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 |
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SirPumpernickle/testsplat | SirPumpernickle | 2023-10-27T13:28:00Z | 0 | 0 | null | [
"license:unknown",
"doi:10.57967/hf/1279",
"region:us"
] | 2023-10-27T13:28:00Z | 2023-10-27T13:19:18.000Z | 2023-10-27T13:19:18 | ---
license: unknown
---
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chirunder/GRE_synonyms_gregmat | chirunder | 2023-10-27T13:20:07Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T13:20:07Z | 2023-10-27T13:20:03.000Z | 2023-10-27T13:20:03 | ---
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---
# Dataset Card for "GRE_synonyms_gregmat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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polyhedralai/tech_reports_mining | polyhedralai | 2023-10-27T13:30:58Z | 0 | 0 | null | [
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Elwii/train | Elwii | 2023-10-27T13:36:25Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-27T13:36:25Z | 2023-10-27T13:35:04.000Z | 2023-10-27T13:35:04 | ---
license: apache-2.0
---
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facat/sft-train-samples | facat | 2023-10-28T04:27:28Z | 0 | 0 | null | [
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] | 2023-10-28T04:27:28Z | 2023-10-27T13:49:23.000Z | 2023-10-27T13:49:23 | ---
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---
# Dataset Card for "sft-train-samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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thanhduycao/soict_sentence_filter | thanhduycao | 2023-10-27T13:49:41Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T13:49:41Z | 2023-10-27T13:49:40.000Z | 2023-10-27T13:49:40 | ---
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path: data/train-*
---
# Dataset Card for "soict_sentence_filter"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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SaiedAlshahrani/ASAD | SaiedAlshahrani | 2023-10-29T18:48:48Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"language:ar",
"license:mit",
"region:us"
] | 2023-10-29T18:48:48Z | 2023-10-27T13:55:52.000Z | 2023-10-27T13:55:52 | ---
license: mit
language:
- ar
pretty_name: ASAD
size_categories:
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---
# Dataset Card for "Arab States Analogy Dataset (ASAD)"
This dataset is created using 20 Arab States<sup>1</sup> with their corresponding capital cities, nationalities, currencies, and on which continents they are located, consisting of four sets: country-capital set, country-currency set, country-nationality set, and country-continent set. Each set has 380 word analogies, and the total number of word analogies in the ASAD dataset is 1520. This dataset is used to evaluate Arabic Word Embedding Models (WEMs).
For more details about the dataset, please **read** and **cite** our paper:
```bash
@inproceedings{alshahrani-etal-2023-implications,
title = "{{Performance Implications of Using Unrepresentative Corpora in Arabic Natural Language Processing}}",
author = "Alshahrani, Saied and Alshahrani, Norah and Dey, Soumyabrata and Matthews, Jeanna",
booktitle = "Proceedings of the The First Arabic Natural Language Processing Conference (ArabicNLP 2023)",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://webspace.clarkson.edu/~alshahsf/unrepresentative_corpora.pdf",
doi = "#################",
pages = "###--###",
abstract = "Wikipedia articles are a widely used source of training data for Natural Language Processing (NLP) research, particularly as corpora for low-resource languages like Arabic. However, it is essential to understand the extent to which these corpora reflect the representative contributions of native speakers, especially when many entries in a given language are directly translated from other languages or automatically generated through automated mechanisms. In this paper, we study the performance implications of using inorganic corpora that are not representative of native speakers and are generated through automated techniques such as bot generation or automated template-based translation. The case of the Arabic Wikipedia editions gives a unique case study of this since the Moroccan Arabic Wikipedia edition (ARY) is small but representative, the Egyptian Arabic Wikipedia edition (ARZ) is large but unrepresentative, and the Modern Standard Arabic Wikipedia edition (AR) is both large and more representative. We intrinsically evaluate the performance of two main NLP upstream tasks, namely word representation and language modeling, using word analogy evaluations and fill-mask evaluations using our two newly created datasets: Arab States Analogy Dataset (ASAD) and Masked Arab States Dataset (MASD). We demonstrate that for good NLP performance, we need both large and organic corpora; neither alone is sufficient. We show that producing large corpora through automated means can be a counter-productive, producing models that both perform worse and lack cultural richness and meaningful representation of the Arabic language and its native speakers.",
}
```
<sub>1. We only drop two Arab states: the United Arab Emirates (الإمارات العربية المتحدة) and Comoros (جزر القمر), because they or their capital cities are written as open compound words (two words), which cannot be directly handled by the word embedding models, like Abu Dhabi (أبو ظبي).</sub> | [
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BiancaZYCao/GRIT_food | BiancaZYCao | 2023-10-27T14:15:20Z | 0 | 0 | null | [
"license:ms-pl",
"region:us"
] | 2023-10-27T14:15:20Z | 2023-10-27T14:04:26.000Z | 2023-10-27T14:04:26 | ---
license: ms-pl
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Frorozcol/LLaVa-instruction-trasaleted | Frorozcol | 2023-10-27T14:18:56Z | 0 | 1 | null | [
"region:us"
] | 2023-10-27T14:18:56Z | 2023-10-27T14:18:29.000Z | 2023-10-27T14:18:29 | ---
configs:
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---
# Dataset Card for "LLaVa-instruction-trasaleted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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FunkyQ/NER_Assignment | FunkyQ | 2023-10-27T18:07:56Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T18:07:56Z | 2023-10-27T14:23:09.000Z | 2023-10-27T14:23:09 | ---
configs:
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---
# Dataset Card for "ner_assignment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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thanhduycao/soict_sentence_synthesis | thanhduycao | 2023-10-27T14:32:57Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T14:32:57Z | 2023-10-27T14:32:56.000Z | 2023-10-27T14:32:56 | ---
dataset_info:
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configs:
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---
# Dataset Card for "soict_sentence_synthesis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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makram93/accepted_pairs_st | makram93 | 2023-10-27T14:55:15Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T14:55:15Z | 2023-10-27T14:53:18.000Z | 2023-10-27T14:53:18 | ---
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---
# Dataset Card for "accepted_pairs_st"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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makram93/rejected_pairs_st | makram93 | 2023-10-27T14:55:18Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T14:55:18Z | 2023-10-27T14:53:21.000Z | 2023-10-27T14:53:21 | ---
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splits:
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dataset_size: 88447.0623234648
configs:
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path: data/train-*
---
# Dataset Card for "rejected_pairs_st"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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makram93/accepted_pairs_small | makram93 | 2023-10-27T14:59:08Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T14:59:08Z | 2023-10-27T14:57:39.000Z | 2023-10-27T14:57:39 | ---
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download_size: 83182
dataset_size: 88447.0623234648
configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "accepted_pairs_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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makram93/rejected_pairs_small | makram93 | 2023-10-27T14:59:10Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T14:59:10Z | 2023-10-27T14:57:42.000Z | 2023-10-27T14:57:42 | ---
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dataset_size: 88447.0623234648
configs:
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---
# Dataset Card for "rejected_pairs_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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arubenruben/dummy-1 | arubenruben | 2023-11-12T18:46:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-12T18:46:18Z | 2023-10-27T15:18:38.000Z | 2023-10-27T15:18:38 | Entry not found | [
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mak048/bahria_admission | mak048 | 2023-10-27T15:35:56Z | 0 | 0 | null | [
"region:us"
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aneeshas/toy-tla-data | aneeshas | 2023-10-27T17:06:24Z | 0 | 0 | null | [
"region:us"
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---
# Dataset Card for "toy-tla-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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reddyprasade/Q_A_Dataset | reddyprasade | 2023-10-27T16:45:35Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-27T16:45:35Z | 2023-10-27T16:38:31.000Z | 2023-10-27T16:38:31 | ---
license: apache-2.0
---
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linhtran92/tts_male | linhtran92 | 2023-10-27T16:48:11Z | 0 | 0 | null | [
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---
# Dataset Card for "tts_male"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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thanhduycao/soict_train_dataset_filter_v2 | thanhduycao | 2023-10-27T16:53:41Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T16:53:41Z | 2023-10-27T16:52:45.000Z | 2023-10-27T16:52:45 | ---
configs:
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---
# Dataset Card for "soict_train_dataset_filter_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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19kmunz/iot-23-preprocessed-allcolumns | 19kmunz | 2023-11-03T16:44:31Z | 0 | 0 | null | [
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---
# Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic
**Homepage:** [https://www.stratosphereips.org/datasets-iot23](https://www.stratosphereips.org/datasets-iot23)
This dataset contains a subset of the data from 20 captures of Malcious network traffic and 3 captures from live Benign Traffic on Internet of Things (IoT) devices. Created by Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga at the Avast AIC laboratory with the funding of Avast Software, this dataset is one of the best in the field for Intrusion Detection Systems (IDS) for IoT Devices [(Comparative Analysis of IoT Botnet Datasets)](https://doi.org/10.53070/bbd.1173687).
