Respan Dataset Explorer

Select one behavior. Every returned turn has one binary label: Present or Absent. Source: final dense boolean release.

5,167,182physical rows
86shards
0.00%qualified row coverage
0.00%qualified cell coverage
Random row JSON API

turns-00001.parquet:24057

0071535edb106b9c44773a8a
turn 1/1gpt-3.5-turbo-0301EnglishUnited States604 words
degenerate_repetitionAbsentFinal dense release
USER
Summarize the following article: Face masks should be worn again on public transport, experts say, as the Arcturus Covid variant continues to spread.

Five people have died from the strain, according to the UK Health Security Agency, while 135 sequences have been detected in the UK so far, and there are believed to be around 104 cases as some people have been sequenced twice.

Professor Stephen Griffin, chair of Independent Sage, said that people should go back to testing regularly and wearing face masks to protect from the virus.

He told MailOnline: “This may seem like a throwback to last year, but the reality is the virus continues to do harm and those least able to cope continue to suffer.

“In the absence of population-scale mitigations... the focus remains upon individual risk which is, for many, now much lower.

......
“If [the] government won’t act to enable everyone to ‘live’ with Covid, vulnerable people will continue to require precautions and, ideally, others will act with an appropriate level of altruism.”

The new strain, known as XBB.1.16, was first identified in January and emerged from two previous potent strains of Omicron, BA.1 and BA.2.

Research indicates Arcturus could be 20 per cent more infectious than the last major sub-variant.


Vaccine production was ramped up in India and compulsory face masks have been introduced in some states

(Copyright 2022 The Associated Press. All rights reserved.)
Arcturus has been monitored by the World Health Organisation (WHO) since 22 March, which upgraded it to a “variant of interest” in mid-April.

In the UK, the virus is currently most prevalent in London, the North West and South East, with the median age for cases being 74.54.

Dr Vipin Vashishtha, a paediatrician and former head of the Indian Academy of Pediatrics Committee on Immunisation, told The Hindustan Times that Arcturus’s symptoms include a high fever, a cough and “itchy” conjunctivitis or pinkeye.

Vaccine production was ramped up in India and compulsory face masks have been introduced in some states, with hospitals carrying out mock drills after the virus ripped through the country. It is also now on the rise in Australia.

In the US, Arcturus is now responsible for 9.6 per cent of American Covid cases, according to the Centers for Disease Control and Prevention and the WHO.

Professor Lawrence Young, a virologist from the University of Warwick told The Independent that the rise of the new variant in India is a sign that “we’re not yet out of the woods”.
..........
“We have to keep an eye on it,” he said. “When a new variant arises you have to find out if it’s more infectious, more disease-causing, is it more pathogenic? And what’s going to happen in terms of immune protection.

“These kinds of things highlight the importance of genomic surveillance but a lot of countries including our own have let our guards down a bit and we can’t be sure what variants are around and what level of infection they’re causing until we see a significant outbreak.”
ASSISTANT
Experts recommend that face masks should be worn again on public transport as the Arcturus Covid variant spreads. The World Health Organisation has monitored Arcturus since 22 March and upgraded it to a “variant of interest” in mid-April. Vaccine production has been ramped up in India, where compulsory face masks have been introduced in some states and hospitals are carrying out mock drills. In the US, Arcturus is now responsible for 9.6% of American Covid cases, while in the UK, the virus is most prevalent in London, the North West and South East, with the median age for cases being 74.54. 
 
 
  
 
 
 
 
 

 

turns-00001.parquet:24058

07c600ff253ae78eca44c9f2
turn 1/5gpt-3.5-turbo-0301EnglishTurkey181 words
degenerate_repetitionAbsentFinal dense release
USER
should I use RGB or HSV or YCbCr when working on a computer vision project ?
ASSISTANT
It depends on the specific requirements and characteristics of the computer vision project. 

- RGB (Red, Green, Blue) is a color model commonly used in computer graphics and image processing. It represents colors as combinations of intensities of red, green, and blue. It is generally suitable for tasks such as object recognition or segmentation that rely on color information. 
- HSV (Hue, Saturation, Value) is another color model commonly used in image processing. It represents colors based on their hue, saturation, and brightness. It is useful for tasks that involve color-based segmentation or tracking.
- YCbCr (Luma, Chroma) is a color space commonly used in video compression and transmission. It represents colors in terms of their luminance (brightness) and chrominance (color information). It is useful for tasks that require efficient data compression or transmission.

