prompt stringlengths 12 7.84k | context stringclasses 0
values | class list | class-suggestion stringclasses 8
values | class-suggestion-metadata dict | response list | response-suggestion stringlengths 15 17.7k | response-suggestion-metadata dict | external_id stringclasses 0
values | metadata stringlengths 113 129 | vectors dict |
|---|---|---|---|---|---|---|---|---|---|---|
Can brain cells move? By movement I mean long distance migration (preferably within the brain only). | null | [] | brainstorming | {
"type": null,
"score": 0.2823514938354492,
"agent": "setfit"
} | [] | The question is relatively broad and one should take into account that the brain not only consists of neurons, but also glial cells (supportive cells) and pre-mitotic neuronal stem cells. Furthermore, as critical fellow-scientists have indicated, developmental stage is very important, as the developing embryonic brain ... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 85, "passed_quality_check": "True", "flesch_reading_ease": 82.39000000000001, "entropy": 0.4352176404374839} | {
"prompt-similarity": [
-0.013013245537877083,
0.01881960965692997,
0.018717532977461815,
-0.014981311745941639,
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0.009528533555567265,
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-0.019534926861524582,
... |
In our computer systems lecture we were introduced to the MIPS processor. It was (re)developed over the course of the term and has in fact been quite easy to understand. It uses a RISC design, that is its elementary commands are regularly encoded and there are only few of them in order to keep the wires simple.
It was ... | null | [] | brainstorming | {
"type": null,
"score": 0.1541222184896469,
"agent": "setfit"
} | [] | There is a general historical trend.
In the olden days, memories were small, and so programs were perforce small. Also, compilers were not very smart, and many programs were written in assembler, so it was considered a good thing to be able to write a program using few instructions. Instruction pipelines were simple, a... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 538, "passed_quality_check": "True", "flesch_reading_ease": 80.92500000000003, "entropy": 3.931978186259446} | {
"prompt-similarity": [
-0.012156513519585133,
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0.06182139739394188,
0.0034399896394461393,
-0.03506855666637421,
0.004013681318610907,
... |
View tabular file such as CSV from command line, having horizontal and vertical scrolling would be great. | null | [] | summarization | {
"type": null,
"score": 0.16503676772117615,
"agent": "setfit"
} | [] | Sure, please take a look at csvkit. It provides a set of tools that adhere to the UNIX philosophy (meaning they are small, simple, single-purposed and can be combined).
Here is an example that extracts the ten most populated cities in Germany from the free Maxmind World Cities database and displays the result in a co... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 89, "passed_quality_check": "True", "flesch_reading_ease": 60.19176470588238, "entropy": 0.41496820745515073} | {
"prompt-similarity": [
-0.019738825038075447,
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-0.027354052290320396,
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0.04327983036637306,
-0.042965229600667953,
-0.049861568957567215,
-0.001399405300617218,
... |
Slater type orbitals (STO) are considered to be more accurate than gaussian type orbitals (GTO) for atomic and molecular QM calculations because - among other reasons - they decay with $e^{-\alpha r}$ as $r \to \infty$. But GTOs are more popular because they are easier to calculate with. GTOs decay with $e^{-\alpha r^2... | null | [] | general_qa | {
"type": null,
"score": 0.16225840151309967,
"agent": "setfit"
} | [] | I'll answer this question from the theoretical side. The exponential behavior follows simply from the Schrödinger equation. Consider the one-electron Schrödinger equation:
$$
(-\frac{1}{2}\nabla^2 + V(\mathbf{r}))\psi(\mathbf{r}) = \epsilon\psi(\mathbf{r}), \epsilon < 0
$$
At spatial points that are very far away from ... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 804, "passed_quality_check": "True", "flesch_reading_ease": 42.43434355828222, "entropy": 4.0899169183897515} | {
"prompt-similarity": [
-0.010380225256085396,
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0.01... |
Explain what "git reset" does. I come from a SVN background and Git is a whole new paradigm. I got mercurial easily, but Git is much more technical.
