Datasets:
Tasks:
Text Classification
Languages:
Spanish
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
expert-generated
Tags:
init commit
Browse files- README.md +154 -1
- hashed_dataset/hashed_test.tsv +156 -0
- hashed_dataset/hashed_train.tsv +1321 -0
- hashed_dataset/hashed_valid.tsv +78 -0
- sts-es.py +112 -0
- to_hash.py +30 -0
README.md
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---
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| 1 |
---
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YAML tags:
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annotations_creators:
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- expert-generated
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language:
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- es
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language_creators:
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- found
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multilinguality:
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- monolingual
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pretty_name: STS-es
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size_categories: []
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source_datasets: []
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tags: []
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task_categories:
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- text-classification
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task_ids:
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- semantic-similarity-scoring
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- text-scoring
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---
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# STS-es
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Website:** https://alt.qcri.org/semeval2014/task10/
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- **Point of Contact:** [Aitor Gonzalez](aitor.gonzalez@bsc.es)
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### Dataset Summary
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For Semantic Text Similarity, we collected the Spanish test sets from SemEval-2014 (Agirre et al., 2014) and SemEval-2015 (Agirre et al., 2015). Since no training data was provided for the Spanish subtask, we randomly sampled both datasets into 1,321 sentences for the train set, 78 sentences for the development set, and 156 sentences for the test set. To make the task harder for the models, we purposely made the development set smaller than the test set.
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We use this corpus as part of the EvalEs Spanish language benchmark.
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### Supported Tasks and Leaderboards
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Semantic Text Similarity Scoring
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### Languages
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The dataset is in Spanish (`es-ES`)
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## Dataset Structure
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### Data Instances
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<pre>
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El "tendón de Aquiles" ("tendo Achillis") o "tendón calcáneo" ("tendo calcaneus") es un tendón de la parte posterior de la pierna. El tendón de Aquiles es la extensión tendinosa de los tres músculos de la pantorrilla: gemelo, sóleo y plantar delgado. 2.8
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</pre>
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### Data Fields
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- Sentence 1
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- Sentence 2
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- Similarity Score
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### Data Splits
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- train: 1,321 instances
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- dev: 78 instances
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- test: 156 instances
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## Dataset Creation
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### Curation Rationale
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[N/A]
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### Source Data
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The source data came from the Spanish Wikipedia (2013 dump) and texts from Spanish news (2014).
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For more information visit the paper from the SemEval-2014 Shared Task [(Agirre et al., 2014)](https://aclanthology.org/S14-2010.pdf) and the SemEval-2015 Shared Task [(Agirre et al., 2015)](https://aclanthology.org/S15-2045.pdf).
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#### Initial Data Collection and Normalization
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For more information visit the paper from the SemEval-2014 Shared Task [(Agirre et al., 2014)](https://aclanthology.org/S14-2010.pdf) and the SemEval-2015 Shared Task [(Agirre et al., 2015)](https://aclanthology.org/S15-2045.pdf).
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#### Who are the source language producers?
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Journalists and Wikipedia contributors.
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### Annotations
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#### Annotation process
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For more information visit the paper from the SemEval-2014 Shared Task [(Agirre et al., 2014)](https://aclanthology.org/S14-2010.pdf) and the SemEval-2015 Shared Task [(Agirre et al., 2015)](https://aclanthology.org/S15-2045.pdf).
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#### Who are the annotators?
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For more information visit the paper from the SemEval-2014 Shared Task [(Agirre et al., 2014)](https://aclanthology.org/S14-2010.pdf) and the SemEval-2015 Shared Task [(Agirre et al., 2015)](https://aclanthology.org/S15-2045.pdf).
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### Personal and Sensitive Information
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[N/A]
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## Considerations for Using the Data
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### Social Impact of Dataset
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This dataset contributes to the development of language models in Spanish.
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### Discussion of Biases
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[N/A]
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### Other Known Limitations
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[N/A]
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## Additional Information
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### Dataset Curators
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[N/A]
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### Licensing Information
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[N/A]
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### Citation Information
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The following paper must be cited when using this corpus:
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Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, and Janyce Wiebe. 2015. SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 252–263, Denver, Colorado. Association for Computational Linguistics.
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Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014. SemEval-2014 Task 10: Multilingual Semantic Textual Similarity. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 81–91, Dublin, Ireland. Association for Computational Linguistics.
