Fix doc errors and typos across the board (#8139)
* Fix doc errors and typos across the board * Fix a typo * Fix the CI * Fix more typos * Fix CI * More fixes * Fix CI * More fixes * More fixes
This commit is contained in:
Santiago Castro
@@ -7,7 +7,7 @@ language: ar
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**AraBERT** is an Arabic pretrained lanaguage model based on [Google's BERT architechture](https://github.com/google-research/bert). AraBERT uses the same BERT-Base config. More details are available in the [AraBERT PAPER](https://arxiv.org/abs/2003.00104v2) and in the [AraBERT Meetup](https://github.com/WissamAntoun/pydata_khobar_meetup)
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There are two version off the model AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the [Farasa Segmenter](http://alt.qcri.org/farasa/segmenter.html).
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There are two version off the model AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the [Farasa Segmenter](http://alt.qcri.org/farasa/segmenter.html).
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The model was trained on ~70M sentences or ~23GB of Arabic text with ~3B words. The training corpora are a collection of publically available large scale raw arabic text ([Arabic Wikidumps](https://archive.org/details/arwiki-20190201), [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4), [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619), Assafir news articles, and 4 other manually crawled news websites (Al-Akhbar, Annahar, AL-Ahram, AL-Wafd) from [the Wayback Machine](http://web.archive.org/))
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@@ -7,7 +7,7 @@ language: ar
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**AraBERT** is an Arabic pretrained lanaguage model based on [Google's BERT architechture](https://github.com/google-research/bert). AraBERT uses the same BERT-Base config. More details are available in the [AraBERT PAPER](https://arxiv.org/abs/2003.00104v2) and in the [AraBERT Meetup](https://github.com/WissamAntoun/pydata_khobar_meetup)
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There are two version off the model AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the [Farasa Segmenter](http://alt.qcri.org/farasa/segmenter.html).
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There are two version off the model AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the [Farasa Segmenter](http://alt.qcri.org/farasa/segmenter.html).
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The model was trained on ~70M sentences or ~23GB of Arabic text with ~3B words. The training corpora are a collection of publically available large scale raw arabic text ([Arabic Wikidumps](https://archive.org/details/arwiki-20190201), [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4), [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619), Assafir news articles, and 4 other manually crawled news websites (Al-Akhbar, Annahar, AL-Ahram, AL-Wafd) from [the Wayback Machine](http://web.archive.org/))
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@@ -4,7 +4,7 @@ tags:
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---
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## CS224n SQuAD2.0 Project Dataset
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The goal of this model is to save CS224n students GPU time when establising
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The goal of this model is to save CS224n students GPU time when establishing
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baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
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The training set used to fine-tune this model is the same as
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the [official one](https://rajpurkar.github.io/SQuAD-explorer/); however,
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@@ -34,7 +34,7 @@ model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_afr_roberta")
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#### Limitations and bias
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Updates will be added continously to improve performance.
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Updates will be added continuously to improve performance.
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## Training data
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@@ -94,7 +94,7 @@ fill_mask(PYTHON_CODE3)
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> Great! 🎉
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## This work is heavely inspired on [CodeBERTa](https://github.com/huggingface/transformers/blob/master/model_cards/huggingface/CodeBERTa-small-v1/README.md) by huggingface team
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## This work is heavily inspired on [CodeBERTa](https://github.com/huggingface/transformers/blob/master/model_cards/huggingface/CodeBERTa-small-v1/README.md) by huggingface team
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<br>
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@@ -11,7 +11,7 @@ This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corp
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- [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora)
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I preprocessed the dataset and splitted it as train / dev (80/20)
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I preprocessed the dataset and split it as train / dev (80/20)
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| Dataset | # Examples |
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| ---------------------- | ----- |
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@@ -65,7 +65,7 @@ Citation:
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</details>
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As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques` (scraping, neural machine translation, etc) to obtain more samples and splited the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
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As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques` (scraping, neural machine translation, etc) to obtain more samples and split the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
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| Dataset | # samples |
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| ----------- | --------- |
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@@ -65,7 +65,7 @@ Citation:
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</details>
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As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques` (scraping, neural machine translation, etc) to obtain more samples and splited the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
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As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques` (scraping, neural machine translation, etc) to obtain more samples and split the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
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| Dataset | # samples |
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| ----------- | --------- |
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@@ -11,7 +11,7 @@ This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corp
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- [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora)
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I preprocessed the dataset and splitted it as train / dev (80/20)
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I preprocessed the dataset and split it as train / dev (80/20)
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| Dataset | # Examples |
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| ---------------------- | ----- |
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@@ -11,7 +11,7 @@ This model is a fine-tuned on Spanish [CONLL CORPORA](https://www.kaggle.com/nlt
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- [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora) with data augmentation techniques
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I preprocessed the dataset and splitted it as train / dev (80/20)
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I preprocessed the dataset and split it as train / dev (80/20)
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| Dataset | # Examples |
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| ---------------------- | ----- |
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@@ -44,7 +44,7 @@ python transformers/examples/question-answering/run_squad.py \
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--save_steps 1000
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```
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It is importatnt to say that this models converges much faster than other ones. So, it is also cheap to fine-tune.