The selection of the subset was determined by [Aqeel Ahmed on Kaggle](https://www.kaggle.com/datasets/engraqeel/iot23preprocesseddata) and contains 6 million samples. The Kaggle upload, nor this one, have employed data balancing. The Kaggle card does not contain methodology to understand what criteria was used to select these samples. If you want ensure best practice, use this dataset to mock-up processing the data into a model before using the full dataset with data balancing. This will require processing the 8GB of conn.log.labelled files.
# Feature information:
All features originate from the [Zeek](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info) processing performed by the dataset creators. [See notes here for caviats for each column](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info).
<details>
<summary>Expand for feature names, descriptions, and datatypes</summary>
Name: ts
Desription: This is the time of the first packet.
Data Type: float64 - Timestamp
Name: uid
Description: A Zeek-defined unique identifier of the connection.
Data type: string
Name: id.orig_h
Description: The originator’s IP address.
Data type: string - for the form 255.255.255.255 for IPv4 or [aaaa:bbbb:cccc:dddd:eeee:ffff:1111:2222] for IPv6
Name: id.orig_p
Description: The originator’s port number.
Data type: int64 - uint64 in original
Name: id.resp_h
Description: The responder’s IP address.
Data type: string - for the form 255.255.255.255 for IPv4 or [aaaa:bbbb:cccc:dddd:eeee:ffff:1111:2222] for IPv6
Name: id.resp_p
Description: The responder’s port number.
Data type: int64 - uint64 in original
Name: proto
Description: The transport layer protocol of the connection.
Data type: string - enum(unknown_transport, tcp, udp, icmp). Only TCP and UDP in subset
Name: service
Description: An identification of an application protocol being sent over the connection.
Data type: optional string
Name: duration
Description: How long the connection lasted.
Data type: optional float64 - time interval
Name: orig_bytes
Description: The number of payload bytes the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_bytes
Description:The number of payload bytes the responder sent.
Data type: optional int64 - uint64 in original
Name: conn_state
Description: Value indicating connection state. (S0, S1, SF, REJ, S2, S3, RSTO, RSTR, RSTOS0, RSTRH, SH, SHR, OTH)
Data type: optional string
Name: local_orig
Description: If the connection is originated locally, this value will be T. If it was originated remotely it will be F.
Data type: optional float64 - bool in original but null for all columns
Name: local_resp
Description: If the connection is responded to locally, this value will be T. If it was responded to remotely it will be F.
Data type: optional float64 - bool in original but null for all columns
Name: missed_bytes
Description: Indicates the number of bytes missed in content gaps, which is representative of packet loss.
Data type: optional int64 - uint64 in original. default = 0
Name: history
Description: Records the state history of connections as a string of letters.
Data type: optional string
Name: orig_pkts
Description: Number of packets that the originator sent.
Data type: optional int64 - uint64 in original
Name: orig_ip_bytes
Description: Number of IP level bytes that the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_pkts
Description: Number of packets that the responder sent.
Data type: optional int64 - uint64 in original
Name: resp_ip_bytes
Description: Number of IP level bytes that the responder sent.
Data type: optional int64 - uint64 in original
Name: label
Description: Specifies if data point is benign or some form of malicious. See the dataset creators paper for descriptions of attack types
Data type: string - enum('PartOfAHorizontalPortScan', 'Okiru', 'DDoS', 'C&C-HeartBeat',
'Benign', 'C&C-Torii', 'C&C', 'C&C-FileDownload', 'Okiru-Attack',
'Attack', 'FileDownload', 'C&C-HeartBeat-FileDownload',
'C&C-Mirai')
NOTE: ts, uid, id.orig_h, id.resp_h SHOULD BE removed as they are dataset specific. Models should not be trained with specific timestamps or IP addresses (id.orig_h), as that can lead to over fitting to dataset specific times and addresses.
Further local_orig, local_resp SHOULD BE removed as they are null in all rows, so they are useless for training.
</details>
## Citation
If you are using this dataset for your research, please reference it as “Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4743746”
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linhtran92/tts_female | linhtran92 | 2023-10-27T17:12:17Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T17:12:17Z | 2023-10-27T17:12:14.000Z | 2023-10-27T17:12:14 | ---
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---
# Dataset Card for "tts_female"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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linhtran92/tts_997 | linhtran92 | 2023-10-27T17:14:02Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T17:14:02Z | 2023-10-27T17:13:56.000Z | 2023-10-27T17:13:56 | ---
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---
# Dataset Card for "tts_997"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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MaxReynolds/TestUpload3 | MaxReynolds | 2023-10-27T17:22:48Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T17:22:48Z | 2023-10-27T17:22:44.000Z | 2023-10-27T17:22:44 | ---
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---
# Dataset Card for "TestUpload3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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quocanh34/private_prediction_1 | quocanh34 | 2023-10-27T17:24:09Z | 0 | 0 | null | [
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# Dataset Card for "private_prediction_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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sayan1101/finetune_run2 | sayan1101 | 2023-10-27T18:11:40Z | 0 | 0 | null | [
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# Dataset Card for "finetune_run2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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sayan1101/filtered_finetune_run2 | sayan1101 | 2023-10-27T19:47:24Z | 0 | 0 | null | [
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# Dataset Card for "filtered_finetune_run2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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citiusLTL/Twitter-COVID-19 | citiusLTL | 2023-10-27T18:35:38Z | 0 | 0 | null | [
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"region:us"
] | 2023-10-27T18:35:38Z | 2023-10-27T18:30:15.000Z | 2023-10-27T18:30:15 | ---
license: gpl-3.0
task_categories:
- text-classification
language:
- es
- en
---
**General description**:
This dataset comprisses a set of tweets crawled during the COVID-19 pandemic (from March 2020 to June 2021). Tweets are located in two different regions: Spain and USA. This adds value to the collection, as it contains data in two languages.
This data was used as part of a broader study that aimed to determine the evolution of different personality traits and disorders during the pandemic. Thus, weak labels for different dimensions, such as sentiment, personality prevalence, and others, are also available.
Further details about this experimentation can be found in the [paper](https://link.springer.com/article/10.1007/s10844-023-00810-3) or [Github](https://github.com/MarcosFP97/COVID-19-Personality).
**Data**:
A sample of the data can be visualised and downloaded from this card. More specifically, it corresponds to the month of January 2021 and tweets are located on USA. Tweets were anonymized for privacy reasons.
The whole dataset is available upon request to fullfil Twitter's restrictions. You can contact either with marcosfernandez.pichel@usc.es or ezra.aragon@usc.es to obtain it.
**Citation**:
For all the future studies using our data, we kindly ask to quote our paper:
@article{fernandez2023personality, \
title={Personality trait analysis during the COVID-19 pandemic: a comparative study on social media}, \
author={Fern{\'a}ndez-Pichel, Marcos and Arag{\'o}n, Mario Ezra and Saborido-Pati{\~n}o, Juli{\'a}n and Losada, David E}, \
journal={Journal of Intelligent Information Systems}, \
pages={1--26}, \
year={2023}, \
publisher={Springer} \
}
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license: apache-2.0
---
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# Dataset Card for "java_repo_star"
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ltg/lambada-context | ltg | 2023-10-30T10:53:07Z | 0 | 0 | null | [
"task_categories:text-generation",
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license: mit
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pretty_name: LAMBADA
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---
## Dataset Description
- **Repository:** [openai/gpt2](https://github.com/openai/gpt-2)
- **Paper:** Radford et al. [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf)
### Dataset Summary
This is the LAMBADA test split modified for bidirectional language models (for example BERT). The original is appended by punctuation symbols (for example `."`), as predicted by GPT-2 (small). The original is the LAMBADA test split [as pre-processed by OpenAI](https://huggingface.co/datasets/EleutherAI/lambada_openai),
LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole text, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse.