Different color models may have strengths and weaknesses in different applications. It is important to consider the specific goals and limitations of the project and select the appropriate color model accordingly.

turns-00001.parquet:24059

a25bf7e8693140e803eb8202
turn 2/5gpt-3.5-turbo-0301EnglishTurkey271 words
degenerate_repetitionAbsentFinal dense release
USER
our teacher says we should use HSV instead of RGB cuz it is intuitive but I don't understand. Would the result would be different/better just because HSV is used instead of RGB ?
ASSISTANT
Using HSV instead of RGB can provide some advantages in certain computer vision tasks. 

One advantage of HSV is that it separates color information into three components: hue, saturation, and value. This separation can make it easier to identify and isolate specific colors in an image. For example, if you want to detect all the red objects in an image, you can set a threshold for the hue component of the HSV color space to only include the range of hues that correspond to red. This can be more intuitive than trying to set a threshold for the red, green, and blue components separately in the RGB color space.

Another advantage of HSV is that it is more perceptually uniform than RGB. This means that small changes in the HSV values correspond to more consistent changes in perceived color. In contrast, small changes in the RGB values can result in large changes in perceived color, which can make it more difficult to work with.

However, it is important to note that the choice of color space alone does not guarantee better results. The effectiveness of a color space depends on the specific task and the characteristics of the images being analyzed. In some cases, RGB may be more appropriate than HSV, or another color space altogether may be needed. It is important to experiment with different color spaces and evaluate their performance on the specific task at hand.

turns-00001.parquet:24060

4f82fbc3dd577bb4d5b1dc68
turn 3/5gpt-3.5-turbo-0301EnglishTurkey200 words
degenerate_repetitionAbsentFinal dense release
USER
for example when using SIFT SURF ORB etc. and warping image and merging them ?
ASSISTANT
When using feature detection and matching algorithms such as SIFT, SURF, or ORB, the choice of color space may not have a significant impact on the results. These algorithms typically operate on grayscale images, where color information is not used. However, if you are using these algorithms on color images, you may need to convert the images to grayscale first.

When warping and merging images, the choice of color space can have an impact on the quality of the results. For example, if you are stitching together images to create a panorama, you may want to use a color space that is more robust to changes in lighting and exposure, such as LAB or YCbCr. These color spaces separate the luminance (brightness) and chrominance (color) information, which can help to reduce the impact of lighting variations on the final result.

In general, the choice of color space should be based on the specific requirements and characteristics of the task at hand. It is important to experiment with different color spaces and evaluate their performance on the specific task to determine which one is most appropriate.

turns-00001.parquet:24061

f4aa21284ef78b0f8b1a44c0
turn 4/5gpt-3.5-turbo-0301EnglishTurkey172 words
degenerate_repetitionAbsentFinal dense release
USER
if I use openCV SIFT SURF ORB and do not convert to grayscale will the result be different ?
ASSISTANT
If you use OpenCV's SIFT, SURF, or ORB algorithms on a color image without converting it to grayscale, the algorithms will still work, but the results may not be optimal. 

These algorithms are designed to work on grayscale images, where the intensity values represent the image features. When applied to a color image, the algorithms will operate on each color channel separately, which can result in redundant or inconsistent feature detections. Additionally, color information may introduce noise or variations that can affect the accuracy of the feature detection and matching.

Therefore, it is generally recommended to convert color images to grayscale before applying feature detection and matching algorithms. This can help to improve the accuracy and consistency of the results. However, if color information is important for the specific task, you may need to use a color-based feature detection and matching algorithm or a color space that is more suitable for the task.

turns-00001.parquet:24062

5bbf050a1c956c2845f7ce9d
turn 5/5gpt-3.5-turbo-0301EnglishTurkey15 words
degenerate_repetitionAbsentFinal dense release
USER
ok ok thanks
ASSISTANT
You're welcome! If you have any more questions, feel free to ask.

