Datasets:

mteb
/

PoemSentimentClassification

text string	label int64
with pale blue berries. in these peaceful shades--	1
it flows so long as falls the rain,	2
and that is why, the lonesome day,	0
when i peruse the conquered fame of heroes, and the victories of mighty generals, i do not envy the generals,	3
of inward strife for truth and liberty.	3
the red sword sealed their vows!	3
and very venus of a pipe.	2
who the man, who, called a brother.	2
and so on. then a worthless gaud or two,	0
to hide the orb of truth--and every throne	2
the call's more urgent when he journeys slow.	2
with the _quart d'heure_ of rabelais!	2
and match, and bend, and thorough-blend, in her colossal form and face.	2
have i played in different countries.	2
tells us that the day is ended."	2
and not alone by gold;	2
that has a charmingly bourbon air.	1
sounded o'er earth and sea its blast of war,	0
chief poet on the tiber-side	2
as under a sunbeam a cloud ascends,	2
brightly expressive as the twins of leda,	1
of night, and all things now retir'd to rest	2
in latmian fountains long ago.	2
in monumental pomp! no grecian drop	1
and when they reached the house,	2
then this old orchard, sloping to the west;	2
so prythee get thee gone.	2
the other dark-eyed dears	2
me honied paths forsake;	2
to that mysterious strand.	2
wid a song up on de way.	2
her visions and those we have seen,--	2
he sat beside the governor and said grace;	2
fifty times the brahmins' offer deluged all the floor.	2
and what are all the prizes won	2
made snow of all the blossoms; at my feet	2
he never told us what he was,	2
want and woe, which torture us,	0
a ruby, and a pearl, or so,	2
an echo returned on the cold gray morn,	0
he says he’s hungry,—he would rather have	2
while i, ... i built up follies like a wall	0
and then he shut his little eyes,	2
ah, what a pang of aching sharp surprise	0
and gladys said,	2
peep timidly from out its nest,	2
the oriole's fledglings fifty times	2
the hostile cohorts melt away;	3
and the old swallow-haunted barns,--	0
from god's design, with threads of rain!	2
how over, though, for even me who knew	2
warped into adamantine fretwork, hung	2
wilt thou forget the love that joined us here?	2
the which she bearing home it burned her nest,	0
have roughened in the gales!	2
pilgrim and soldier, saint and sage,	2
down in the west upon the ocean floor	2
"what did you hear, for instance?" willis said.	2
should favour equal to the sons of heaven:	2
some, not so large, in rings,--	2
the crown of sorrow on their heads, their loss	0
the eternal law,	2
and lips where heavenly smiles would hang and blend	1
we're a band!" said the weary big dragoon.	2
fu' to ba' de battle's brunt.	2
and brief related whom they brought, wher found,	2
i lay and watched the lonely gloom;	0
honour to the bugle-horn!	1
a sceptre,--monstrous, winged, intolerable.	0
max laid his hand upon the old man's arm,	2
when on the boughs the purple buds expand,	2
if the pure and holy angels	1
endymion would have passed across the mead	2
upon the thought of perfect noon. and when	1
thy hands all cunning arts that women prize.	1
reasoning to admiration, and with mee	1
while the rude winds blow off each shadowy crown.	0
the former, as the slacken’d reins he drew	2
she falls back from the freedom she had hoped."	2
then--i would gather it, to thee unaware,	2
amidst the gold and the purple, and the pillows of his bed:	2
all hastening onward, yet none seemed to know	2
the wheat-blade whispers of the sheaf.	2
but o, nevermore can we prison him tight.	0
under these leafy vaults and walls,	2
(distinctly here the spirit sneezed,)	2
it shines superior on a throne of gold:	1
around it cling.	2
may meditate a whole youth's loss,	0
i'm safe enlisted fer the war,	2
whom phoebus taught unerring prophecy,	2
when thee, the eyes of that harsh long ago	0
flutter,	2
a way that safely will my passage guide.”	2
and breaths were gathering sure	2
you have done this, says one judge; done that, says another;	2
in their archetypes endure.	2
returne, the starres of morn shall see him rise	2
brown-gabled, long, and full of seams	2
the foes inclosing, and his friend pursued,	0

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PoemSentimentClassification

An MTEB dataset

Massive Text Embedding Benchmark

Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg.


Task category	t2c
Domains	Reviews, Written
Reference	https://arxiv.org/abs/2011.02686

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(["PoemSentimentClassification"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repitory.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.


@misc{sheng2020investigating,
  archiveprefix = {arXiv},
  author = {Emily Sheng and David Uthus},
  eprint = {2011.02686},
  primaryclass = {cs.CL},
  title = {Investigating Societal Biases in a Poetry Composition System},
  year = {2020},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("PoemSentimentClassification")

desc_stats = task.metadata.descriptive_stats

{
    "validation": {
        "num_samples": 105,
        "number_of_characters": 4096,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 12,
        "average_text_length": 39.00952380952381,
        "max_text_length": 64,
        "unique_text": 105,
        "unique_labels": 3,
        "labels": {
            "2": {
                "count": 69
            },
            "1": {
                "count": 17
            },
            "0": {
                "count": 19
            }
        }
    },
    "test": {
        "num_samples": 104,
        "number_of_characters": 3907,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 9,
        "average_text_length": 37.56730769230769,
        "max_text_length": 75,
        "unique_text": 104,
        "unique_labels": 3,
        "labels": {
            "2": {
                "count": 69
            },
            "1": {
                "count": 16
            },
            "0": {
                "count": 19
            }
        }
    },
    "train": {
        "num_samples": 892,
        "number_of_characters": 34197,
        "number_texts_intersect_with_train": null,
        "min_text_length": 7,
        "average_text_length": 38.337443946188344,
        "max_text_length": 109,
        "unique_text": 892,
        "unique_labels": 4,
        "labels": {
            "1": {
                "count": 133
            },
            "2": {
                "count": 555
            },
            "0": {
                "count": 155
            },
            "3": {
                "count": 49
            }
        }
    }
}

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