I think ```git reset``` is close to ```hg revert```, but it seems there are differences. Please include detailed explanations about:
* the options ```--hard```, ```--soft... | null | [] | closed_qa | {
"type": null,
"score": 0.2118387669324875,
"agent": "setfit"
} | [] | Remember that in ```git``` you have:
* the ```HEAD``` pointer, which tells you what commit you're working on
* the working tree, which represents the state of the files on your system
* the staging area (also called the index), which "stages" changes so that they can later be committed together
> Please include det... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 463, "passed_quality_check": "False", "flesch_reading_ease": 80.29591491841494, "entropy": 3.3348391710428453} | {
"prompt-similarity": [
0.0019898908212780952,
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-0.023906927555799484,
-0.05944724380970001,
0.0008035888313315809,
-0.0... |
I am looking to use Java to get the MD5 checksum of a file.
How is it done? | null | [] | information_extraction | {
"type": null,
"score": 0.15216578543186188,
"agent": "setfit"
} | [] | The com.google.common.hash API offers:
* A unified user-friendly API for all hash functions
* Seedable 32- and 128-bit implementations of murmur3
* md5(), sha1(), sha256(), sha512() adapters, change only one line of code to switch between these, and murmur.
* goodFastHash(int bits), for when you don't care what algori... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 59, "passed_quality_check": "False", "flesch_reading_ease": 103.68723684210525, "entropy": 0.8201050799144105} | {
"prompt-similarity": [
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0.022660112008452415,
-0.0142160439863801,
-0.05595777556300163,
-0.004895154852420092,
0.0... |
What are the primary objections Democrats have to a border wall? | null | [] | brainstorming | {
"type": null,
"score": 0.19386498630046844,
"agent": "setfit"
} | [] | It's not clear to me that Democrats are opposed to border-wall construction (your one source suggests they are fine with border walls/fences where necessary), so much as they opposed to some of the plans for full-border walls that have been put forth so far.
Recently, in response to reports that the spending bill would... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 54, "passed_quality_check": "True", "flesch_reading_ease": 80.30636363636367, "entropy": 0.3577953965491265} | {
"prompt-similarity": [
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0.012709199450910091,
-0.023340899497270584,
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0.... |
I'm converting a video to GIF file with ```ffmpeg```:
```ffmpeg \
-i input.flv \
-ss 00:00:00.000 \
-pix_fmt rgb24 \
-r 10 \
-s 320x240 \
-t 00:00:10.000 \
output.gif
```
It works great, but output gif file has a very low quality.
Any ideas how can I improve quality of converted gif? | null | [] | creative_writing | {
"type": null,
"score": 0.15073613822460175,
"agent": "setfit"
} | [] | If you would prefer to avoid intermediate image files, the commands provided by LordNeckBeard can be piped between ```ffmpeg``` and ImageMagick's ```convert``` so that no intermediate files are required:
```ffmpeg -i input.flv -vf scale=320:-1 -r 10 -f image2pipe -vcodec ppm - | convert -delay 10 -loop 0 - output.gif
`... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 243, "passed_quality_check": "False", "flesch_reading_ease": 64.08676470588239, "entropy": 0.972318343420335} | {
"prompt-similarity": [
-0.006318738684058189,
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0.04691997542977333,
-0.002028796123340726,
-0.03374088183045387,
0.0176463071256876,
-0.... |
Tor can only handle TCP connections, but DNS is a UDP protocol. How does Tor route DNS requests over its TCP based network? Why can the same approach not be used to route all UDP traffic over Tor? | null | [] | summarization | {
"type": null,
"score": 0.15275253355503082,
"agent": "setfit"
} | [] | Tor clients do not, in general, directly do DNS requests. When you open a connection through the tor network you usually ask your client (on its socks ports) to connect you to a hostname and port, say www.example.com:80.
Your tor client, once it has created a circuit, will send that hostname and port to the exit node ... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 159, "passed_quality_check": "True", "flesch_reading_ease": 96.02043859649125, "entropy": 0.8163104643214635} | {
"prompt-similarity": [
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0.05966678634285927,
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0.016762491315603256,
0... |
Why does this throw ```NullPointerException```
```public static void main(String[] args) throws Exception {
Boolean b = true ? returnsNull() : false; // NPE on this line.
