Datasets
All datasets matching “Twitter”twitter-financial-news-sentiment
Dataset Description
The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.
The dataset holds 11,932 documents annotated with 3 labels:
sentiments = {
"LABEL_0": "Bearish",
"LABEL_1": "Bullish",
"LABEL_2": "Neutral"
}
The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.TwitterHjerneRetrieval
TwitterHjerneRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Danish question asked on Twitter with the Hashtag #Twitterhjerne ('Twitter brain') and their corresponding answer.
Task category
t2t
Domains
Social, Written
Reference
https://huggingface.co/datasets/sorenmulli/da-hashtag-twitterhjerne
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/TwitterHjerneRetrieval.twittersemeval2015-pairclassification
TwitterSemEval2015
An MTEB dataset
Massive Text Embedding Benchmark
Paraphrase-Pairs of Tweets from the SemEval 2015 workshop.
Task category
t2t
Domains
Social, Written
Reference
https://alt.qcri.org/semeval2015/task1/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["TwitterSemEval2015"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/twittersemeval2015-pairclassification.twitterurlcorpus-pairclassification
TwitterURLCorpus
An MTEB dataset
Massive Text Embedding Benchmark
Paraphrase-Pairs of Tweets.
Task category
t2t
Domains
Social, Written
Reference
https://languagenet.github.io/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["TwitterURLCorpus"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run… See the full description on the dataset page: https://huggingface.co/datasets/mteb/twitterurlcorpus-pairclassification.twitter-financial-news-topic
Dataset Description
The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their topic.
The dataset holds 21,107 documents annotated with 20 labels:
topics = {
"LABEL_0": "Analyst Update",
"LABEL_1": "Fed | Central Banks",
"LABEL_2": "Company | Product News",
"LABEL_3": "Treasuries | Corporate Debt",
"LABEL_4": "Dividend"… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-topic.da-hashtag-twitterhjerne
Dataset Card for "da-hashtag-twitterhjerne"
Danish questions asked on Twitter using the Hashtag "#Twitterhjerne" ('Twitter brain') and their answers.
For each question tweet 2-6 answer tweets are included.
Further details can be found in Section 4.2.3 in the thesis.
Produced by: Søren Vejlgaard Holm under supervision of Lars Kai Hansen and Martin Carsten Nielsen.
Usable for: Question Answering Evaluation.
Contact: Søren Vejlgaard Holm at swiho@dtu.dk or swh@alvenir.ai.