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It is important to say that this models converges much faster than other ones. So, it is also cheap to fine-tune.
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## Test set Results 🧾
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@@ -44,7 +44,7 @@ python transformers/examples/question-answering/run_squad.py \
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--version_2_with_negative
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```
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It is importatnt to say that this models converges much faster than other ones. So, it is also cheap to fine-tune.
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It is important to say that this models converges much faster than other ones. So, it is also cheap to fine-tune.
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## Test set Results 🧾
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@@ -48,7 +48,7 @@ python code/run_squad.py \
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| SpanBERT (large) | [94.6](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv1) | [88.7](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv2) | 79.6 | [70.8](https://huggingface.co/mrm8488/spanbert-large-finetuned-tacred) |
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Note: The numbers marked as * are evaluated on the development sets becaus those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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Note: The numbers marked as * are evaluated on the development sets because those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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## Model in action
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@@ -54,7 +54,7 @@ python code/run_squad.py \
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| SpanBERT (large) | [94.6](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv1) | [88.7](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv2) | 79.6 | [70.8](https://huggingface.co/mrm8488/spanbert-large-finetuned-tacred) |
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Note: The numbers marked as * are evaluated on the development sets becaus those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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Note: The numbers marked as * are evaluated on the development sets because those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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## Model in action
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@@ -45,7 +45,7 @@ python code/run_tacred.py \
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| SpanBERT (large) | [94.6](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv1) | [88.7](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv2) | 79.6 | [70.8](https://huggingface.co/mrm8488/spanbert-base-finetuned-tacred) |
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Note: The numbers marked as * are evaluated on the development sets becaus those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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Note: The numbers marked as * are evaluated on the development sets because those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
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@@ -48,7 +48,7 @@ python code/run_squad.py \
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| SpanBERT (large) | **94.6** (this) | [88.7](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv2) | 79.6 | [70.8](https://huggingface.co/mrm8488/spanbert-large-finetuned-tacred) |
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Note: The numbers marked as * are evaluated on the development sets becaus those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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Note: The numbers marked as * are evaluated on the development sets because those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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## Model in action
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@@ -54,7 +54,7 @@ python code/run_squad.py \
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| SpanBERT (large) | [94.6](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv1) | **88.7** (this) | 79.6 | [70.8](https://huggingface.co/mrm8488/spanbert-large-finetuned-tacred) |
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Note: The numbers marked as * are evaluated on the development sets becaus those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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Note: The numbers marked as * are evaluated on the development sets because those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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## Model in action
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@@ -45,7 +45,7 @@ python code/run_tacred.py \
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| SpanBERT (large) | [94.6](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv1) | [88.7](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv2) | 79.6 | **70.8** (this one) |
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Note: The numbers marked as * are evaluated on the development sets becaus those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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Note: The numbers marked as * are evaluated on the development sets because those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
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@@ -50,7 +50,7 @@ tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL-sql
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model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL-sql-to-en")
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def get_explanation(query):
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input_text = "translante Sql to English: %s </s>" % query
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input_text = "translate Sql to English: %s </s>" % query
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features = tokenizer([input_text], return_tensors='pt')
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output = model.generate(input_ids=features['input_ids'],
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@@ -50,7 +50,7 @@ tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
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model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
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def get_sql(query):
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input_text = "translante English to SQL: %s </s>" % query
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input_text = "translate English to SQL: %s </s>" % query
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features = tokenizer([input_text], return_tensors='pt')
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output = model.generate(input_ids=features['input_ids'],
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@@ -50,7 +50,7 @@ tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-wikiSQL")
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model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-small-finetuned-wikiSQL")
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def get_sql(query):
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input_text = "translante English to SQL: %s </s>" % query
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input_text = "translate English to SQL: %s </s>" % query
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features = tokenizer([input_text], return_tensors='pt')
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output = model.generate(input_ids=features['input_ids'],
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@@ -71,7 +71,7 @@ Citation:
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</details>
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As XQuAD is just an evaluation dataset, I used Data augmentation techniques (scraping, neural machine translation, etc) to obtain more samples and splited the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
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As XQuAD is just an evaluation dataset, I used Data augmentation techniques (scraping, neural machine translation, etc) to obtain more samples and split the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
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| Dataset | # samples |
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| ----------- | --------- |
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