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| 156 |
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|
hashed_dataset/hashed_train.tsv
ADDED
|
@@ -0,0 +1,1321 @@
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| 1100 |
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0115ae22676532c63c333b6b42d4899819219c366347063069d02b781ea5a35a 0
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|
| 1317 |
+
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|
| 1318 |
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|
| 1319 |
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|
| 1320 |
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|
| 1321 |
+
381983b11eb0868302f71fea76d8afec2ef677335c1e743404f0699af2f8461b 2.6
|
hashed_dataset/hashed_valid.tsv
ADDED
|
@@ -0,0 +1,78 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
|
|
|
| 1 |
+
12a65fa7b4cff5bb01f11e1de5744f7e4b5060b9f92d8be99376f509223d6c6d 1.8
|
| 2 |
+
6590d3792a8d488abaf0ff1e9267c16fd857a9e00b16b2f5dee282e53e741892 1.6
|
| 3 |
+
58fb1d30322977d25fca7502671a9fc910c799ec42d593ec3730e82a414203d1 2.2
|
| 4 |
+
75c60f776eff659654deeaea884585ff23678d1385395a0d9cec82c84a6473e7 0.8
|
| 5 |
+
433775571f4754a814850906c690e7a7b282c731c42c93657129d8f9237e3e16 2.4
|
| 6 |
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|
| 7 |
+
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|
| 8 |
+
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|
| 9 |
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|
| 10 |
+
5ce83c32d1fe362351dd88d29d17a970aacfc3d738086860645f8ce459b52e87 2.4
|
| 11 |
+
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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1d9c054e1fa2f7364f0626d38d24a40d548ff4e12c01d0efc51e23d706f4326c 2.4
|
| 25 |
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|
| 26 |
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dd9157b2c0621271c7079dbe16e129cd61d6b5a83bc13f94841a74d4461e4973 1.4
|
| 27 |
+
7d11d182d76505c22c0a2ec05a3fa524e37466ad185f03af6cfaa47377bbfd3f 0.2
|
| 28 |
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27fb0b919c50f44d581b37ecd486fe1918dda24da3d10649436a1c8d942586c9 0.2
|
| 29 |
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b7527d186affef7b51fb275e701b70c63c5628cfe20fb78b0fde25473ad37f44 0
|
| 30 |
+
bd0d9a967131fe01a7a47719541be6a60403928e87ee04e73a6760a13d88174d 3.6
|
| 31 |
+
6e2468c4d52b5488e902beec5a2899480ce5593418285ff93202b2c4a9956382 1.6
|
| 32 |
+
3a7681221658dacb7444cb9616aa5081cd033bc8b0fa0f7feb88808decb253a0 2.2
|
| 33 |
+
d846fec25c3ca29c34e48fd3e1223e9bc9bbcd762e5fa43aa754f3793a430be3 2.2
|
| 34 |
+
59cf77db3ad6b705c1afc3d7ecce78f2f6d91ca263fef96d659b7b99a61ec13b 2.4
|
| 35 |
+
140221241fbb87d052edffed00ba00cc4c36590f7587e79af508b36a7963e8fd 3.2
|
| 36 |
+
1bb1faf3887ee8f74350886162c07c788bb614dc81cd694047f9bb239e5ac581 1
|
| 37 |
+
dbe8047721d5c5f561de24f34674b454e51c52883c36986aa8b3cdf9487f9747 2.2
|
| 38 |
+
d541397a19b825f5501be19092626caa4a007eaebdfd9c3070513249deb7fceb 1.4
|
| 39 |
+
c2b5cc106bd9ecf7a36ca2f69c1fd882ecc066f73076ceec5d2e271d5ccafac0 2.4
|
| 40 |
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6665c7c89bbf0d9ebe5e8b16637d82fb7e31d0a4da6271f6cad1af7105fc439e 2
|
| 41 |
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140221241fbb87d052edffed00ba00cc4c36590f7587e79af508b36a7963e8fd 3.6
|
| 42 |
+
05e83f1d21d92b890f4a4ed17303e010a358b172fe5586e9cf11f8cccae548ae 3.6
|
| 43 |
+
16235c18cd8c13550b8072f25f016fde546c76b82924ccfc61647b8d37b9b7ed 2.6
|
| 44 |
+