### Languages
English
### Source Data
[EleutherAI/lambada_openai](https://huggingface.co/datasets/EleutherAI/lambada_openai)
### Licensing
License: [Modified MIT](https://github.com/openai/gpt-2/blob/master/LICENSE)
### Citation
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
```bibtex
@misc{
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
title={The LAMBADA dataset},
DOI={10.5281/zenodo.2630551},
publisher={Zenodo},
year={2016},
month={Aug}
}
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ayoub999/test_1 | ayoub999 | 2023-10-28T17:07:24Z | 0 | 0 | null | [
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# Dataset Card for "test_1"
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# Dataset Card for "qa_blue_amazon_legislation_v2_68k"
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license: openrail
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# Dataset Card for "GRE_all_text"
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# Dataset Card for "Vince_GRE_frequency"
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dataset_size: 235174567045.0
---
# Dataset Card for "imagenet-1k-rand_hog"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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finleyhu/vogueman | finleyhu | 2023-10-28T06:44:14Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T06:44:14Z | 2023-10-28T06:42:48.000Z | 2023-10-28T06:42:48 | Entry not found | [
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quocanh34/private_model_tts3_no_denoise | quocanh34 | 2023-10-28T08:37:46Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T08:37:46Z | 2023-10-28T08:36:14.000Z | 2023-10-28T08:36:14 | ---
dataset_info:
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---
# Dataset Card for "private_model_tts3_no_denoise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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BossBossNJb/cifar10_dataset_th_en | BossBossNJb | 2023-10-28T09:13:12Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T09:13:12Z | 2023-10-28T09:13:04.000Z | 2023-10-28T09:13:04 | ---
configs:
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path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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names:
'0': airplane
'1': automobile
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---
# Dataset Card for "cifar10_dataset_th_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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acozma/imagenet-1k-rand_entropy | acozma | 2023-11-02T18:51:03Z | 0 | 0 | null | [
"region:us"
] | 2023-11-02T18:51:03Z | 2023-10-28T10:46:57.000Z | 2023-10-28T10:46:57 | Entry not found | [
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22Plaruno/100_image | 22Plaruno | 2023-10-28T11:40:25Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T11:40:25Z | 2023-10-28T11:36:08.000Z | 2023-10-28T11:36:08 | ---
configs:
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---
# Dataset Card for "100_image"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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22Plaruno/image | 22Plaruno | 2023-10-28T11:48:28Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T11:48:28Z | 2023-10-28T11:41:08.000Z | 2023-10-28T11:41:08 | ---
dataset_info:
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dataset_size: 10359951.0
configs:
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---
# Dataset Card for "image"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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MuGeminorum/emo163_playlists | MuGeminorum | 2023-10-28T15:18:05Z | 0 | 1 | null | [
"task_categories:audio-classification",
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] | 2023-10-28T15:18:05Z | 2023-10-28T12:07:08.000Z | 2023-10-28T12:07:08 | ---
license: mit
task_categories:
- audio-classification
language:
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- en
tags:
- music
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pretty_name: netease music playlist emotion classification
size_categories:
- 10K<n<100K
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datamol-io/safe-drugs | datamol-io | 2023-10-28T12:23:11Z | 0 | 0 | null | [
"license:cc-by-4.0",
"arxiv:2310.10773",
"region:us"
] | 2023-10-28T12:23:11Z | 2023-10-28T12:18:50.000Z | 2023-10-28T12:18:50 | ---
license: cc-by-4.0
configs:
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data_files:
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path: data/train-*
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num_bytes: 12691
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download_size: 18556
dataset_size: 12691
---
# SAFE
Sequential Attachment-based Fragment Embedding (SAFE) is a novel molecular line notation that represents molecules as an unordered sequence of fragment blocks to improve molecule design using generative models.
This is the drugs dataset used for benchmarking.
Find the details and how to use at SAFE in the repo https://github.com/datamol-io/safe or the paper https://arxiv.org/pdf/2310.10773.pdf. | [
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0.0615268908441066... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
eniokilder/Banco-Imagem | eniokilder | 2023-10-28T12:48:31Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T12:48:31Z | 2023-10-28T12:31:58.000Z | 2023-10-28T12:31:58 | # Projeto
Banco-Imagem
### Nome do aluno
Enio Kilder Oliveira da Silva
|**Tipo de Projeto**|**Modelo Selecionado**|**Linguagem**|
|--|--|--|
Classificação de Objetos |YOLOv5|PyTorch|
## Performance
O modelo treinado possui performance de **98.6%**.
### Output do bloco de treinamento
<details>
<summary>Expandir Conteúdo!</summary>
```text
%%time
%cd ../yolov5
!python classify/train.py --model yolov5n-cls.pt --data $DATASET_NAME --epochs 128 --batch 16 --img 320 --pretrained weights/yolov5n-cls.pt
/content/yolov5
2023-10-28 01:49:35.242300: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-10-28 01:49:35.242363: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-10-28 01:49:35.242406: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
classify/train: model=yolov5n-cls.pt, data=Banco-Imagem-1, epochs=128, batch_size=16, imgsz=320, nosave=False, cache=None, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=weights/yolov5n-cls.pt, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
TensorBoard: Start with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/
albumentations: RandomResizedCrop(p=1.0, height=320, width=320, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)
Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt to yolov5n-cls.pt...
100% 4.87M/4.87M [00:00<00:00, 48.4MB/s]
Model summary: 149 layers, 1218405 parameters, 1218405 gradients, 3.0 GFLOPs
optimizer: Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias
Image sizes 320 train, 320 test
Using 1 dataloader workers
Logging results to runs/train-cls/exp
Starting yolov5n-cls.pt training on Banco-Imagem-1 dataset with 5 classes for 128 epochs...
Epoch GPU_mem train_loss test_loss top1_acc top5_acc
1/128 0.508G 1.55 1.51 0.194 1: 100% 16/16 [00:06<00:00, 2.59it/s]
2/128 0.508G 1.39 1.86 0.222 1: 100% 16/16 [00:02<00:00, 6.81it/s]
3/128 0.508G 1.4 2.07 0.194 1: 100% 16/16 [00:02<00:00, 7.04it/s]
4/128 0.508G 1.35 1.75 0.222 1: 100% 16/16 [00:02<00:00, 6.38it/s]
5/128 0.508G 1.34 2.17 0.222 1: 100% 16/16 [00:02<00:00, 5.51it/s]
6/128 0.508G 1.26 1.76 0.25 1: 100% 16/16 [00:04<00:00, 3.51it/s]
7/128 0.508G 1.32 1.3 0.306 1: 100% 16/16 [00:02<00:00, 6.76it/s]
8/128 0.508G 1.27 1.57 0.333 1: 100% 16/16 [00:02<00:00, 6.99it/s]
9/128 0.508G 1.38 1.5 0.306 1: 100% 16/16 [00:02<00:00, 6.51it/s]
10/128 0.508G 1.3 1.39 0.278 1: 100% 16/16 [00:02<00:00, 5.73it/s]
11/128 0.508G 1.3 1.55 0.361 1: 100% 16/16 [00:03<00:00, 4.95it/s]
12/128 0.508G 1.28 1.45 0.306 1: 100% 16/16 [00:02<00:00, 6.98it/s]
13/128 0.508G 1.28 1.33 0.528 1: 100% 16/16 [00:02<00:00, 6.34it/s]
14/128 0.508G 1.24 1.19 0.417 1: 100% 16/16 [00:02<00:00, 6.90it/s]
15/128 0.508G 1.27 1.81 0.222 1: 100% 16/16 [00:03<00:00, 4.79it/s]
16/128 0.508G 1.25 1.52 0.361 1: 100% 16/16 [00:02<00:00, 6.45it/s]
17/128 0.508G 1.28 1.2 0.361 1: 100% 16/16 [00:02<00:00, 6.15it/s]
18/128 0.508G 1.25 1.33 0.528 1: 100% 16/16 [00:02<00:00, 6.79it/s]
19/128 0.508G 1.18 1.17 0.5 1: 100% 16/16 [00:02<00:00, 6.67it/s]
20/128 0.508G 1.23 1.33 0.306 1: 100% 16/16 [00:04<00:00, 3.52it/s]
21/128 0.508G 1.21 1.39 0.417 1: 100% 16/16 [00:02<00:00, 6.89it/s]
22/128 0.508G 1.18 1.36 0.528 1: 100% 16/16 [00:02<00:00, 6.43it/s]
23/128 0.508G 1.14 1.38 0.5 1: 100% 16/16 [00:02<00:00, 6.70it/s]
24/128 0.508G 1.17 1.3 0.556 1: 100% 16/16 [00:03<00:00, 4.59it/s]
25/128 0.508G 1.2 1.13 0.583 1: 100% 16/16 [00:02<00:00, 6.26it/s]
26/128 0.508G 1.11 1.12 0.528 1: 100% 16/16 [00:02<00:00, 6.69it/s]
27/128 0.508G 1.12 1.06 0.583 1: 100% 16/16 [00:02<00:00, 6.37it/s]
28/128 0.508G 1.12 1.45 0.417 1: 100% 16/16 [00:02<00:00, 6.95it/s]
29/128 0.508G 1.19 1.11 0.5 1: 100% 16/16 [00:03<00:00, 4.33it/s]
30/128 0.508G 1.14 1.2 0.583 1: 100% 16/16 [00:02<00:00, 6.86it/s]
31/128 0.508G 1.1 1.34 0.5 1: 100% 16/16 [00:02<00:00, 5.83it/s]
32/128 0.508G 1.17 2.32 0.278 1: 100% 16/16 [00:02<00:00, 6.40it/s]
33/128 0.508G 1.11 1.02 0.667 1: 100% 16/16 [00:02<00:00, 5.47it/s]
34/128 0.508G 1.16 1.37 0.5 1: 100% 16/16 [00:03<00:00, 5.17it/s]
35/128 0.508G 1.1 1.12 0.472 1: 100% 16/16 [00:02<00:00, 6.79it/s]
36/128 0.508G 1.08 1.2 0.556 1: 100% 16/16 [00:03<00:00, 4.22it/s]
37/128 0.508G 1.11 1.08 0.556 1: 100% 16/16 [00:02<00:00, 6.21it/s]
38/128 0.508G 1.13 1.26 0.528 1: 100% 16/16 [00:03<00:00, 4.65it/s]
39/128 0.508G 1.12 1.11 0.667 1: 100% 16/16 [00:02<00:00, 6.73it/s]
40/128 0.508G 1.11 1.19 0.639 1: 100% 16/16 [00:02<00:00, 6.53it/s]
41/128 0.508G 1.07 0.947 0.556 1: 100% 16/16 [00:02<00:00, 6.87it/s]
42/128 0.508G 1.07 1.18 0.611 1: 100% 16/16 [00:03<00:00, 5.17it/s]
43/128 0.508G 1.14 1.44 0.528 1: 100% 16/16 [00:02<00:00, 5.41it/s]
44/128 0.508G 1.05 1.01 0.667 1: 100% 16/16 [00:02<00:00, 6.64it/s]
45/128 0.508G 1.08 1.14 0.639 1: 100% 16/16 [00:02<00:00, 6.77it/s]
46/128 0.508G 1.07 1.33 0.528 1: 100% 16/16 [00:02<00:00, 6.31it/s]
47/128 0.508G 1.03 1 0.639 1: 100% 16/16 [00:03<00:00, 4.78it/s]
48/128 0.508G 1.04 1.71 0.611 1: 100% 16/16 [00:02<00:00, 5.78it/s]
49/128 0.508G 1.04 1.64 0.528 1: 100% 16/16 [00:02<00:00, 6.66it/s]
50/128 0.508G 1.02 1 0.75 1: 100% 16/16 [00:02<00:00, 6.63it/s]
51/128 0.508G 1.02 1.11 0.667 1: 100% 16/16 [00:02<00:00, 6.63it/s]
52/128 0.508G 1.06 1.59 0.611 1: 100% 16/16 [00:03<00:00, 4.26it/s]
53/128 0.508G 0.973 1.07 0.667 1: 100% 16/16 [00:02<00:00, 6.46it/s]
54/128 0.508G 0.925 1.34 0.556 1: 100% 16/16 [00:02<00:00, 6.46it/s]
55/128 0.508G 1.1 0.927 0.667 1: 100% 16/16 [00:03<00:00, 4.46it/s]
56/128 0.508G 1 1.97 0.583 1: 100% 16/16 [00:05<00:00, 3.06it/s]
57/128 0.508G 0.993 1.34 0.611 1: 100% 16/16 [00:02<00:00, 6.75it/s]
58/128 0.508G 0.954 1.17 0.639 1: 100% 16/16 [00:02<00:00, 6.50it/s]
59/128 0.508G 1.03 1.54 0.5 1: 100% 16/16 [00:02<00:00, 6.59it/s]
60/128 0.508G 1.01 1.12 0.611 1: 100% 16/16 [00:03<00:00, 5.32it/s]
61/128 0.508G 1 1.13 0.583 1: 100% 16/16 [00:03<00:00, 5.28it/s]
62/128 0.508G 0.943 0.986 0.639 1: 100% 16/16 [00:02<00:00, 6.75it/s]
63/128 0.508G 0.909 1.12 0.639 1: 100% 16/16 [00:02<00:00, 6.97it/s]
64/128 0.508G 0.888 0.867 0.75 1: 100% 16/16 [00:02<00:00, 6.32it/s]
65/128 0.508G 0.958 0.975 0.667 1: 100% 16/16 [00:03<00:00, 4.41it/s]
66/128 0.508G 0.939 0.947 0.639 1: 100% 16/16 [00:02<00:00, 6.54it/s]
67/128 0.508G 1.02 1.11 0.694 1: 100% 16/16 [00:03<00:00, 5.04it/s]
68/128 0.508G 0.998 0.971 0.667 1: 100% 16/16 [00:02<00:00, 5.55it/s]
69/128 0.508G 0.968 0.98 0.694 1: 100% 16/16 [00:03<00:00, 4.52it/s]
70/128 0.508G 0.965 1.11 0.722 1: 100% 16/16 [00:02<00:00, 6.55it/s]
71/128 0.508G 0.965 1.47 0.583 1: 100% 16/16 [00:02<00:00, 6.84it/s]
72/128 0.508G 0.953 1.2 0.611 1: 100% 16/16 [00:02<00:00, 6.54it/s]
73/128 0.508G 0.863 0.772 0.722 1: 100% 16/16 [00:02<00:00, 6.90it/s]
74/128 0.508G 0.946 0.884 0.667 1: 100% 16/16 [00:03<00:00, 4.25it/s]
75/128 0.508G 0.911 0.942 0.694 1: 100% 16/16 [00:02<00:00, 6.78it/s]
76/128 0.508G 0.964 1.16 0.694 1: 100% 16/16 [00:02<00:00, 6.80it/s]
77/128 0.508G 0.917 1.2 0.694 1: 100% 16/16 [00:02<00:00, 6.44it/s]
78/128 0.508G 0.941 0.955 0.639 1: 100% 16/16 [00:02<00:00, 6.22it/s]
79/128 0.508G 0.885 1.02 0.722 1: 100% 16/16 [00:03<00:00, 4.58it/s]
80/128 0.508G 0.864 0.802 0.694 1: 100% 16/16 [00:02<00:00, 6.33it/s]
81/128 0.508G 0.908 1.11 0.833 1: 100% 16/16 [00:02<00:00, 6.52it/s]
82/128 0.508G 0.915 0.843 0.778 1: 100% 16/16 [00:02<00:00, 6.82it/s]
83/128 0.508G 0.899 1.14 0.722 1: 100% 16/16 [00:03<00:00, 4.96it/s]
84/128 0.508G 0.826 0.81 0.75 1: 100% 16/16 [00:02<00:00, 5.77it/s]
85/128 0.508G 0.831 0.883 0.694 1: 100% 16/16 [00:02<00:00, 6.61it/s]
86/128 0.508G 0.804 0.95 0.694 1: 100% 16/16 [00:02<00:00, 6.42it/s]
87/128 0.508G 0.805 0.916 0.694 1: 100% 16/16 [00:02<00:00, 6.60it/s]
88/128 0.508G 0.824 0.936 0.667 1: 100% 16/16 [00:03<00:00, 4.40it/s]
89/128 0.508G 0.854 0.854 0.639 1: 100% 16/16 [00:02<00:00, 6.48it/s]
