is not a full sentence.
Example:
Text: 'Herr Müller ist 37 Jahre alt.'
Question: 'Wie alt ist Herr Müller?'
Answer: '37 Jahre'
Text: <<context>>
Question: <<question>>
Answer:
Evaluations of the method of extracting substrings from a specified context to answer a posed question via ChatGPT indicated a recurrent issue: ChatGPT frequently transformed the substring into a complete sentence, thereby compromising the extractive nature of the resultant database. Emphasizing the necessity for extractive answers, coupled with a demonstrative example, markedly enhanced the outcomes. However, of the responses generated by ChatGPT, 1,725 are not given as substrings of the context, leading to a final ad-hoc QuAD database size of 7,407.
The code for creating the dataset can be found [here](https://github.com/FinTexIFB/AdHocQuAD).
## Dataset Curators
The dataset was created by Moritz Scherrmann using ChatGPT 3.5 turbo
## Citation Information
@misc{scherrmann2023german,
title={German FinBERT: A German Pre-trained Language Model},
author={Moritz Scherrmann},
year={2023},
eprint={2311.08793},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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NomeIncrivel/Tivo-stan | NomeIncrivel | 2023-11-14T15:22:36Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-14T15:22:36Z | 2023-11-14T15:21:38.000Z | 2023-11-14T15:21:38 | ---
license: openrail
---
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khelverskovp/Chemistry2e_embeddings | khelverskovp | 2023-11-14T15:26:25Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-14T15:26:25Z | 2023-11-14T15:25:09.000Z | 2023-11-14T15:25:09 | ---
license: mit
---
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atom-in-the-universe/cc-pdf-extracted-100k | atom-in-the-universe | 2023-11-14T15:41:47Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-14T15:41:47Z | 2023-11-14T15:29:17.000Z | 2023-11-14T15:29:17 | ---
license: apache-2.0
---
# Stats for number of tokens:
```
+--------------+
|sum(num_words)|
+--------------+
| 34469983|
+--------------+
+---------------+
|sum(num_tokens)|
+---------------+
| 59332879|
+---------------+
+-----------------+
| avg(num_words)|
+-----------------+
|3895.353486269635|
+-----------------+
+-----------------+
| avg(num_tokens)|
+-----------------+
|6705.037744377896|
+-----------------+
``` | [
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KaraKaraWitch/WitchesWarehouse | KaraKaraWitch | 2023-11-14T15:55:41Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-14T15:55:41Z | 2023-11-14T15:29:39.000Z | 2023-11-14T15:29:39 | ---
license: apache-2.0
pretty_name: Witches Warehouse
---
# Dataset Card for Witches Warehouse
If you got linked to here, you probably know what you want. | [
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Pablao0948/Naldo_Benny | Pablao0948 | 2023-11-14T15:31:37Z | 0 | 0 | null | [
"license:openrail",
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] | 2023-11-14T15:31:37Z | 2023-11-14T15:30:22.000Z | 2023-11-14T15:30:22 | ---
license: openrail
---
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snake8c/ds-test | snake8c | 2023-11-14T15:38:29Z | 0 | 0 | null | [
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Daniil-plotnikov/musicdataset | Daniil-plotnikov | 2023-11-15T14:23:22Z | 0 | 0 | null | [
"music",
"region:us"
] | 2023-11-15T14:23:22Z | 2023-11-14T15:39:43.000Z | 2023-11-14T15:39:43 | ---
tags:
- music
---
This dataset is a text file with music in ABC format. A compositions is separated from another by two '###' | [
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jzzcutler/github-issues | jzzcutler | 2023-11-14T15:42:47Z | 0 | 0 | null | [
"region:us"
] | 2023-11-14T15:42:47Z | 2023-11-14T15:42:32.000Z | 2023-11-14T15:42:32 | ---
dataset_info:
features:
- name: url
dtype: string
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dtype: string
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dtype: string
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struct:
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struct:
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splits:
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num_bytes: 25458344
num_examples: 4000
download_size: 7291887
dataset_size: 25458344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hippocrates/MIMIC_SUM_test | hippocrates | 2023-11-14T16:07:04Z | 0 | 0 | null | [
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configs:
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---
# Dataset Card for "MIMIC_SUM_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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---
# Dataset Card for "chemistry-bookshelves-merged"
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