195da8c3c977efc7f3ebc9824b74beceac3f5a4797d637287b86456edc46059e 1.4
|
| 45 |
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d9b22bb5d2ecce8d3381b73054204e43d3d033be1d2e9ae6c821ef0c701ad8b0 0
|
| 46 |
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e90724f4609b9586776e72ba480a7b679c2e4ef1b37ddf1e689ef67f3b3e0c0b 0.8
|
| 47 |
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cca1e0ad9123d71a0383021ccb0c4661faae5bdefba2437a16dbd9feee87c0a1 3.4
|
| 48 |
+
4e9a9761b00f1a50fcd6459b8e21f8c46c286db44af518c10f45ddf5e512be0a 1
|
| 49 |
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8ddbdedcded0485a36f7ce7b99a7696ce40c0915dd4bb8fb4dc9c88b38b8840d 2.6
|
| 50 |
+
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|
| 51 |
+
e41b6062bce101870109cce08fed3db1c244e16c0a4e2ed9c12ed71734449aa3 2.2
|
| 52 |
+
94933067c30d2b7aed7968850546c897bd18b7d9851b2b633b1e2d7cfcec38bc 2.2
|
| 53 |
+
b1cb2ad5fd7aebe5367a0cc373e1079df32bc632c721d4c121dcbf43f8ff2a65 3.4
|
| 54 |
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|
| 55 |
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|
| 56 |
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8ddd50e0e6fa16556a2cc81c945e42dc1b9b9cb25aa1c99efa9b8b45f6444dd6 2.2
|
| 57 |
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106ac7a7322562e11ff3307a545ac7662f8fe427af3d7889b1907be54e12ffaa 3
|
| 58 |
+
7b362b82f257294a8e43ad89ce8d3b5ca7009bf59528d1c588e178b73e4917e8 2.6
|
| 59 |
+
d74bcf49d2e97d74094f77c5e6b21bc863581a202558c319150c3f14c494aef5 3.2
|
| 60 |
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6a0e67117e67abae83545a4aa54819caa800ea5db31f204b9abaf21f9add076e 2.6
|
| 61 |
+
0b6f217db268898c5d23dbfee4b39b50c352d3106d2e43fc8b7a05628f99d05e 3.2
|
| 62 |
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09f5c197b35fd8532e3f88a3fcf2d3aa35de49305c06725fb0a781ccc82ada42 2.2
|
| 63 |
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446d68eea49bb80dc869bd52a0fa3e8ad8c240dbbc30ab82f0008865d3a31842 2
|
| 64 |
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|
| 65 |
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594b6163cec0738c7378d9e0d447ed0dc8295f1d774666c807095c1123d7c69f 0
|
| 66 |
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3fa9e6406aead3d22bf199a35cf6cb4f0ea1e96e4b328c8e4e8506bfd3673c63 2.4
|
| 67 |
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6ea040c216be71b0550e3afd6b157ef768220e839711114d64a2c727b443fe4e 4
|
| 68 |
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39180a95367f2ce5c4227bb6695af3b4e13af53a7283ecc6414c5bca8c726f7d 1.8
|
| 69 |
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31d5d5b53d66a01afc6969e33ccd68743c19ff231d65ae49e1c065f16d1dc98b 3
|
| 70 |
+
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|
| 71 |
+
859cae5e052fbe59c7f9d74278fc7e9e9702b4123550b72125500db5972b57ab 2
|
| 72 |
+
ba249e66c2d91cabd7bc816e8068287cc0a546b33108ccfb227a4b036b21bcb8 3.6
|
| 73 |
+
d35e88077283a62a2e951893e41d4bf6ebb4a874ab8e0736427fe5d6305ba50a 3.6
|
| 74 |
+
7125e3f4ac63dd74ee439ca7aaed979e1aad756ba709df8f550f8c5d3fcace7b 1.6
|
| 75 |
+
f7e25b21594a8809f361531fd3efaa6aa046f6a2e67c5eee673a640dabde733f 0.2
|
| 76 |
+
65a582526c0ae898c8aee352930ba3dfd1e4a494fd9c495337a1ed46bc84c95d 2
|
| 77 |
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9f4c8ce01a30c20257a1ab16584116e84a96b9304de8651abb8f710ba8feee02 3.8
|
| 78 |
+
ca181b87afc4eb60fbeb5ce82440d96a35565bc4ceb00e5a0efaa65877109f18 2.8
|
sts-es.py
ADDED
|
@@ -0,0 +1,112 @@
|
|
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|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