90/128 0.508G 0.79 1.14 0.694 1: 100% 16/16 [00:02<00:00, 6.72it/s]
91/128 0.508G 0.83 0.848 0.75 1: 100% 16/16 [00:02<00:00, 6.59it/s]
92/128 0.508G 0.805 1.32 0.639 1: 100% 16/16 [00:02<00:00, 6.47it/s]
93/128 0.508G 0.813 1.22 0.75 1: 100% 16/16 [00:03<00:00, 4.23it/s]
94/128 0.508G 0.796 0.91 0.722 1: 100% 16/16 [00:02<00:00, 6.68it/s]
95/128 0.508G 0.823 0.778 0.75 1: 100% 16/16 [00:02<00:00, 6.70it/s]
96/128 0.508G 0.827 0.898 0.806 1: 100% 16/16 [00:02<00:00, 6.50it/s]
97/128 0.508G 0.777 0.833 0.778 1: 100% 16/16 [00:02<00:00, 5.78it/s]
98/128 0.508G 0.79 0.735 0.806 1: 100% 16/16 [00:03<00:00, 4.78it/s]
99/128 0.508G 0.824 0.797 0.778 1: 100% 16/16 [00:02<00:00, 6.19it/s]
100/128 0.508G 0.802 0.893 0.806 1: 100% 16/16 [00:02<00:00, 5.94it/s]
101/128 0.508G 0.778 1.11 0.778 1: 100% 16/16 [00:02<00:00, 6.61it/s]
102/128 0.508G 0.795 1.15 0.722 1: 100% 16/16 [00:03<00:00, 4.30it/s]
103/128 0.508G 0.777 1.54 0.667 1: 100% 16/16 [00:02<00:00, 6.39it/s]
104/128 0.508G 0.764 0.916 0.722 1: 100% 16/16 [00:02<00:00, 6.66it/s]
105/128 0.508G 0.737 1.04 0.778 1: 100% 16/16 [00:02<00:00, 6.57it/s]
106/128 0.508G 0.689 0.792 0.75 1: 100% 16/16 [00:02<00:00, 6.55it/s]
107/128 0.508G 0.769 0.945 0.75 1: 100% 16/16 [00:03<00:00, 4.40it/s]
108/128 0.508G 0.78 1.21 0.75 1: 100% 16/16 [00:02<00:00, 6.61it/s]
109/128 0.508G 0.768 0.958 0.75 1: 100% 16/16 [00:02<00:00, 6.37it/s]
110/128 0.508G 0.802 0.953 0.75 1: 100% 16/16 [00:02<00:00, 6.41it/s]
111/128 0.508G 0.765 0.71 0.75 1: 100% 16/16 [00:02<00:00, 5.42it/s]
112/128 0.508G 0.709 1.07 0.722 1: 100% 16/16 [00:03<00:00, 5.15it/s]
113/128 0.508G 0.683 1.1 0.694 1: 100% 16/16 [00:02<00:00, 6.57it/s]
114/128 0.508G 0.685 0.892 0.778 1: 100% 16/16 [00:02<00:00, 6.41it/s]
115/128 0.508G 0.678 0.78 0.722 1: 100% 16/16 [00:02<00:00, 6.25it/s]
116/128 0.508G 0.714 1.19 0.722 1: 100% 16/16 [00:03<00:00, 4.29it/s]
117/128 0.508G 0.718 0.777 0.694 1: 100% 16/16 [00:02<00:00, 6.04it/s]
118/128 0.508G 0.744 0.855 0.778 1: 100% 16/16 [00:02<00:00, 6.72it/s]
119/128 0.508G 0.732 0.708 0.75 1: 100% 16/16 [00:02<00:00, 6.66it/s]
120/128 0.508G 0.7 0.88 0.778 1: 100% 16/16 [00:02<00:00, 5.85it/s]
121/128 0.508G 0.687 0.852 0.778 1: 100% 16/16 [00:03<00:00, 4.65it/s]
122/128 0.508G 0.671 1.01 0.778 1: 100% 16/16 [00:02<00:00, 6.46it/s]
123/128 0.508G 0.695 0.708 0.75 1: 100% 16/16 [00:02<00:00, 6.40it/s]
124/128 0.508G 0.685 0.725 0.778 1: 100% 16/16 [00:02<00:00, 6.69it/s]
125/128 0.508G 0.681 0.991 0.75 1: 100% 16/16 [00:03<00:00, 4.79it/s]
126/128 0.508G 0.674 0.72 0.75 1: 100% 16/16 [00:03<00:00, 4.96it/s]
127/128 0.508G 0.674 0.733 0.75 1: 100% 16/16 [00:02<00:00, 6.52it/s]
128/128 0.508G 0.687 0.682 0.75 1: 100% 16/16 [00:02<00:00, 6.48it/s]
Training complete (0.105 hours)
Results saved to runs/train-cls/exp
Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg
Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data Banco-Imagem-1
Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt')
Visualize: https://netron.app
CPU times: user 4.67 s, sys: 452 ms, total: 5.12 s
Wall time: 6min 43s
!python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data $DATASET_NAME
classify/val: data=Banco-Imagem-1, weights=['runs/train-cls/exp/weights/best.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=False, dnn=False
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers...
Model summary: 117 layers, 1214869 parameters, 0 gradients, 2.9 GFLOPs
testing: 100% 1/1 [00:00<00:00, 1.05it/s]
Class Images top1_acc top5_acc
all 36 0.639 1
avioes 7 0.571 1
barcos 6 0.667 1
carros 11 0.545 1
helicopteros 8 0.875 1
motos 4 0.5 1
Speed: 0.1ms pre-process, 14.8ms inference, 0.6ms post-process per image at shape (1, 3, 224, 224)
Results saved to runs/val-cls/exp
```
</details>
### Evidências do treinamento
#### Gráficos de precisão e perdas
#### Matriz de Confusão
#### Inferindo com o modelo personalizado
```
#Pega a localização de uma imagem do conjunto de testes ou validações
if os.path.exists(os.path.join(dataset.location, "test")):
split_path = os.path.join(dataset.location, "test")
else:
os.path.join(dataset.location, "valid")
example_class = os.listdir(split_path)[4]
example_image_name = os.listdir(os.path.join(split_path, example_class))[4]
example_image_path = os.path.join(split_path, example_class, example_image_name)
os.environ["TEST_IMAGE_PATH"] = example_image_path
print(f"Inferindo sobre um exemplo da classe '{example_class}'")
#Infer
!python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source $TEST_IMAGE_PATH
Inferindo sobre um exemplo da classe 'carros'
classify/predict: weights=['runs/train-cls/exp/weights/best.pt'], source=/content/yolov5/Banco-Imagem-1/test/carros/00012_jpg.rf.9f0d32646e83139878c5788b040038f7.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers...
Model summary: 117 layers, 1214869 parameters, 0 gradients, 2.9 GFLOPs
image 1/1 /content/yolov5/Banco-Imagem-1/test/carros/00012_jpg.rf.9f0d32646e83139878c5788b040038f7.jpg: 224x224 carros 0.91, avioes 0.08, motos 0.01, helicopteros 0.00, barcos 0.00, 2.7ms
Speed: 0.3ms pre-process, 2.7ms inference, 5.1ms NMS per image at shape (1, 3, 224, 224)
Results saved to runs/predict-cls/exp14
```
```
#### Modelo treinado com 80% ou mais de acurácia/precisão
=========================================================
```
```
#carro
import requests
image_url = "https://i.imgur.com/GB9Tihf.jpg"
response = requests.get(image_url)
response.raise_for_status()
with open('carro.jpg', 'wb') as handler:
handler.write(response.content)
!python classify/predict.py --weights ./weights/yolov5x-cls.pt --source carro.jpg
classify/predict: weights=['./weigths/yolov5x-cls.pt'], source=carro.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers...
Model summary: 264 layers, 48072600 parameters, 0 gradients, 129.9 GFLOPs
image 1/1 /content/yolov5/carro.jpg: 224x224 sports car 0.95, race car 0.02, convertible 0.01, car wheel 0.00, grille 0.00, 12.9ms
Speed: 0.4ms pre-process, 12.9ms inference, 6.9ms NMS per image at shape (1, 3, 224, 224)
Results saved to runs/predict-cls/exp13
### Modelo treinado com ao menos 50% de acurácia/precisão
=========================================================
```
```
#Moto
import requests
image_url = "https://i.imgur.com/ASwjAT5.jpg"
response = requests.get(image_url)
response.raise_for_status()
with open('moto.jpg', 'wb') as handler:
handler.write(response.content)
!python classify/predict.py --weights ./weights/yolov5m-cls.pt --source moto.jpg
classify/predict: weights=['./weigths/yolov5m-cls.pt'], source=moto.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers...