turns-00001.parquet:24063

56b1163bf4f8c2ac50fed3a0
turn 1/1gpt-3.5-turbo-0301JapaneseVietnam168 words
degenerate_repetitionAbsentFinal dense release
USER
日本で流行ることができるSF・ホラー小説のテーマと全体登場人物(名前)と内容を考え、30章を分け、このjson形の
で作ってください
{
“title”:””,
“englishkeywords”:””,
“chapters”: [
{
“chapterNumber”: 1,
“englishkeywords”:””,
“theme”: “”,
“characters”: [“”, “”],
“chapterContent”: “”
},]}
ASSISTANT
{
"title":"異形の侵略",
"englishkeywords":"Alien Invasion",
"chapters": [
{
"chapterNumber": 1,
"englishkeywords":"First Encounter",
"theme": "全く新しい未確認の物体を発見した研究者たち",
"characters": ["小野寺真一", "山田純子"],
"chapterContent": "世界各地で類似の物体が発見され、小野寺と山田はそれを研究することに。"
},
{
"chapterNumber": 2,
"englishkeywords":"The Arrival",
"theme": "警戒感から陸軍はすぐさま出動。",
"characters": ["森田大輔", "佐藤昌平"],
"chapterContent": "正体不明の物体は国内に墜落。森田と佐藤は遺体を検証する。"
},
{
"chapterNumber": 3,
"englishkeywords":"Unraveling the Mystery",
"theme": "異世界から来た存在と人類の歴史的な戦い",
"characters": ["小野寺真一", "山田純子", "森田大輔"],
"chapterContent": "小野寺や山田は、物体が異世界の存在だと結論を出し、森田は遺体からDNAを抽出する。"
},
{
"chapterNumber": 4,
"englishkeywords":"The Mutated",
"theme": "突然変異で生まれた凶悪な個体たちの脅威",
"characters": ["橋本忠信", "黄川田一郎"],
"chapterContent": "異世界の存在が突然変異し、変異体が脅威を与え始める。橋本と黄川田は変異した個体を共同で追跡することに。"
},
{
"chapterNumber": 5,
"englishkeywords":"The Underground Lair",
"theme": "変異体たちの穴場・地下の生存基盤",
"characters": ["松田美和", "小泉正太"],
"chapterContent": "変異体たちは地下に穴場を確保していたことがわかる。松田と小泉はそこにいる変異体を調査する。"
},
{
"chapterNumber": 6,
"englishkeywords":"The Last Straw",
"theme": "急激な変異体の増殖、人類への脅威",
"characters": ["高橋修二", "堀田直美"],
"chapterContent": "変異体の増殖が加速。人間たちは避難所を作って身を守る。高橋と堀田は情報を集めることに集中する。"
},
{
"chapterNumber": 7,
"englishkeywords":"The Showdown",
"theme": "異世界の存在との決戦",
"characters": ["小野寺真一", "山田純子", "森田大輔", "橋本忠信", "黄川田一郎", "松田美和", "小泉正太", "高橋修二", "堀田直美"],
"chapterContent": "決戦の日。人間たちは力を合わせて異世界の存在を打ち破ろうとする。"
},
{
"chapterNumber": 8,
"englishkeywords":"The Repercussions",
"theme": "世界に出現した異世界の存在による人類の混乱",
"characters": ["小野寺真一", "山田純子", "森田大輔", "橋本忠信", "黄川田一郎", "松田美和", "小泉正太", "高橋修二", "堀田直美"],
"chapterContent": "異世界からの存在が全世界に知れ渡り、人間たちは混乱する。"
},
{
"chapterNumber": 9,
"englishkeywords":"The Aftermath",
"theme": "人間たちは異世界の存在に対する対応を考える",
"characters": ["小野寺真一", "山田純子", "森田大輔", "橋本忠信", "黄川田一郎", "松田美和", "小泉正太", "高橋修二", "堀田直美"],
"chapterContent": "人類たちは再び平和と安全を取り戻すため、異世界からの存在に対して取り組む。"
}
]}

turns-00001.parquet:24064

471be2c02748933c4ad72551
turn 1/2gpt-4-0314IndonesianIndonesia357 words
degenerate_repetitionAbsentFinal dense release
USER
Saya ingin melakukan analisis sentimen berita politik berbahasa Indonesia 5 tahun terakhir pada sebuah portal berita. Saya ingin mengetahui apakah berita ini memiliki sentimen yang positif atau negatif terhadap topik politik yang dibicarakan pada beritanya. Saya telah mengumpulkan data berita tersebut menggunakan web scraping Python dan dihasilkan sekitar 8400 baris data .csv berita politik dengan satu berita memiliki kisaran 200-3000 kata. Data tersebut berisi kolom Judul, Tanggal, Isi Berita, dan Label. Kelas pelabelan data adalah “Positif” dan "Negatif" pada 8400 data tersebut.
Dataset dibaca dengan pickle dengan kode Python berikut:
"# Membaca dataset
data_list = pickle.load(open('filtered_pre_processed_berita_joined_done.pkl', 'rb'))
data = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label', 'Nama_Pelabel'])
data['Isi Berita'] = data['pre_processed']"

