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Fix many typos (#8708)

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Santiago Castro
2020-11-22 00:58:10 -03:00
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parent 9c0afdaf7b
commit e1f3156b21
35 changed files with 51 additions and 51 deletions
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6
model_cards/KB/albert-base-swedish-cased-alpha/README.md
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@@ -4,7 +4,7 @@ language: sv
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aiming to provide a representative BERT model for Swedish text. A more complete description will be published later on.
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aiming to provide a representative BERT model for Swedish text. A more complete description will be published later on.
The following three models are currently available:
@@ -86,7 +86,7 @@ for token in nlp(text):
print(l)
```
Which should result in the following (though less cleanly formated):
Which should result in the following (though less cleanly formatted):
```python
[ { 'word': 'Engelbert', 'score': 0.99..., 'entity': 'PRS'},
@@ -104,7 +104,7 @@ Which should result in the following (though less cleanly formated):
### ALBERT base
The easisest way to do this is, again, using Huggingface Transformers:
The easiest way to do this is, again, using Huggingface Transformers:
```python
from transformers import AutoModel,AutoTokenizer

6
model_cards/KB/bert-base-swedish-cased-ner/README.md
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@@ -4,7 +4,7 @@ language: sv
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aiming to provide a representative BERT model for Swedish text. A more complete description will be published later on.
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aiming to provide a representative BERT model for Swedish text. A more complete description will be published later on.
The following three models are currently available:
@@ -86,7 +86,7 @@ for token in nlp(text):
print(l)
```
Which should result in the following (though less cleanly formated):
Which should result in the following (though less cleanly formatted):
```python
[ { 'word': 'Engelbert', 'score': 0.99..., 'entity': 'PRS'},
@@ -104,7 +104,7 @@ Which should result in the following (though less cleanly formated):
### ALBERT base
The easisest way to do this is, again, using Huggingface Transformers:
The easiest way to do this is, again, using Huggingface Transformers:
```python
from transformers import AutoModel,AutoTokenizer

2
model_cards/elgeish/cs224n-squad2.0-albert-base-v2/README.md
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@@ -4,7 +4,7 @@ tags:
---
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establising
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,

2
model_cards/elgeish/cs224n-squad2.0-albert-xxlarge-v1/README.md
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@@ -4,7 +4,7 @@ tags:
---
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establising
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,

2
model_cards/elgeish/cs224n-squad2.0-distilbert-base-uncased/README.md
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@@ -1,5 +1,5 @@
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establising
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,

2
model_cards/elgeish/cs224n-squad2.0-roberta-base/README.md
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@@ -1,5 +1,5 @@
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establising
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,

2
model_cards/mrm8488/t5-base-finetuned-e2m-intent/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the downstream task (Intent Prediction) - Dataset 📚
Dataset ID: ```event2Mind``` from [HugginFace/NLP](https://github.com/huggingface/nlp)
Dataset ID: ```event2Mind``` from [Huggingface/NLP](https://github.com/huggingface/nlp)
| Dataset | Split | # samples |
| -------- | ----- | --------- |

2
model_cards/mrm8488/t5-base-finetuned-question-generation-ap/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
Dataset ID: ```squad``` from [HugginFace/NLP](https://github.com/huggingface/nlp)
Dataset ID: ```squad``` from [Huggingface/NLP](https://github.com/huggingface/nlp)
| Dataset | Split | # samples |
| -------- | ----- | --------- |

2
model_cards/mrm8488/t5-base-finetuned-squadv2/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
Dataset ID: ```squad_v2``` from [HugginFace/NLP](https://github.com/huggingface/nlp)
Dataset ID: ```squad_v2``` from [Huggingface/NLP](https://github.com/huggingface/nlp)
| Dataset | Split | # samples |
| -------- | ----- | --------- |

2
model_cards/mrm8488/t5-base-finetuned-wikiSQL-sql-to-en/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the Dataset 📚
Dataset ID: ```wikisql``` from [HugginFace/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
Dataset ID: ```wikisql``` from [Huggingface/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
| Dataset | Split | # samples |
| -------- | ----- | --------- |

2
model_cards/mrm8488/t5-base-finetuned-wikiSQL/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the Dataset 📚
Dataset ID: ```wikisql``` from [HugginFace/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
Dataset ID: ```wikisql``` from [Huggingface/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
| Dataset | Split | # samples |
| -------- | ----- | --------- |

2
model_cards/mrm8488/t5-small-finetuned-quora-for-paraphrasing/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the downstream task (Question Paraphrasing) - Dataset 📚❓↔️❓
Dataset ID: ```quora``` from [HugginFace/NLP](https://github.com/huggingface/nlp)
Dataset ID: ```quora``` from [Huggingface/NLP](https://github.com/huggingface/nlp)
| Dataset | Split | # samples |
| -------- | ----- | --------- |

2
model_cards/mrm8488/t5-small-finetuned-squadv1/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
Dataset ID: ```squad``` from [HugginFace/NLP](https://github.com/huggingface/nlp)
Dataset ID: ```squad``` from [Huggingface/NLP](https://github.com/huggingface/nlp)
| Dataset | Split | # samples |
| -------- | ----- | --------- |

2
model_cards/mrm8488/t5-small-finetuned-squadv2/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
Dataset ID: ```squad_v2``` from [HugginFace/NLP](https://github.com/huggingface/nlp)
Dataset ID: ```squad_v2``` from [Huggingface/NLP](https://github.com/huggingface/nlp)
| Dataset | Split | # samples |
| -------- | ----- | --------- |

2
model_cards/mrm8488/t5-small-finetuned-wikiSQL/README.md
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@@ -19,7 +19,7 @@ Transfer learning, where a model is first pre-trained on a data-rich task before
## Details of the Dataset 📚
Dataset ID: ```wikisql``` from [HugginFace/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
Dataset ID: ```wikisql``` from [Huggingface/NLP](https://huggingface.co/nlp/viewer/?dataset=wikisql)
| Dataset | Split | # samples |
| -------- | ----- | --------- |