|
|
|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
| 1 |
+
# Loading script for the STS dataset.
|
| 2 |
+
import datasets
|
| 3 |
+
import os
|
| 4 |
+
import csv
|
| 5 |
+
import hashlib
|
| 6 |
+
import pandas as pd
|
| 7 |
+
|
| 8 |
+
logger = datasets.logging.get_logger(__name__)
|
| 9 |
+
|
| 10 |
+
_CITATION = """
|
| 11 |
+
Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, and Janyce Wiebe. 2015. SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 252–263, Denver, Colorado. Association for Computational Linguistics.
|
| 12 |
+
|
| 13 |
+
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014. SemEval-2014 Task 10: Multilingual Semantic Textual Similarity. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 81–91, Dublin, Ireland. Association for Computational Linguistics.
|
| 14 |
+
"""
|
| 15 |
+
|
| 16 |
+
_DESCRIPTION = """
|
| 17 |
+
For Semantic Text Similarity, we collected the Spanish test sets from SemEval-2014 (Agirre et al., 2014) and SemEval-2015 (Agirre et al., 2015). Since no training data was provided for the Spanish subtask, we randomly sampled both datasets into 1,321 sentences for the train set, 78 sentences for the development set, and 156 sentences for the test set. To make the task harder for the models, we purposely made the development set smaller than the test set.
|
| 18 |
+
"""
|
| 19 |
+
|
| 20 |
+
_HOMEPAGE = """http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page"""
|
| 21 |
+
|
| 22 |
+
_URL = "http://ixa2.si.ehu.es/stswiki/images/"
|
| 23 |
+
_FILE_1 = "9/9d/STS2015-es-test.zip"
|
| 24 |
+
_FILE_2 = "9/9a/STS2014-es-test.zip"
|
| 25 |
+
|
| 26 |
+
_LOC_HASHED_FILES = 'https://huggingface.co/datasets/Severino/sts-es/resolve/main/hashed_dataset'
|
| 27 |
+
|
| 28 |
+
_TRAIN = "hashed_train.tsv"
|
| 29 |
+
_VALID = "hashed_valid.tsv"
|
| 30 |
+
_TEST = "hashed_test.tsv"
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
class STSConfig(datasets.BuilderConfig):
|
| 34 |
+
""" Builder config for the STS dataset """
|
| 35 |
+
|
| 36 |
+
def __init__(self, **kwargs):
|
| 37 |
+
"""BuilderConfig for STS.
|
| 38 |
+
Args:
|
| 39 |
+
**kwargs: keyword arguments forwarded to super.
|
| 40 |
+
"""
|
| 41 |
+
super(STSConfig, self).__init__(**kwargs)
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
class STS(datasets.GeneratorBasedBuilder):
|
| 45 |
+
""" STS dataset."""
|
| 46 |
+
|
| 47 |
+
BUILDER_CONFIGS = [
|
| 48 |
+
STSConfig(
|
| 49 |
+
name="STS",
|
| 50 |
+
version=datasets.Version("1.0.0"),
|
| 51 |
+
description="STS dataset"
|
| 52 |
+
),
|
| 53 |
+
]
|
| 54 |
+
|
| 55 |
+
def _info(self):
|
| 56 |
+
return datasets.DatasetInfo(
|
| 57 |
+
description=_DESCRIPTION,
|
| 58 |
+
features=datasets.Features(
|
| 59 |
+
{
|
| 60 |
+
"id": datasets.Value("string"),
|
| 61 |
+
"sentence1": datasets.Value("string"),
|
| 62 |
+
"sentence2": datasets.Value("string"),
|
| 63 |
+
"label": datasets.features.Value('float')
|
| 64 |
+
}
|
| 65 |
+
),
|
| 66 |
+
supervised_keys=None,
|
| 67 |
+
homepage=_HOMEPAGE,
|
| 68 |
+
citation=_CITATION,
|
| 69 |
+
)
|
| 70 |
+
|
| 71 |
+
def _split_generators(self, dl_manager):
|
| 72 |
+
"""Returns SplitGenerators."""
