diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index a770de78287b5353fb380cf8774b1e6caf2881d9..0000000000000000000000000000000000000000 --- a/.gitattributes +++ /dev/null @@ -1,37 +0,0 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zstandard filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text -# Audio files - uncompressed -*.pcm filter=lfs diff=lfs merge=lfs -text -*.sam filter=lfs diff=lfs merge=lfs -text -*.raw filter=lfs diff=lfs merge=lfs -text -# Audio files - compressed -*.aac filter=lfs diff=lfs merge=lfs -text -*.flac filter=lfs diff=lfs merge=lfs -text -*.mp3 filter=lfs diff=lfs merge=lfs -text -*.ogg filter=lfs diff=lfs merge=lfs -text -*.wav filter=lfs diff=lfs merge=lfs -text diff --git a/CITATION.cff b/CITATION.cff deleted file mode 100644 index c8306b9f6de5e7928899714c7abd964f1883a5ee..0000000000000000000000000000000000000000 --- a/CITATION.cff +++ /dev/null @@ -1,50 +0,0 @@ -cff-version: 1.0.0 -message: "MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages" -authors: -- family-names: "FitzGerald" - given-names: "Jack" -- family-names: "Hench" - given-names: "Christopher" -- family-names: "Peris" - given-names: "Charith" -- family-names: "Mackie" - given-names: "Scott" -- family-names: "Rottmann" - given-names: "Kay" -- family-names: "Sanchez" - given-names: "Ana" -- family-names: "Nash" - given-names: "Aaron" -- family-names: "Urbach" - given-names: "Liam" -- family-names: "Kakarala" - given-names: "Vishesh" -- family-names: "Singh" - given-names: "Richa" -- family-names: "Ranganath" - given-names: "Swetha" -- family-names: "Crist" - given-names: "Laurie" -- family-names: "Britan" - given-names: "Misha" -- family-names: "Leeuwis" - given-names: "Wouter" -- family-names: "Tur" - given-names: "Gokhan" -- family-names: "Natarajan" - given-names: "Prem" -title: "MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages" -version: 1.0.0 -date-released: 2022-04-18 -url: "https://arxiv.org/abs/2204.08582" - - - - - - - - - - - \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 5e3fb878c9b8905a8bc6cf460f7f58999a46023c..0000000000000000000000000000000000000000 --- a/LICENSE +++ /dev/null @@ -1,397 +0,0 @@ -Copyright Amazon.com Inc. or its affiliates. - -Attribution 4.0 International - -======================================================================= - -Creative Commons Corporation ("Creative Commons") is not a law firm and -does not provide legal services or legal advice. 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For -the avoidance of doubt, this paragraph does not form part of the public -licenses. - -Creative Commons may be contacted at creativecommons.org. diff --git a/README.md b/README.md deleted file mode 100644 index acee1cd9ccd5cdc6af177d9c9db747265e17a5c9..0000000000000000000000000000000000000000 --- a/README.md +++ /dev/null @@ -1,864 +0,0 @@ ---- -annotations_creators: -- expert-generated -language_creators: -- found -license: -- cc-by-4.0 -multilinguality: -- af-ZA -- am-ET -- ar-SA -- az-AZ -- bn-BD -- ca-ES -- cy-GB -- da-DK -- de-DE -- el-GR -- en-US -- es-ES -- fa-IR -- fi-FI -- fr-FR -- he-IL -- hi-IN -- hu-HU -- hy-AM -- id-ID -- is-IS -- it-IT -- ja-JP -- jv-ID -- ka-GE -- km-KH -- kn-IN -- ko-KR -- lv-LV -- ml-IN -- mn-MN -- ms-MY -- my-MM -- nb-NO -- nl-NL -- pl-PL -- pt-PT -- ro-RO -- ru-RU -- sl-SL -- sq-AL -- sv-SE -- sw-KE -- ta-IN -- te-IN -- th-TH -- tl-PH -- tr-TR -- ur-PK -- vi-VN -- zh-CN -- zh-TW -size_categories: -- 100K 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. - -| Name | Lang | Utt/Lang | Domains | Intents | Slots | -|:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:| -| MASSIVE 1.1 | 52 | 19,521 | 18 | 60 | 55 | -| SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 | -| NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 | -| Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 | -| ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 | -| MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 | -| Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 | -| Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 | -| Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 | -| Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 | -| Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 | -| Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 | -| Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - | -| Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 | - -### Supported Tasks and Leaderboards - -The dataset can be used to train a model for `natural-language-understanding` (NLU) : - -- `intent-classification` -- `multi-class-classification` -- `natural-language-understanding` - -### Languages - -The MASSIVE 1.1 corpora consists of parallel sentences from 52 languages : - -- `Afrikaans - South Africa (af-ZA)` -- `Amharic - Ethiopia (am-ET)` -- `Arabic - Saudi Arabia (ar-SA)` -- `Azeri - Azerbaijan (az-AZ)` -- `Bengali - Bangladesh (bn-BD)` -- `Catalan - Spain (ca-ES)` -- `Chinese - China (zh-CN)` -- `Chinese - Taiwan (zh-TW)` -- `Danish - Denmark (da-DK)` -- `German - Germany (de-DE)` -- `Greek - Greece (el-GR)` -- `English - United States (en-US)` -- `Spanish - Spain (es-ES)` -- `Farsi - Iran (fa-IR)` -- `Finnish - Finland (fi-FI)` -- `French - France (fr-FR)` -- `Hebrew - Israel (he-IL)` -- `Hungarian - Hungary (hu-HU)` -- `Armenian - Armenia (hy-AM)` -- `Indonesian - Indonesia (id-ID)` -- `Icelandic - Iceland (is-IS)` -- `Italian - Italy (it-IT)` -- `Japanese - Japan (ja-JP)` -- `Javanese - Indonesia (jv-ID)` -- `Georgian - Georgia (ka-GE)` -- `Khmer - Cambodia (km-KH)` -- `Korean - Korea (ko-KR)` -- `Latvian - Latvia (lv-LV)` -- `Mongolian - Mongolia (mn-MN)` -- `Malay - Malaysia (ms-MY)` -- `Burmese - Myanmar (my-MM)` -- `Norwegian - Norway (nb-NO)` -- `Dutch - Netherlands (nl-NL)` -- `Polish - Poland (pl-PL)` -- `Portuguese - Portugal (pt-PT)` -- `Romanian - Romania (ro-RO)` -- `Russian - Russia (ru-RU)` -- `Slovanian - Slovania (sl-SL)` -- `Albanian - Albania (sq-AL)` -- `Swedish - Sweden (sv-SE)` -- `Swahili - Kenya (sw-KE)` -- `Hindi - India (hi-IN)` -- `Kannada - India (kn-IN)` -- `Malayalam - India (ml-IN)` -- `Tamil - India (ta-IN)` -- `Telugu - India (te-IN)` -- `Thai - Thailand (th-TH)` -- `Tagalog - Philippines (tl-PH)` -- `Turkish - Turkey (tr-TR)` -- `Urdu - Pakistan (ur-PK)` -- `Vietnamese - Vietnam (vi-VN)` -- `Welsh - United Kingdom (cy-GB)` - -## Load the dataset with HuggingFace - -```python -from datasets import load_dataset -dataset = load_dataset("AmazonScience/massive", "en-US", split='train') -print(dataset[0]) -``` - -## Dataset Structure - -### Data Instances - -```json -{ - "id": "0", - "locale": "fr-FR", - "partition": "test", - "scenario": "alarm", - "intent": "alarm_set", - "utt": "réveille-moi à cinq heures du matin cette semaine", - "annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]", - "worker_id": "22", - "slot_method": [ - { "slot": "time", "method": "translation" }, - { "slot": "date", "method": "translation" } - ], - "judgments": [ - { - "worker_id": "22", - "intent_score": 1, - "slots_score": 1, - "grammar_score": 4, - "spelling_score": 2, - "language_identification": "target" - }, - { - "worker_id": "8", - "intent_score": 1, - "slots_score": 1, - "grammar_score": 4, - "spelling_score": 2, - "language_identification": "target" - }, - { - "worker_id": "0", - "intent_score": 1, - "slots_score": 1, - "grammar_score": 4, - "spelling_score": 2, - "language_identification": "target" - } - ] -} -``` - -### Data Fields - -`id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization. - -`locale`: is the language and country code accoring to ISO-639-1 and ISO-3166. - -`partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp). - -`scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance - -`intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}` - -`utt`: the raw utterance text without annotations - -`annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]` - -`worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales. - -`slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification). - -`judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker. - -```plain -intent_score : "Does the sentence match the intent?" - 0: No - 1: Yes - 2: It is a reasonable interpretation of the goal - -slots_score : "Do all these terms match the categories in square brackets?" - 0: No - 1: Yes - 2: There are no words in square brackets (utterance without a slot) - -grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?" - 0: Completely unnatural (nonsensical, cannot be understood at all) - 1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language) - 2: Some errors (the meaning can be understood but it doesn't sound natural in your language) - 3: Good enough (easily understood and sounds almost natural in your language) - 4: Perfect (sounds natural in your language) - -spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error." - 0: There are more than 2 spelling errors - 1: There are 1-2 spelling errors - 2: All words are spelled correctly - -language_identification : "The following sentence contains words in the following languages (check all that apply)" - 1: target - 2: english - 3: other - 4: target & english - 5: target & other - 6: english & other - 7: target & english & other -``` - -### Data Splits - -|Language|Train|Dev|Test| -|:---:|:---:|:---:|:---:| -|af-ZA|11514|2033|2974| -|am-ET|11514|2033|2974| -|ar-SA|11514|2033|2974| -|az-AZ|11514|2033|2974| -|bn-BD|11514|2033|2974| -|ca-ES|11514|2033|2974| -|cy-GB|11514|2033|2974| -|da-DK|11514|2033|2974| -|de-DE|11514|2033|2974| -|el-GR|11514|2033|2974| -|en-US|11514|2033|2974| -|es-ES|11514|2033|2974| -|fa-IR|11514|2033|2974| -|fi-FI|11514|2033|2974| -|fr-FR|11514|2033|2974| -|he-IL|11514|2033|2974| -|hi-IN|11514|2033|2974| -|hu-HU|11514|2033|2974| -|hy-AM|11514|2033|2974| -|id-ID|11514|2033|2974| -|is-IS|11514|2033|2974| -|it-IT|11514|2033|2974| -|ja-JP|11514|2033|2974| -|jv-ID|11514|2033|2974| -|ka-GE|11514|2033|2974| -|km-KH|11514|2033|2974| -|kn-IN|11514|2033|2974| -|ko-KR|11514|2033|2974| -|lv-LV|11514|2033|2974| -|ml-IN|11514|2033|2974| -|mn-MN|11514|2033|2974| -|ms-MY|11514|2033|2974| -|my-MM|11514|2033|2974| -|nb-NO|11514|2033|2974| -|nl-NL|11514|2033|2974| -|pl-PL|11514|2033|2974| -|pt-PT|11514|2033|2974| -|ro-RO|11514|2033|2974| -|ru-RU|11514|2033|2974| -|sl-SL|11514|2033|2974| -|sq-AL|11514|2033|2974| -|sv-SE|11514|2033|2974| -|sw-KE|11514|2033|2974| -|ta-IN|11514|2033|2974| -|te-IN|11514|2033|2974| -|th-TH|11514|2033|2974| -|tl-PH|11514|2033|2974| -|tr-TR|11514|2033|2974| -|ur-PK|11514|2033|2974| -|vi-VN|11514|2033|2974| -|zh-CN|11514|2033|2974| -|zh-TW|11514|2033|2974| - - -### Personal and Sensitive Information - -The corpora is free of personal or sensitive information. - -## Additional Information - -### Dataset Curators - -__MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan. - -__SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena. - -__Hugging Face Upload and Integration__: Labrak Yanis (Not affiliated with the original corpus) - -### Licensing Information - -```plain -Copyright Amazon.com Inc. or its affiliates. - -Attribution 4.0 International - -======================================================================= - -Creative Commons Corporation ("Creative Commons") is not a law firm and -does not provide legal services or legal advice. 