16dvnk/AaI_mini.plus_beta_250824_Base
Text Generation
•
Updated
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10
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1
Search is not available for this dataset
0
float64 -0.16
0.21
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float64 -0.16
0.14
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float64 -0.18
0.19
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float64 -0.16
0.15
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float64 -0.2
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0.17
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float64 -0.15
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float64 -0.17
0.2
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float64 -0.14
0.13
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float64 -0.14
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float64 -0.14
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float64 -0.14
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float64 -0.14
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float64 -0.14
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float64 -0.16
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float64 -0.19
0.2
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0.15
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float64 -0.17
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float64 -0.18
0.2
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float64 -0.14
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float64 -0.2
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float64 -0.2
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| 0.051087
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| -0.002271
| 0.002746
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| 0.031854
| 0.040535
| -0.025757
| -0.01622
| 0.073893
| -0.010525
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| -0.048189
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| 0.025519
| -0.019491
| 0.008121
| 0.014315
| -0.006221
| 0.051677
| 0.088029
| 0.016382
| -0.0518
| 0.012302
| -0.020941
| 0.000103
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| -0.012701
| -0.036966
| 0.078627
| 0.040255
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| 0.049168
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| 0.063534
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| -0.037793
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| -0.001601
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| 0.025867
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| 0.103167
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| 0.006078
| 0.039304
| -0.059832
| -0.00921
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| 0.033222
| 0.050839
| 0.003564
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| -0.027637
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| -0.047827
| 0.015333
| -0.013699
| -0.054657
| -0.006761
| 0.006477
| 0.028326
| -0.017588
| -0.001716
| -0.026642
| 0.076323
| 0.008866
| -0.048573
| 0.022926
| 0.038257
| -0.003524
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| 0.052838
| -0.007819
|
End of preview. Expand
in Data Studio
Only for the reaseaching usage.
The original data from http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz.
We use nq_distilbert-base-v1 model encode all the data to the PyTorch Tensors. And normalize the embeddings by using sentence_transformers.util.normalize_embeddings.
How to use
See notebook Wikipedia Q&A Retrieval-Semantic Search
Installing the package
!pip install sentence-transformers==2.3.1
The converting process
# the whole process takes 1287.0s GPU P100
import os
import json
import gzip
from sentence_tranformers.util import http_get
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import normalize_embeddings
os.environ['DATASET_NAME']='simplewiki-2020-11-01.jsonl.gz'
os.environ['DATASET_URL']='http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz'
os.environ['MODEL_NAME']='multi-qa-MiniLM-L6-cos-v1'
os.environ['CROSS_CODE_NAME']='cross-encoder/ms-marco-MiniLM-L-6-v2'
http_get(os.getenv('DATASET_URL'), os.getenv('DATASET_NAME'))
passages=[]
with gzip.open(os.getenv('DATASET_NAME'), 'rt', encoding='utf-8') as fIn:
for line in fIn:
data=json.loads(line.strip())
# add all paragraphs
# passages.extend(data['paragraphs'])
# only add the first paragraph
# passages.append(data['paragraph'][0])
for paragraph in data['paragraphs']:
# We encode the passages as [title, text]
passages.append([data['title'], paragraph])
print('Passages:', len(passages))
bi_encoder=SentenceTransformer('nq-distilbert-base-v1')
bi_encoder.max_seq_length=256
bi_encoder.to('cuda')
corpus_embeddings=bi_encoder.encode(passages, convert_to_tensor=True, show_progress_bar=True).to('cuda')
corpus_embeddings=normalize_embeddings(corpus_embeddings)
len(corpus_embeddings)
import pandas as pd
embedding_data=pd.DataFrame(corpus_embeddings.cpu())
embedding_data.to_csv('simple_english_wikipedia_2020_11_01.csv', index=False)
- Downloads last month
- 25
Size of downloaded dataset files:
4.84 GB
Size of the auto-converted Parquet files:
3.73 GB
Number of rows:
509,663