System.out.println(b);
}
public static Boolean returnsNull() {
return null;
}
```
while this doesn't
```public static void main(String[] ... | null | [] | closed_qa | {
"type": null,
"score": 0.23641127347946167,
"agent": "setfit"
} | [] | The difference is that the explicit type of the ```returnsNull()``` method affects the static typing of the expressions at compile time:
```E1: `true ? returnsNull() : false` - boolean (auto-unboxing 2nd operand to boolean)
E2: `true ? null : false` - Boolean (autoboxing of 3rd operand to Boolean)
```
See Java Language... | {
"type": null,
"score": null,
"agent": null
} | null | {"n_characters": 603, "passed_quality_check": "False", "flesch_reading_ease": 79.433370125092, "entropy": 2.6997410863531037} | {
"prompt-similarity": [
-0.046802956610918045,
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0.014486245810985565,
-0.06420960277318954,
-0.03457311913371086,
-0.04995397478342056,
0... |
End of preview. Expand in Data Studio
Dataset Card for textclass_descriptives_vectors
This dataset has been created with Argilla.
As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.
Dataset Summary
This dataset contains:
A dataset configuration file conforming to the Argilla dataset format named
argilla.yaml. This configuration file will be used to configure the dataset when using theFeedbackDataset.from_huggingfacemethod in Argilla.Dataset records in a format compatible with HuggingFace
datasets. These records will be loaded automatically when usingFeedbackDataset.from_huggingfaceand can be loaded independently using thedatasetslibrary viaload_dataset.The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
Load with Argilla
To load with Argilla, you'll just need to install Argilla as pip install argilla --upgrade and then use the following code:
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("nataliaElv/textclass_descriptives_vectors")
Load with datasets
To load this dataset with datasets, you'll just need to install datasets as pip install datasets --upgrade and then use the following code:
from datasets import load_dataset
ds = load_dataset("nataliaElv/textclass_descriptives_vectors")
Supported Tasks and Leaderboards
This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.
There are no leaderboards associated with this dataset.
Languages
[More Information Needed]
Dataset Structure
Data in Argilla
The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.
The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
|---|---|---|---|---|
| prompt | Prompt | text | True | True |
| context | Context | text | False | True |
The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
|---|---|---|---|---|---|
| class | Classify the instruction according to its class | label_selection | True | N/A | ['closed_qa', 'classification', 'open_qa', 'information_extraction', 'brainstorming', 'general_qa', 'summarization', 'creative_writing'] |
| response | Response | text | True | N/A | N/A |
The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the metadata_properties defined in the dataset configuration file in argilla.yaml.
✨ NEW The vectors are different columns that contain a vector in floating point, which is constraint to the pre-defined dimensions in the vectors_settings when configuring the vectors within the dataset itself, also the dimensions will always be 1-dimensional. The vectors are optional and identified by the pre-defined vector name in the dataset configuration file in argilla.yaml.
| Vector Name | Title | Dimensions |
|---|---|---|
| prompt-similarity | prompt-similarity | [1, 768] |
| Metadata Name | Title | Type | Values | Visible for Annotators |
|---|---|---|---|---|
| n_characters | No. of characters | integer | None - None | True |
| passed_quality_check | Passed quality check? | terms | - | True |
| flesch_reading_ease | Reading ease | float | None - None | True |
| entropy | Entropy | float | None - None | True |
The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.
Data Instances
An example of a dataset instance in Argilla looks as follows:
{
"external_id": null,
"fields": {
"prompt": "Can brain cells move? By movement I mean long distance migration (preferably within the brain only)."
},
"metadata": {
"entropy": 0.4352176404374839,
"flesch_reading_ease": 82.39000000000001,
"n_characters": 85,
"passed_quality_check": "True"
},
"responses": [],
"suggestions": [
{
"agent": "setfit",
"question_name": "class",
"score": 0.2823514938354492,
"type": null,
"value": "brainstorming"
},
{
"agent": null,
"question_name": "response",
"score": null,
"type": null,
"value": "The question is relatively broad and one should take into account that the brain not only consists of neurons, but also glial cells (supportive cells) and pre-mitotic neuronal stem cells. Furthermore, as critical fellow-scientists have indicated, developmental stage is very important, as the developing embryonic brain is very different from the adult brain.\nHowever, after sifting through various publications, the answer to the question is actually remarkably simple: Yes, brain cells migrate.\nIn the adult brain glial cells migrate in the brain (Kl\u00e4mbt, 2009). Glial cells are involved in a myriad of functions, but a notable example of migrating glial cells are the oligodendrocytes that migrate relative long distances to find their target axons onto which they wrap themselves to form the insulating myelin sheath (Tsai and Miller, 2002).\nNeuronal stem cells migrate over long distances in response to injury (Imitola et al., 2004) and they migrate from specific stem-cell locations (e.g., hippocampus and subventricular zone) to other regions (Clarke, 2003).\nPost-mitotic, but non-differentiated neurons have been shown to migrate in the adult brain in fish (Scott et al., 2012), and in mammals and non-human primates as well (Sawada et al., 2011).\nNot surprisingly, glial cells, stem cells and neurons also migrate during embryonic development. Most notably, post-mitotic neurons destined to fulfill peripheral functions have to migrate over relatively long distances from the neural crest to their target locations (Neuroscience, 2nd ed, Neuronal Migration)."