Model summary: 166 layers, 12947192 parameters, 0 gradients, 31.7 GFLOPs
image 1/1 /content/yolov5/moto.jpg: 224x224 moped 0.64, scooter 0.17, disc brake 0.06, crash helmet 0.05, snowmobile 0.01, 5.4ms
Speed: 0.4ms pre-process, 5.4ms inference, 6.9ms NMS per image at shape (1, 3, 224, 224)
Results saved to runs/predict-cls/exp16
```
## Roboflow
Banco-Imagem > 2023-10-24 9:29pm
https://universe.roboflow.com/eniokilder/banco-imagem
Provided by a Roboflow user
License: CC BY 4.0
## HuggingFace
Link para o HuggingFace:
https://huggingface.co/datasets/eniokilder/Banco-Imagem
| [
-0.4593811631202698,
-0.3919568359851837,
0.34515008330345154,
0.03634129464626312,
-0.24451057612895966,
-0.0009318120428360999,
-0.20693594217300415,
-0.3097967207431793,
0.7372346520423889,
-0.1685200333595276,
-0.4843345582485199,
-0.6351588368415833,
-0.6292174458503723,
0.10971602797... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jfloresf/demo | jfloresf | 2023-11-12T23:38:12Z | 0 | 0 | null | [
"language:en",
"clouds",
"sentinel-2",
"image-segmentation",
"deep-learning",
"remote-sensing",
"region:us"
] | 2023-11-12T23:38:12Z | 2023-10-28T13:35:52.000Z | 2023-10-28T13:35:52 | ---
language:
- en
tags:
- clouds
- sentinel-2
- image-segmentation
- deep-learning
- remote-sensing
pretty_name: cloudsen12
---
# cloudsen12
***``A dataset about clouds from Sentinel-2``***
CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms.
CloudSEN12 is designed to support both weakly and self-/semi-supervised learning strategies by including three distinct forms of hand-crafted labeling data: high-quality, scribble and no-annotation. For more details on how we created the dataset see our paper: CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.
**ML-STAC Snippet**
```python
import mlstac
secret = 'https://huggingface.co/datasets/jfloresf/mlstac-demo/resolve/main/main.json'
train_db = mlstac.load(secret, framework='torch', stream=True, device='cpu')
```
**Sensor: Sentinel 2 - MSI**
**ML-STAC Task: TensorToTensor, TensorSegmentation**
**Data raw repository: [http://www.example.com/](http://www.example.com/)**
**Dataset discussion: [https://github.com/IPL-UV/ML-STAC/discussions/2](https://github.com/IPL-UV/ML-STAC/discussions/2)**
**Review mean score: 5.0**
**Split_strategy: random**
**Paper: [https://www.nature.com/articles/s41597-022-01878-2](https://www.nature.com/articles/s41597-022-01878-2)**
## Data Providers
|Name|Role|URL|
| :---: | :---: | :---: |
|Image & Signal Processing|['host']|https://isp.uv.es/|
|ESA|['producer']|https://www.esa.int/|
## Curators
|Name|Organization|URL|
| :---: | :---: | :---: |
|Cesar Aybar|Image & Signal Processing|http://csaybar.github.io/|
## Reviewers
|Name|Organization|URL|Score|
| :---: | :---: | :---: | :---: |
|Cesar Aybar|Image & Signal Processing|http://csaybar.github.io/|5|
## Labels
|Name|Value|
| :---: | :---: |
|clear|0|
|thick-cloud|1|
|thin-cloud|2|
|cloud-shadow|3|
## Dimensions
### input
|Axis|Name|Description|
| :---: | :---: | :---: |
|0|C|Channels - Spectral bands|
|1|H|Height|
|2|W|Width|
### target
|Axis|Name|Description|
| :---: | :---: | :---: |
|0|C|Hand-crafted labels|
|1|H|Height|
|2|W|Width|
## Spectral Bands
|Name|Common Name|Description|Center Wavelength|Full Width Half Max|Index|
| :---: | :---: | :---: | :---: | :---: | :---: |
|B01|coastal aerosol|Band 1 - Coastal aerosol - 60m|443.5|17.0|0|
|B02|blue|Band 2 - Blue - 10m|496.5|53.0|1|
|B03|green|Band 3 - Green - 10m|560.0|34.0|2|
|B04|red|Band 4 - Red - 10m|664.5|29.0|3|
|B05|red edge 1|Band 5 - Vegetation red edge 1 - 20m|704.5|13.0|4|
|B06|red edge 2|Band 6 - Vegetation red edge 2 - 20m|740.5|13.0|5|
|B07|red edge 3|Band 7 - Vegetation red edge 3 - 20m|783.0|18.0|6|
|B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|7|
|B8A|red edge 4|Band 8A - Vegetation red edge 4 - 20m|864.5|19.0|8|
|B09|water vapor|Band 9 - Water vapor - 60m|945.0|18.0|9|
|B10|cirrus|Band 10 - Cirrus - 60m|1375.5|31.0|10|
|B11|SWIR 1|Band 11 - Shortwave infrared 1 - 20m|1613.5|89.0|11|
|B12|SWIR 2|Band 12 - Shortwave infrared 2 - 20m|2199.5|173.0|12|
| [
-0.9984703660011292,
-0.2543541193008423,
0.5137819051742554,
0.09152677655220032,
-0.24046069383621216,
-0.17216815054416656,
0.002941639395430684,
-0.5283443927764893,
0.5636826157569885,
0.3077278733253479,
-0.8889753818511963,
-0.9166725873947144,
-0.5985231399536133,
-0.15662029385566... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
316usman/my_dataset | 316usman | 2023-10-28T13:59:53Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T13:59:53Z | 2023-10-28T13:59:51.000Z | 2023-10-28T13:59:51 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 31
num_examples: 1
download_size: 1349
dataset_size: 31
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "my_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7651488780975342,
-0.254448801279068,
0.19326873123645782,
0.20587411522865295,
-0.011474200524389744,
0.01958027482032776,
0.2946239709854126,
-0.09512994438409805,
1.0738111734390259,
0.5611461997032166,
-0.9016962647438049,
-0.6428890824317932,
-0.5292006731033325,
0.0116622773930430... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Hi-ToM/Hi-ToM_Dataset | Hi-ToM | 2023-10-29T04:32:30Z | 0 | 0 | null | [
"region:us"
] | 2023-10-29T04:32:30Z | 2023-10-28T15:48:30.000Z | 2023-10-28T15:48:30 | # Hi-ToM Dataset
This is the dataset for the paper "Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models".
<img src=media/Picture1.png height=430>
### The `Hi-ToM_data` folder
Contains ToMh data consisting of story-question pairs and the corresponding answers.
The names of subfolder branches have the following meanings:
- `Tell` / `No_Tell`: whether or not the stories contain communications among agents.
- `MC` / `CoT`: the prompting style. `MC` corresponds to Vanilla Prompting (VP) in the paper, while `CoT` stands for Chain-of-Thought Prompting (CoTP).
- `length_n`: the story length, i.e. the number of chapters in a story. From 1 to 3.
- `sample_n`: the numbering of different sample stories.
- `order_n`: the ToM order of the question. From 0 to 4.
### The `Hi-ToM_prompt` folder
Contains prompt files that can be directly input to API.
The data in it are almost the same as `Hi-ToM_data`, except that answers are eliminated.
### Generate new data and prompts
Run the script `generate_tomh.sh`.
| [
-0.8540229797363281,
-0.9433227181434631,
0.6212500333786011,
-0.08953399956226349,
-0.2682628631591797,
-0.09831574559211731,
-0.320081889629364,
-0.34558093547821045,
0.30042096972465515,
0.7909821271896362,
-0.9346863627433777,
-0.5220252871513367,
-0.39683568477630615,
0.20706640183925... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
akkasi/go_emotions | akkasi | 2023-10-28T16:02:47Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T16:02:47Z | 2023-10-28T16:02:44.000Z | 2023-10-28T16:02:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: float64
- name: label2idx
dtype: string
- name: idx2label
dtype: string
splits:
- name: train
num_bytes: 210169067
num_examples: 168980
- name: test
num_bytes: 52552436
num_examples: 42245
download_size: 13348134
dataset_size: 262721503
---
# Dataset Card for "go_emotions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Starkate/original | Starkate | 2023-10-28T17:15:38Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T17:15:38Z | 2023-10-28T16:54:47.000Z | 2023-10-28T16:54:47 | Entry not found | [
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AurumnPegasus/AurumnPegasus | AurumnPegasus | 2023-10-28T17:43:57Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T17:43:57Z | 2023-10-28T17:29:23.000Z | 2023-10-28T17:29:23 | ---
dataset_info:
features:
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download_size: 26192269
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configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "AurumnPegasus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Tsuinzues/siciliavidal | Tsuinzues | 2023-10-28T18:04:47Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-10-28T18:04:47Z | 2023-10-28T18:04:26.000Z | 2023-10-28T18:04:26 | ---
license: openrail
---
| [
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akkasi/dutch_social | akkasi | 2023-10-28T18:21:48Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T18:21:48Z | 2023-10-28T18:21:45.000Z | 2023-10-28T18:21:45 | ---
configs:
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data_files:
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path: data/train-*
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path: data/test-*
dataset_info:
features:
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---
# Dataset Card for "dutch_social"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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akkasi/EnglishNLPDataset | akkasi | 2023-10-28T18:27:28Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T18:27:28Z | 2023-10-28T18:27:25.000Z | 2023-10-28T18:27:25 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
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path: data/validation-*
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path: data/test-*
dataset_info:
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download_size: 5458653
dataset_size: 21310550
---
# Dataset Card for "EnglishNLPDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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imessam/Python_code_assistant_with_prompt | imessam | 2023-10-28T20:07:49Z | 0 | 2 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-28T20:07:49Z | 2023-10-28T18:51:38.000Z | 2023-10-28T18:51:38 | ---
license: apache-2.0
configs:
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data_files:
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path: data/train-*
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---
Formatted with a prompt template.