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For -the avoidance of doubt, this paragraph does not form part of the public -licenses. - -Creative Commons may be contacted at creativecommons.org. -``` - -### Citation Information - -Please cite the following papers when using this dataset. - -```latex -@misc{fitzgerald2022massive, - title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, - author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, - year={2022}, - eprint={2204.08582}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} - -@inproceedings{bastianelli-etal-2020-slurp, - title = "{SLURP}: A Spoken Language Understanding Resource Package", - author = "Bastianelli, Emanuele and - Vanzo, Andrea and - Swietojanski, Pawel and - Rieser, Verena", - booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", - month = nov, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2020.emnlp-main.588", - doi = "10.18653/v1/2020.emnlp-main.588", - pages = "7252--7262", - abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." -} -``` diff --git a/af-ZA/massive-test.parquet b/af-ZA/massive-test.parquet new file mode 100644 index 0000000000000000000000000000000000000000..142a5d6417e441a3e75eace97681031223e18c67 --- /dev/null +++ b/af-ZA/massive-test.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee3caa8644c06a623593f34aa1b4d77080a9fcda36417b2d609d3769d058b716 +size 217059 diff --git a/af-ZA/massive-train.parquet b/af-ZA/massive-train.parquet new file mode 100644 index 0000000000000000000000000000000000000000..ba42b445543ffbd737f21d54352389956a76ddcb --- /dev/null +++ b/af-ZA/massive-train.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e656f237350263a32de5fceaba4225909ca441e97ea36c7d0254a0aa4a56b3e3 +size 785314 diff --git a/af-ZA/massive-validation.parquet b/af-ZA/massive-validation.parquet new file mode 100644 index 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0000000000000000000000000000000000000000..8947ff0f36eb27d87caeda02678c65e964fe7b8c --- /dev/null +++ b/lv-LV/massive-validation.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e30441a3253da4ed2331e88bb53df2af8473bb2c06b7bce6ac564756cfc6c809 +size 164632 diff --git a/massive.py b/massive.py deleted file mode 100644 index 4731be1adc5c42b075603588248d8ae99cb01896..0000000000000000000000000000000000000000 --- a/massive.py +++ /dev/null @@ -1,660 +0,0 @@ -# coding=utf-8 - -"""MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages""" - -import json -import datasets - -logger = datasets.logging.get_logger(__name__) - -_DESCRIPTION = """\ - MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations - for the Natural Language Understanding tasks of intent prediction and slot annotation. - Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing - the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. -""" - -_CITATION = """\ - @misc{fitzgerald2022massive, - title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, - author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, - year={2022}, - eprint={2204.08582}, - archivePrefix={arXiv}, - primaryClass={cs.CL} - } - \ - @inproceedings{bastianelli-etal-2020-slurp, - title = "{SLURP}: A Spoken Language Understanding Resource Package", - author = "Bastianelli, Emanuele and - Vanzo, Andrea and - Swietojanski, Pawel and - Rieser, Verena", - booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", - month = nov, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2020.emnlp-main.588", - doi = "10.18653/v1/2020.emnlp-main.588", - pages = "7252--7262", - abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." - } -""" - -_LICENSE = """\ -Copyright Amazon.com Inc. or its affiliates. - -Attribution 4.0 International - -======================================================================= - -Creative Commons Corporation ("Creative Commons") is not a law firm and -does not provide legal services or legal advice. Distribution of -Creative Commons public licenses does not create a lawyer-client or -other relationship. 