}
],
"vectors": {
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]
}
}
While the same record in HuggingFace datasets looks as follows:
{
"class": [],
"class-suggestion": "brainstorming",
"class-suggestion-metadata": {
"agent": "setfit",
"score": 0.2823514938354492,
"type": null
},
"context": null,
"external_id": null,
"metadata": "{\"n_characters\": 85, \"passed_quality_check\": \"True\", \"flesch_reading_ease\": 82.39000000000001, \"entropy\": 0.4352176404374839}",
"prompt": "Can brain cells move? By movement I mean long distance migration (preferably within the brain only).",
"response": [],
"response-suggestion": "The question is relatively broad and one should take into account that the brain not only consists of neurons, but also glial cells (supportive cells) and pre-mitotic neuronal stem cells. Furthermore, as critical fellow-scientists have indicated, developmental stage is very important, as the developing embryonic brain is very different from the adult brain.\nHowever, after sifting through various publications, the answer to the question is actually remarkably simple: Yes, brain cells migrate.\nIn the adult brain glial cells migrate in the brain (Kl\u00e4mbt, 2009). Glial cells are involved in a myriad of functions, but a notable example of migrating glial cells are the oligodendrocytes that migrate relative long distances to find their target axons onto which they wrap themselves to form the insulating myelin sheath (Tsai and Miller, 2002).\nNeuronal stem cells migrate over long distances in response to injury (Imitola et al., 2004) and they migrate from specific stem-cell locations (e.g., hippocampus and subventricular zone) to other regions (Clarke, 2003).\nPost-mitotic, but non-differentiated neurons have been shown to migrate in the adult brain in fish (Scott et al., 2012), and in mammals and non-human primates as well (Sawada et al., 2011).\nNot surprisingly, glial cells, stem cells and neurons also migrate during embryonic development. Most notably, post-mitotic neurons destined to fulfill peripheral functions have to migrate over relatively long distances from the neural crest to their target locations (Neuroscience, 2nd ed, Neuronal Migration).",
"response-suggestion-metadata": {
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}
}
Data Fields
Among the dataset fields, we differentiate between the following:
Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
- prompt is of type
text. - (optional) context is of type
text.
- prompt is of type
Questions: These are the questions that will be asked to the annotators. They can be of different types, such as
RatingQuestion,TextQuestion,LabelQuestion,MultiLabelQuestion, andRankingQuestion.- class is of type
label_selectionwith the following allowed values ['closed_qa', 'classification', 'open_qa', 'information_extraction', 'brainstorming', 'general_qa', 'summarization', 'creative_writing']. - response is of type
text.
- class is of type
Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
- (optional) class-suggestion is of type
label_selectionwith the following allowed values ['closed_qa', 'classification', 'open_qa', 'information_extraction', 'brainstorming', 'general_qa', 'summarization', 'creative_writing']. - (optional) response-suggestion is of type
text.
- (optional) class-suggestion is of type
✨ NEW Vectors: As of Argilla 1.19.0, the vectors have been included in order to add support for similarity search to explore similar records based on vector search powered by the search engine defined. The vectors are optional and cannot be seen within the UI, those are uploaded and internally used. Also the vectors will always be optional, and only the dimensions previously defined in their settings.
- (optional) prompt-similarity is of type
float32and has a dimension of (1,768).
- (optional) prompt-similarity is of type
Additionally, we also have two more fields that are optional and are the following:
- metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the
metadata_propertiesdefined in the dataset configuration file inargilla.yaml. - external_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
Data Splits
The dataset contains a single split, which is train.
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 guidelines
This is a supervised fine-tuning dataset that contains instructions. Please write the response to the instruction in the response field. Take the context into account when writing the response.
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
Contributions
[More Information Needed]
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