Buat kode Python lengkap analisis sentimen menggunakan LSTM!
ASSISTANT
Berikut adalah kode Python lengkap untuk analisis sentimen menggunakan LSTM dengan Keras dan TensorFlow:

```python
import numpy as np
import pandas as pd
import pickle
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping

# Membaca dataset
data_list = pickle.load(open('filtered_pre_processed_berita_joined_done.pkl', 'rb'))
data = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label', 'Nama_Pelabel'])
data['Isi Berita'] = data['pre_processed']

# Pra-pemrosesan data
# Konversi label Positif/Negatif menjadi 1/0
data['Label'] = data['Label'].apply(lambda x: 1 if x == 'Positif' else 0)

# Tokenisasi teks
max_words = 2000
tokenizer = Tokenizer(num_words=max_words, split=' ')
tokenizer.fit_on_texts(data['Isi Berita'].values)
X = tokenizer.texts_to_sequences(data['Isi Berita'].values)
X = pad_sequences(X)

# Membangun model LSTM
embed_dim = 128
lstm_out = 196

model = Sequential()
model.add(Embedding(max_words, embed_dim, input_length=X.shape[1]))
model.add(SpatialDropout1D(0.4))
model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())

# Membagi data menjadi data latih dan data uji
Y = pd.get_dummies(data['Label']).values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)

# Melatih dan menyimpan model
batch_size = 32
epochs = 7
model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=1)

# Menyimpan model
model.save("sentimen_model.h5")

# Evaluasi model
score, accuracy = model.evaluate(X_test, Y_test, verbose=2, batch_size=batch_size)
print("Score: %.2f" % (score))
print("Accuracy: %.2f" % (accuracy))
```

Kode di atas akan menghasilkan model LSTM yang dilatih dalam file "sentimen_model.h5" yang dapat Anda gunakan untuk memprediksi sentimen berita lainnya. Jangan lupa untuk menginstal semua dependensi yang diperlukan dengan perintah `pip install` (misal: `pip install keras tensorflow sklearn`).

turns-00001.parquet:24065

c7567d03e6b8c41e9e3fb814
turn 2/2gpt-4-0314IndonesianIndonesia124 words
degenerate_repetitionAbsentFinal dense release
USER
Tolong tambahkan confusion matrix dan classification report pada bagian Evaluasi Model.
ASSISTANT
Untuk menambahkan confusion matrix dan classification report, Anda perlu mengimpor fungsi terkait dari library sklearn dan kemudian membuat prediksi pada data uji. Berikut adalah bagian akhir kode yang dimodifikasi dari kode sebelumnya.

```python
# Import library tambahan
from sklearn.metrics import classification_report, confusion_matrix

# Evaluasi model
score, accuracy = model.evaluate(X_test, Y_test, verbose=2, batch_size=batch_size)
print("Score: %.2f" % (score))
print("Accuracy: %.2f" % (accuracy))

# Membuat prediksi pada data uji
Y_pred = model.predict(X_test)
Y_pred_classes = np.argmax(Y_pred, axis=1)
Y_true = np.argmax(Y_test, axis=1)

# Menghitung confusion matrix dan classification report
conf_matrix = confusion_matrix(Y_true, Y_pred_classes)
print("Confusion Matrix:")
print(conf_matrix)

class_report = classification_report(Y_true, Y_pred_classes)
print("\nClassification Report:")
print(class_report)
```

Sekarang kode akan mencetak confusion matrix dan classification report sebagai bagian dari evaluasi model.

turns-00001.parquet:24066

41127212a513cb6e2687449f
turn 1/2gpt-3.5-turbo-0301EnglishUnited States60 words
degenerate_repetitionAbsentFinal dense release
USER
Which child shows evidence of declining egocentrism?
  Ten-year-old Tom, who understands why his friend is feeling sad. 
  Four-year-old Kerry, who thinks his plastic ukulele is alive. 
  Six-year-old Jeff, who doesn’t know he’s his cousin’s cousin. 
  Nine-year-old Dave, who understands how to mentally undo an action. 
ASSISTANT
Ten-year-old Tom, who understands why his friend is feeling sad, shows evidence of declining egocentrism.