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If the provision - cannot be reformed, it shall be severed from this Public License - without affecting the enforceability of the remaining terms and - conditions. - - c. No term or condition of this Public License will be waived and no - failure to comply consented to unless expressly agreed to by the - Licensor. - - d. Nothing in this Public License constitutes or may be interpreted - as a limitation upon, or waiver of, any privileges and immunities - that apply to the Licensor or You, including from the legal - processes of any jurisdiction or authority. - - -======================================================================= - -Creative Commons is not a party to its public licenses. -Notwithstanding, Creative Commons may elect to apply one of its public -licenses to material it publishes and in those instances will be -considered the “Licensor.” The text of the Creative Commons public -licenses is dedicated to the public domain under the CC0 Public Domain -Dedication. Except for the limited purpose of indicating that material -is shared under a Creative Commons public license or as otherwise -permitted by the Creative Commons policies published at -creativecommons.org/policies, Creative Commons does not authorize the -use of the trademark "Creative Commons" or any other trademark or logo -of Creative Commons without its prior written consent including, -without limitation, in connection with any unauthorized modifications -to any of its public licenses or any other arrangements, -understandings, or agreements concerning use of licensed material. For -the avoidance of doubt, this paragraph does not form part of the public -licenses. - -Creative Commons may be contacted at creativecommons.org. -""" -# version 1.0 -_URL0 = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz" -# version 1.1 -_URL1 = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.1.tar.gz" - -# version 1.0 -_LANGUAGES0 = ['af-ZA', 'am-ET', 'ar-SA', 'az-AZ', 'bn-BD', 'cy-GB', 'da-DK', 'de-DE', 'el-GR', 'en-US', - 'es-ES', 'fa-IR', 'fi-FI', 'fr-FR', 'he-IL', 'hi-IN', 'hu-HU', 'hy-AM', 'id-ID', 'is-IS', - 'it-IT', 'ja-JP', 'jv-ID', 'ka-GE', 'km-KH', 'kn-IN', 'ko-KR', 'lv-LV', 'ml-IN', 'mn-MN', - 'ms-MY', 'my-MM', 'nb-NO', 'nl-NL', 'pl-PL', 'pt-PT', 'ro-RO', 'ru-RU', 'sl-SL', 'sq-AL', - 'sv-SE', 'sw-KE', 'ta-IN', 'te-IN', 'th-TH', 'tl-PH', 'tr-TR', 'ur-PK', 'vi-VN', 'zh-CN', - 'zh-TW'] - -# version 1.1 ('ca-ES' added) -_LANGUAGES1 = sorted(_LANGUAGES0 + ['ca-ES']) - -_SCENARIOS = ['social', 'transport', 'calendar', 'play', 'news', 'datetime', 'recommendation', 'email', - 'iot', 'general', 'audio', 'lists', 'qa', 'cooking', 'takeaway', 'music', 'alarm', 'weather'] - -_INTENTS = ['datetime_query', 'iot_hue_lightchange', 'transport_ticket', 'takeaway_query', 'qa_stock', - 'general_greet', 'recommendation_events', 'music_dislikeness', 'iot_wemo_off', 'cooking_recipe', - 'qa_currency', 'transport_traffic', 'general_quirky', 'weather_query', 'audio_volume_up', - 'email_addcontact', 'takeaway_order', 'email_querycontact', 'iot_hue_lightup', - 'recommendation_locations', 'play_audiobook', 'lists_createoradd', 'news_query', - 'alarm_query', 'iot_wemo_on', 'general_joke', 'qa_definition', 'social_query', - 'music_settings', 'audio_volume_other', 'calendar_remove', 'iot_hue_lightdim', - 'calendar_query', 'email_sendemail', 'iot_cleaning', 'audio_volume_down', - 'play_radio', 'cooking_query', 'datetime_convert', 'qa_maths', 'iot_hue_lightoff', - 'iot_hue_lighton', 'transport_query', 'music_likeness', 'email_query', 'play_music', - 'audio_volume_mute', 'social_post', 'alarm_set', 'qa_factoid', 'calendar_set', - 'play_game', 'alarm_remove', 'lists_remove', 'transport_taxi', 'recommendation_movies', - 'iot_coffee', 'music_query', 'play_podcasts', 'lists_query'] - -_ALL = "all" - - -class MassiveConfig(datasets.BuilderConfig): - """BuilderConfig for MASSIVE.""" - - def __init__(self, dataset_version=None, *args, **kwargs): - """BuilderConfig for MASSIVE. - Args: - **kwargs: keyword arguments forwarded to super. - """ - super(MassiveConfig, self).