참고자료:
엔데
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:wmt20_mlqe_task1/en-de')
- 설명 :
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource
English--German (En-De) and English--Chinese (En-Zh), medium-resource
Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource
Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a
dataset with a combination of Wikipedia articles and Reddit articles
for Russian-English (En-Ru). The datasets were collected by translating
sentences sampled from source language articles using state-of-the-art NMT
models built using the fairseq toolkit and annotated with Direct Assessment (DA)
scores by professional translators. Each sentence was annotated following the
FLORES setup, which presents a form of DA, where at least three professional
translators rate each sentence from 0-100 according to the perceived translation
quality. DA scores are standardised using the z-score by rater. Participating systems
are required to score sentences according to z-standardised DA scores.
- 라이센스 : 알 수 없음
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- 특징 :
{
"segid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"translation": {
"languages": [
"en",
"de"
],
"id": null,
"_type": "Translation"
},
"scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"z_scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"z_mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"model_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"doc_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"nmt_output": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_probas": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
엔즈
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:wmt20_mlqe_task1/en-zh')
- 설명 :
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource
English--German (En-De) and English--Chinese (En-Zh), medium-resource
Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource
Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a
dataset with a combination of Wikipedia articles and Reddit articles
for Russian-English (En-Ru). The datasets were collected by translating
sentences sampled from source language articles using state-of-the-art NMT
models built using the fairseq toolkit and annotated with Direct Assessment (DA)
scores by professional translators. Each sentence was annotated following the
FLORES setup, which presents a form of DA, where at least three professional
translators rate each sentence from 0-100 according to the perceived translation
quality. DA scores are standardised using the z-score by rater. Participating systems
are required to score sentences according to z-standardised DA scores.
- 라이센스 : 알 수 없음
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- 특징 :
{
"segid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"translation": {
"languages": [
"en",
"zh"
],
"id": null,
"_type": "Translation"
},
"scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"z_scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"z_mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"model_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"doc_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"nmt_output": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_probas": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
등등
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:wmt20_mlqe_task1/et-en')
- 설명 :
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource
English--German (En-De) and English--Chinese (En-Zh), medium-resource
Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource
Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a
dataset with a combination of Wikipedia articles and Reddit articles
for Russian-English (En-Ru). The datasets were collected by translating
sentences sampled from source language articles using state-of-the-art NMT
models built using the fairseq toolkit and annotated with Direct Assessment (DA)
scores by professional translators. Each sentence was annotated following the
FLORES setup, which presents a form of DA, where at least three professional
translators rate each sentence from 0-100 according to the perceived translation
quality. DA scores are standardised using the z-score by rater. Participating systems
are required to score sentences according to z-standardised DA scores.
- 라이센스 : 알 수 없음
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- 특징 :
{
"segid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"translation": {
"languages": [
"et",
"en"
],
"id": null,
"_type": "Translation"
},
"scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"z_scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"z_mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"model_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"doc_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"nmt_output": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_probas": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
네엔
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:wmt20_mlqe_task1/ne-en')
- 설명 :
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource
English--German (En-De) and English--Chinese (En-Zh), medium-resource
Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource
Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a
dataset with a combination of Wikipedia articles and Reddit articles
for Russian-English (En-Ru). The datasets were collected by translating
sentences sampled from source language articles using state-of-the-art NMT
models built using the fairseq toolkit and annotated with Direct Assessment (DA)
scores by professional translators. Each sentence was annotated following the
FLORES setup, which presents a form of DA, where at least three professional
translators rate each sentence from 0-100 according to the perceived translation
quality. DA scores are standardised using the z-score by rater. Participating systems
are required to score sentences according to z-standardised DA scores.
- 라이센스 : 알 수 없음
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- 특징 :
{
"segid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"translation": {
"languages": [
"ne",
"en"
],
"id": null,
"_type": "Translation"
},
"scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"z_scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"z_mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"model_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"doc_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"nmt_output": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_probas": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
로엔
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:wmt20_mlqe_task1/ro-en')
- 설명 :
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource
English--German (En-De) and English--Chinese (En-Zh), medium-resource
Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource
Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a
dataset with a combination of Wikipedia articles and Reddit articles
for Russian-English (En-Ru). The datasets were collected by translating
sentences sampled from source language articles using state-of-the-art NMT
models built using the fairseq toolkit and annotated with Direct Assessment (DA)
scores by professional translators. Each sentence was annotated following the
FLORES setup, which presents a form of DA, where at least three professional
translators rate each sentence from 0-100 according to the perceived translation
quality. DA scores are standardised using the z-score by rater. Participating systems
are required to score sentences according to z-standardised DA scores.
- 라이센스 : 알 수 없음
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- 특징 :
{
"segid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"translation": {
"languages": [
"ro",
"en"
],
"id": null,
"_type": "Translation"
},
"scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"z_scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"z_mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"model_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"doc_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"nmt_output": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_probas": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
시엔
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:wmt20_mlqe_task1/si-en')
- 설명 :
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource
English--German (En-De) and English--Chinese (En-Zh), medium-resource
Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource
Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a
dataset with a combination of Wikipedia articles and Reddit articles
for Russian-English (En-Ru). The datasets were collected by translating
sentences sampled from source language articles using state-of-the-art NMT
models built using the fairseq toolkit and annotated with Direct Assessment (DA)
scores by professional translators. Each sentence was annotated following the
FLORES setup, which presents a form of DA, where at least three professional
translators rate each sentence from 0-100 according to the perceived translation
quality. DA scores are standardised using the z-score by rater. Participating systems
are required to score sentences according to z-standardised DA scores.
- 라이센스 : 알 수 없음
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- 특징 :
{
"segid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"translation": {
"languages": [
"si",
"en"
],
"id": null,
"_type": "Translation"
},
"scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"z_scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"z_mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"model_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"doc_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"nmt_output": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_probas": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
루엔
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:wmt20_mlqe_task1/ru-en')
- 설명 :
This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced this year include: a new
task where sentences are annotated with Direct Assessment (DA)
scores instead of labels based on post-editing; a new multilingual
sentence-level dataset mainly from Wikipedia articles, where the
source articles can be retrieved for document-wide context; the
availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource
English--German (En-De) and English--Chinese (En-Zh), medium-resource
Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource
Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a
dataset with a combination of Wikipedia articles and Reddit articles
for Russian-English (En-Ru). The datasets were collected by translating
sentences sampled from source language articles using state-of-the-art NMT
models built using the fairseq toolkit and annotated with Direct Assessment (DA)
scores by professional translators. Each sentence was annotated following the
FLORES setup, which presents a form of DA, where at least three professional
translators rate each sentence from 0-100 according to the perceived translation
quality. DA scores are standardised using the z-score by rater. Participating systems
are required to score sentences according to z-standardised DA scores.
- 라이센스 : 알 수 없음
- 버전 : 1.1.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- 특징 :
{
"segid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"translation": {
"languages": [
"ru",
"en"
],
"id": null,
"_type": "Translation"
},
"scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"z_scores": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"z_mean": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"model_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"doc_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"nmt_output": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_probas": {
"feature": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}