|
| 73 |
+
# download original official sentences dataset
|
| 74 |
+
urls_to_download = {
|
| 75 |
+
"file1": f"{_URL}{_FILE_1}",
|
| 76 |
+
"file2": f"{_URL}{_FILE_2}",
|
| 77 |
+
}
|
| 78 |
+
downloaded_files = dl_manager.download_and_extract(urls_to_download)
|
| 79 |
+
# create a hash-sentences dictionary with the original sentences
|
| 80 |
+
hashes_sentences_dict = {}
|
| 81 |
+
for file_number, folder_path in downloaded_files.items():
|
| 82 |
+
for file_name in os.listdir(folder_path):
|
| 83 |
+
if 'STS.input' not in file_name:
|
| 84 |
+
continue
|
| 85 |
+
i_f = open(os.path.join(folder_path, file_name))
|
| 86 |
+
lines = csv.reader(i_f, delimiter='\t', quoting=csv.QUOTE_NONE)
|
| 87 |
+
for line in lines:
|
| 88 |
+
hash_ = hashlib.sha256(f"{line[0]}{line[1]}".encode('utf-8')).hexdigest()
|
| 89 |
+
if hash_ in hashes_sentences_dict and hashes_sentences_dict[hash_] != line:
|
| 90 |
+
print(f"WARNING: hash {hash_} is repeated.\nThis is produced by the following couple of "
|
| 91 |
+
f"sentences:\n - {hashes_sentences_dict[hash_]}\n - {line}\nThis can cause problems!\n")
|
| 92 |
+
hashes_sentences_dict[hash_] = line
|
| 93 |
+
# generate splits
|
| 94 |
+
return [
|
| 95 |
+
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{_LOC_HASHED_FILES}/{_TRAIN}", "hash_dict":hashes_sentences_dict}),
|
| 96 |
+
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": f"{_LOC_HASHED_FILES}/{_VALID}", "hash_dict":hashes_sentences_dict}),
|
| 97 |
+
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": f"{_LOC_HASHED_FILES}/{_TEST}", "hash_dict":hashes_sentences_dict}),
|
| 98 |
+
]
|
| 99 |
+
|
| 100 |
+
def _generate_examples(self, filepath, hash_dict):
|
| 101 |
+
|
| 102 |
+
logger.info("⏳ Generating examples from = %s", filepath)
|
| 103 |
+
df = pd.read_csv(filepath, delimiter='\t', quoting=csv.QUOTE_NONE)
|
| 104 |
+
for index, line in df.iterrows():
|
| 105 |
+
sentence1, sentence2 = hash_dict[line[0]]
|
| 106 |
+
label = line[1]
|
| 107 |
+
yield index, {
|
| 108 |
+
"id": str(index),
|
| 109 |
+
"sentence1": sentence1,
|
| 110 |
+
"sentence2": sentence2,
|
| 111 |
+
"label": label
|
| 112 |
+
}
|
to_hash.py
ADDED
|
@@ -0,0 +1,30 @@
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
import hashlib
|
| 2 |
+
import csv
|
| 3 |
+
|
| 4 |
+
INPUT_OUTPUT_COUPLES = [
|
| 5 |
+
("./dataset/train.tsv", "./hashed_dataset/hashed_train.tsv"),
|
| 6 |
+
("./dataset/valid.tsv", "./hashed_dataset/hashed_valid.tsv"),
|
| 7 |
+
("./dataset/test.tsv", "./hashed_dataset/hashed_test.tsv")]
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
def main(input_file, output_file):
|
| 11 |
+
with open(input_file) as f:
|
| 12 |
+
r = csv.reader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
|
| 13 |
+
data=[]
|
| 14 |
+
for line in r:
|
| 15 |
+
data.append(line)
|
| 16 |
+
hashes_and_labels = []
|
| 17 |
+
for line in data:
|
| 18 |
+
hash_ = hashlib.sha256(f"{line[0]}{line[1]}".encode('utf-8')).hexdigest()
|
| 19 |
+
if len(line) < 3:
|
| 20 |
+
print(line)
|
| 21 |
+
label = line[2]
|
| 22 |
+
hashes_and_labels.append((hash_, label))
|
| 23 |
+
with open(output_file, 'w') as o:
|
| 24 |
+
for hash_, label in hashes_and_labels:
|
| 25 |
+
o.write(f"{hash_}\t{label}\n")
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
if __name__ == "__main__":
|
| 29 |
+
for input_file, output_file in INPUT_OUTPUT_COUPLES:
|
| 30 |
+
main(input_file, output_file)
|