__init__(*args, **kwargs) - self.dataset_version = dataset_version if dataset_version else "1.0" - self.data_url = _URL0 if self.dataset_version == "1.0" else _URL1 - - -class MASSIVE(datasets.GeneratorBasedBuilder): - """MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages""" - - # All individual locale datasets are served from the latest version. - BUILDER_CONFIGS = [ - MassiveConfig( - name = name, - dataset_version = '1.1', - version = datasets.Version("1.0.0"), - description = f"The MASSIVE v1.1 corpora for {name}", - ) for name in _LANGUAGES1 - ] - # Version 1.0 - BUILDER_CONFIGS.append(MassiveConfig( - name = _ALL, - dataset_version = '1.0', - version = datasets.Version("1.0.0"), - description = f"The MASSIVE v1.0 corpora for entire corpus", - ) - ) - # Version 1.1 - BUILDER_CONFIGS.append(MassiveConfig( - name = _ALL + '_1.1', # Append '_1.1' to 'all' in order to get the latest version. - dataset_version = '1.1', - version = datasets.Version("1.0.0"), - description = f"The MASSIVE v1.1 corpora for entire corpus", - ) - ) - - DEFAULT_CONFIG_NAME = _ALL - - def _info(self): - return datasets.DatasetInfo( - description=_DESCRIPTION, - features=datasets.Features( - { - "id": datasets.Value("string"), - "locale": datasets.Value("string"), - "partition": datasets.Value("string"), - "scenario": datasets.features.ClassLabel(names=_SCENARIOS), - "intent": datasets.features.ClassLabel(names=_INTENTS), - "utt": datasets.Value("string"), - "annot_utt": datasets.Value("string"), - "worker_id": datasets.Value("string"), - "slot_method": datasets.Sequence({ - "slot": datasets.Value("string"), - "method": datasets.Value("string"), - }), - "judgments": datasets.Sequence({ - "worker_id": datasets.Value("string"), - "intent_score": datasets.Value("int8"), - "slots_score": datasets.Value("int8"), - "grammar_score": datasets.Value("int8"), - "spelling_score": datasets.Value("int8"), - "language_identification": datasets.Value("string"), - }), - }, - ), - supervised_keys=None, - homepage="https://github.com/alexa/massive", - citation=_CITATION, - license=_LICENSE, - ) - - def _split_generators(self, dl_manager): - - archive = dl_manager.download(self.config.data_url) - return [ - datasets.SplitGenerator( - name=datasets.Split.TRAIN, - gen_kwargs={ - "files": dl_manager.iter_archive(archive), - "split": "train", - "lang": self.config.name, - } - ), - datasets.SplitGenerator( - name=datasets.Split.VALIDATION, - gen_kwargs={ - "files": dl_manager.iter_archive(archive), - "split": "dev", - "lang": self.config.name, - } - ), - datasets.SplitGenerator( - name=datasets.Split.TEST, - gen_kwargs={ - "files": dl_manager.iter_archive(archive), - "split": "test", - "lang": self.config.name, - } - ), - ] - - def _generate_examples(self, files, split, lang): - - key_ = 0 - - if lang == "all_1.1": - lang = _LANGUAGES1.copy() - elif lang == "all": - lang = _LANGUAGES0.copy() - else: - lang = [lang] - - logger.info("⏳ Generating examples from = %s", ", ".join(lang)) - - for path, f in files: - - l = path.split("/")[-1].split(".")[0] - - if not lang: - break - elif l in lang: - lang.remove(l) - else: - continue - - # Read the file - lines = f.read().decode(encoding="utf-8").split("\n") - - for line in lines: - - data = json.loads(line) - - if data["partition"] != split: - continue - - # Slot method - if "slot_method" in data: - slot_method = [ - { - "slot": s["slot"], - "method": s["method"], - } for s in data["slot_method"] - ] - else: - slot_method = [] - - # Judgments - if "judgments" in data: - judgments = [ - { - "worker_id": j["worker_id"], - "intent_score": j["intent_score"], - "slots_score": j["slots_score"], - "grammar_score": j["grammar_score"], - "spelling_score": j["spelling_score"], - "language_identification": j["language_identification"] if "language_identification" in j else "target", - } for j in data["judgments"] - ] - else: - judgments = [] - - yield key_, { - "id": data["id"], - "locale": data["locale"], - "partition": data["partition"], - "scenario": data["scenario"], - "intent": data["intent"], - "utt": data["utt"], - "annot_utt": data["annot_utt"], - "worker_id": data["worker_id"], - "slot_method": slot_method, - "judgments": judgments, - } - - key_ += 1 diff --git a/ml-IN/massive-test.parquet b/ml-IN/massive-test.parquet new file mode 100644 index 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