Modified from this dataset https://huggingface.co/datasets/Nan-Do/reason_code-search-net-python | [
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quocanh34/new_nlu_tts3_with_correction | quocanh34 | 2023-10-28T20:26:11Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T20:26:11Z | 2023-10-28T20:24:35.000Z | 2023-10-28T20:24:35 | ---
dataset_info:
features:
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dataset_size: 568308476
---
# Dataset Card for "new_nlu_tts3_with_correction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Bsbell21/MFA_tweet_topics | Bsbell21 | 2023-10-28T20:52:50Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T20:52:50Z | 2023-10-28T20:52:48.000Z | 2023-10-28T20:52:48 | ---
dataset_info:
features:
- name: tweet
dtype: string
- name: topics
dtype: string
splits:
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num_bytes: 21732
num_examples: 121
download_size: 18513
dataset_size: 21732
configs:
- config_name: default
data_files:
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path: data/train-*
---
# Dataset Card for "MFA_tweet_topics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kinianlo/wiki_20220301_en_nltk_uncased_phrases_clean | kinianlo | 2023-10-28T22:42:23Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T22:42:23Z | 2023-10-28T22:42:16.000Z | 2023-10-28T22:42:16 | ---
dataset_info:
features:
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dtype: uint32
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download_size: 41983842
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configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "wiki_20220301_en_nltk_uncased_phrases_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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satwant/ExpertMedQA | satwant | 2023-10-28T22:44:57Z | 0 | 1 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | 2023-10-28T22:44:57Z | 2023-10-28T22:43:33.000Z | 2023-10-28T22:43:33 | ---
license: cc-by-nc-4.0
---
This dataset provides the complete ExpertMedQA dataset along with responses generated by BooksMed, highlighting the dataset's diversity and complexity, and providing a comprehensive overview of dataset questions. ExpertMedQA is a novel benchmark characterized by open-ended, expert-level clinical questions, which bridge this gap by requiring not only an understanding of the most recent clinical literature but also an analysis of the strength of the evidence presented. From current treatment guidelines to open-ended discussions requiring knowledge and analysis based on current clinical research studies, this dataset covers a wide range of topics. | [
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creativelybrainstorm/maqsa | creativelybrainstorm | 2023-10-28T22:50:59Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T22:50:59Z | 2023-10-28T22:43:52.000Z | 2023-10-28T22:43:52 | Entry not found | [
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quocanh34/old_nlu_new_asr_v1 | quocanh34 | 2023-10-28T23:39:11Z | 0 | 0 | null | [
"region:us"
] | 2023-10-28T23:39:11Z | 2023-10-28T23:38:40.000Z | 2023-10-28T23:38:40 | ---
dataset_info:
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splits:
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num_bytes: 568314186
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download_size: 462244745
dataset_size: 568314186
---
# Dataset Card for "old_nlu_new_asr_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Kabatubare/medical-alpaca | Kabatubare | 2023-10-29T00:17:30Z | 0 | 1 | null | [
"region:us"
] | 2023-10-29T00:17:30Z | 2023-10-28T23:58:55.000Z | 2023-10-28T23:58:55 | Entry not found | [
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furry-br/AI-reference | furry-br | 2023-10-29T01:44:09Z | 0 | 0 | null | [
"region:us"
] | 2023-10-29T01:44:09Z | 2023-10-29T01:43:13.000Z | 2023-10-29T01:43:13 | Entry not found | [
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Fiacre/PV-system-expert-500 | Fiacre | 2023-10-29T02:39:59Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-10-29T02:39:59Z | 2023-10-29T02:38:58.000Z | 2023-10-29T02:38:58 | ---
license: openrail
---
| [
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venkat-srinivasan-nexusflow/cve_train_prompt_change_only | venkat-srinivasan-nexusflow | 2023-10-29T04:22:49Z | 0 | 0 | null | [
"region:us"
] | 2023-10-29T04:22:49Z | 2023-10-29T02:43:34.000Z | 2023-10-29T02:43:34 | ---
dataset_info:
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splits:
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num_bytes: 396691
num_examples: 302
download_size: 119758
dataset_size: 396691
---
# Dataset Card for "cve_train_main"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yubo0306/fed_ja | yubo0306 | 2023-10-29T04:26:57Z | 0 | 0 | null | [
"task_categories:conversational",
"language:ja",
"license:unknown",
"region:us"
] | 2023-10-29T04:26:57Z | 2023-10-29T03:55:00.000Z | 2023-10-29T03:55:00 | ---
configs:
- config_name: default
data_files:
- split: train
path: fed_data.json
language:
- ja
pretty_name: fed_ja
task_categories:
- conversational
license: unknown
---
[FEDデータセット](http://shikib.com/fed_data.json)をGoogle Cloud Translate API v2で日本語化したデータセットです．
機械翻訳のため，一部dimensionはアノテーションとの整合性が適切ではない可能性があります．
使用するdimensionには注意してください．
| [
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PsiPi/PascalQnA100 | PsiPi | 2023-10-29T05:52:25Z | 0 | 0 | null | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"code",
"region:us"
] | 2023-10-29T05:52:25Z | 2023-10-29T04:04:23.000Z | 2023-10-29T04:04:23 | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: pascal100
size_categories:
- n<1K
---
100 Pascal Q and A
60% with an input string of some kind | [
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Naveengo/sql-create-context-5000rows | Naveengo | 2023-10-29T05:18:24Z | 0 | 0 | null | [
"region:us"
] | 2023-10-29T05:18:24Z | 2023-10-29T05:18:20.000Z | 2023-10-29T05:18:20 | ---
dataset_info:
features:
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dtype: string
- name: answer
dtype: string
- name: context
dtype: string
splits:
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num_bytes: 1104644.8706364457
num_examples: 5000
download_size: 548687
dataset_size: 1104644.8706364457
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sql-create-context-5000rows"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mujif/VisualReferPrompt | mujif | 2023-11-07T14:00:37Z | 0 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:visual-question-answering",
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"region:us"
] | 2023-11-07T14:00:37Z | 2023-10-29T06:04:17.000Z | 2023-10-29T06:04:17 | ---
license: cc-by-sa-4.0
configs:
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data_files:
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path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: qid
dtype: int64
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task_categories:
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language_creators:
- expert-generated
- found
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
vrpbench is a benchmark dataset designed for visual referring prompting.
The dataset includes original images and their variants annotated with specific referring prompts.
The original images are sourced from
(1). [Mathvista](https://huggingface.co/datasets/AI4Math/MathVista)
(2). We manually craft some examples.
The variants are manually labeled and recorded by the creators.
Each image is accompanied by a question that has been created and verified by humans.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** Zonkey LEE
- **Funded by [optional]:** HKUST CSE
- **Shared by [optional]:** SKYWF
- **Language(s) (NLP):** EN
- **License:** cc-by-4.0
<!-- ### Dataset Sources [optional] -->
<!-- Provide the basic links for the dataset. -->
<!-- - **Repository:** [More Information Needed] -->
<!-- - **Paper [optional]:** [More Information Needed] -->
<!-- - **Demo [optional]:** [More Information Needed] -->
## License
The new contributions to our dataset are distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license, including
- The creation of our dataset;
- The filtering and cleaning of source datasets;
- The standard formalization of instances for evaluation purposes;
- The annotations of metadata.
The copyright of the images and the questions belongs to the original authors,
The copyright of newly introduced images, and all the questions belong to Zonkey LEE.
Alongside this license, the following conditions apply:
- **Purpose:** The dataset was primarily designed for use as a test set.
- **Commercial Use:** The dataset can be used commercially as a test set, but using it as a training set is prohibited. By accessing or using this dataset, you acknowledge and agree to abide by these terms in conjunction with the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license.
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Data Downloading
All the data examples were in *test* dataset.
- **test**: 2,145 examples for standard evaluation. Notably, the answer labels for test will NOT be publicly released.
You can download this dataset by the following command (make sure that you have installed [Huggingface Datasets](https://huggingface.co/docs/datasets/quickstart)):
```python
from datasets import load_dataset
dataset = load_dataset("mujif/VisualReferPrompt")
```
Here are some examples of how to access the downloaded dataset:
```python
# print the first example on the test set
print(dataset["test"][0])
print(dataset["test"][0]['qid']) # print the problem id
print(dataset["test"][0]['category']) # print the question category
print(dataset["test"][0]['ori_img']) # print the image path
print(dataset["test"][0]['question']) # print the query text
print(dataset["test"][0]['gt_answer']) # print the answer
print(dataset["test"][0]['img_size']) # print the img size
print(dataset["test"][0]['vis_ref_type']) # print the answer
print(dataset["test"][0]['details']) # print the answer
dataset["test"][0]['image'] # display the image
# print the first example on the test set
print(dataset["test"][0])
```
## Dataset Creation
### Data Source
The **VisualReferPrompt** dataset is derived from newly collected dataset MathVista, which contains three datasets: IQTest, FunctionQA, and Paper, as well as 28 other source datasets. All these source datasets have been preprocessed and labeled for evaluation purposes.
### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
Notably, to aviod personal information and follow the rules of current LMMs, we **We do not include any portrait images**.
### Automatic Evaluation
🔔 To automatically evaluate a model on the dataset, please refer to our GitHub repository [here]().
## Citation
If you use the **VisualReferPrompt** dataset in your work, please kindly cite the paper using this BibTeX:
Our paper will soon be published, please wait.
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Falah/race_random_prompts | Falah | 2023-10-29T07:04:25Z | 0 | 0 | null | [
"region:us"
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---
# Dataset Card for "race_random_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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SoAp9035/Turkish_TinyStories_Large | SoAp9035 | 2023-10-29T07:46:26Z | 0 | 1 | null | [
"language:tr",
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"region:us"
] | 2023-10-29T07:46:26Z | 2023-10-29T07:45:44.000Z | 2023-10-29T07:45:44 | ---
license: cdla-sharing-1.0
language:
- tr
---
# Turkish TinyStories Large
### License: CDLA-Sharing-1.0
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aazer/WeatherGov-dataset | aazer | 2023-10-29T09:23:39Z | 0 | 0 | null | [
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license: mit
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language:
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tags:
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size_categories:
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carles-undergrad-thesis/msmarco-corpus-en-id-parallel-sentences | carles-undergrad-thesis | 2023-10-29T08:29:35Z | 0 | 0 | null | [
"region:us"
] | 2023-10-29T08:29:35Z | 2023-10-29T08:27:41.000Z | 2023-10-29T08:27:41 | ---
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configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "msmarco-corpus-en-id-parallel-sentences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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carles-undergrad-thesis/msmarco-query-en-id-parallel-sentences | carles-undergrad-thesis | 2023-10-29T08:32:19Z | 0 | 0 | null | [
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] | 2023-10-29T08:32:19Z | 2023-10-29T08:32:16.000Z | 2023-10-29T08:32:16 | ---
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path: data/train-*
---
# Dataset Card for "msmarco-query-en-id-parallel-sentences"
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---
# Dataset Card for "covid-tweet-sentiment-analyzer-roberta-latest-data"
1. **input_ids:**
- `input_ids` represent the input to a natural language processing (NLP) model in the form of tokenized and numerical values.
- These are the tokenized versions of the text data, where words and tokens are converted to unique numerical identifiers.
- These numerical values enable the model to understand and process the text data, making it suitable for machine learning algorithms.
2. **attention_mask:**
- `attention_mask` is a companion to `input_ids` and is used to indicate which parts of the input sequence should be attended to by the model and which parts should be ignored.
- The attention mask is important for maintaining the structure and integrity of the input data while accommodating variations in text length.
3. **labels:**
- `labels` refer to the target values that the model is trying to predict.
- These are '1' for neutral, '2' for positive, and '0' for negative sentiment. | [
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autoevaluate/autoeval-eval-banking77-default-b28a77-98055146974 | autoevaluate | 2023-10-29T12:06:34Z | 0 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2023-10-29T12:06:34Z | 2023-10-29T12:05:50.000Z | 2023-10-29T12:05:50 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- banking77
eval_info:
task: multi_class_classification
model: Kirie/test-bert-base-banking77
metrics: []
dataset_name: banking77
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: Kirie/test-bert-base-banking77
* Dataset: banking77
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@i got my credit card](https://huggingface.co/i got my credit card) for evaluating this model. | [
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---
# Dataset Card for "Synatra_IHA"
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DaviGamer/KennyMaccormic | DaviGamer | 2023-10-29T16:05:47Z | 0 | 0 | null | [
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license: openrail
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-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
digitalwas-solutions/midjourney-prompts | digitalwas-solutions | 2023-10-29T16:49:52Z | 0 | 1 | null | [
"region:us"
] | 2023-10-29T16:49:52Z | 2023-10-29T16:49:51.000Z | 2023-10-29T16:49:51 | ---
dataset_info:
features:
- name: Prompt
dtype: string
- name: autotrain_text
dtype: string
splits:
- name: train
num_bytes: 77100
num_examples: 288
- name: validation
num_bytes: 77100
num_examples: 288
download_size: 47998
dataset_size: 154200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "autotrain-data-l840-cwyf-0kjj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5481590032577515,
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-0.083432637155056,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
islamrokon/Test | islamrokon | 2023-11-11T15:37:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-11T15:37:52Z | 2023-10-29T16:51:58.000Z | 2023-10-29T16:51:58 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 17012.625
num_examples: 14
- name: test
num_bytes: 2430.375
num_examples: 2
download_size: 17101
dataset_size: 19443.0
---
# Dataset Card for "Test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.6750997304916382,
-0.4192483425140381,
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-0.47400179505348206,
-0.1874891966581344... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pjayl/faq_embeddings | pjayl | 2023-10-29T17:30:24Z | 0 | 0 | null | [
"region:us"
] | 2023-10-29T17:30:24Z | 2023-10-29T17:28:36.000Z | 2023-10-29T17:28:36 | Entry not found | [
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-0.22568407654762268,
0.8622258901596069,
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-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yongchanskii/only-text-data-various-domain | yongchanskii | 2023-10-29T17:36:06Z | 0 | 0 | null | [
"region:us"
] | 2023-10-29T17:36:06Z | 2023-10-29T17:35:51.000Z | 2023-10-29T17:35:51 | ---
dataset_info:
features:
- name: docId
dtype: string
- name: category
dtype: string
- name: domainTag
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 26467274.758485764
num_examples: 84235
- name: test
num_bytes: 6616897.241514237
num_examples: 21059
download_size: 20057835
dataset_size: 33084172.0
---
# Dataset Card for "only-text-data-various-domain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.528340756893158,
-0.669133722782135,
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-0.012173439376056... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
flyingfishinwater/wikipedia_20231001 | flyingfishinwater | 2023-11-01T21:54:16Z | 0 | 0 | null | [
"task_categories:text-generation",
"size_categories:10B<n<100B",
"language:en",
"license:apache-2.0",
"chemistry",
"biology",
"legal",
"music",
"art",
"medical",
"region:us"
] | 2023-11-01T21:54:16Z | 2023-10-29T18:43:44.000Z | 2023-10-29T18:43:44 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- chemistry
- biology
- legal
- music
- art
- medical
size_categories:
- 10B<n<100B
---
It's the English content dumped from 2023-10-01 version of Wikipedia dump site.
The format is similar with "[datasets/wikipedia](https://huggingface.co/datasets/wikipedia?row=0)". It has use same method to clean the text.
However, I ommitted the 'url' field because it follows the same format: "https://en.wikipedia.org/wiki/[title]".
Another change is the title. I merged the "REDIRECTED" title with its original and use comma as seperator.
For example, the title "An American in Paris, AnAmericanInParis" means "An American in Paris" and "AnAmericanInParis" points to the same content.
| [
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0.645456075668335,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MichaelVeser/finetuningopensecurity-llama | MichaelVeser | 2023-10-29T19:33:18Z | 0 | 0 | null | [
"region:us"
] | 2023-10-29T19:33:18Z | 2023-10-29T19:33:16.000Z | 2023-10-29T19:33:16 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4000
num_examples: 1000
download_size: 714
dataset_size: 4000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "finetuningopensecurity-llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.2913663387298584,
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-0.6986488699913025,
-0.1359430998563766... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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