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Loodos model cards had errors on "Usage" section. It is fixed. Also "electra-base-turkish-uncased" model removed from s3 and re-uploaded as "electra-base-turkish-uncased-discriminator". Its README added. (#6921)

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Co-authored-by: Abdullah Oluk <abdullaholuk123@gmail.com>
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abdullaholuk-loodos
2020-09-03 16:13:43 +03:00
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16
model_cards/loodos/albert-base-turkish-uncased/README.md
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@@ -4,11 +4,11 @@ language: tr
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here](https://github.com/Loodos/turkish-language-models)
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
# Turkish ALBERT-Base (uncased)
This is ALBERT-Base model which has 12 repeated encore layers with 768 hidden layer size trained on uncased Turkish dataset.
This is ALBERT-Base model which has 12 repeated encoder layers with 768 hidden layer size trained on uncased Turkish dataset.
## Usage
@@ -16,16 +16,19 @@ Using AutoModel and AutoTokenizer from Transformers, you can import the model as
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("loodos/albert-base-turkish-uncased", do_lower_case=False, keep_accents=True)
model = AutoModel.from_pretrained("loodos/albert-base-turkish-uncased")
normalizer = TextNormalization()
normalized_text = normalizer(text, do_lower_case=True)
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)
tokenizer.tokenize(normalized_text)
```
### Notes on Tokenizers
Currently, huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are 2 reasons.
Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.
1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.
@@ -38,11 +41,12 @@ respectively. However, in Turkish, 'I' and 'İ' are two different letters.
We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).
# Details and Contact
## Details and Contact
You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
# Acknowledgments
## Acknowledgments
Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.

15
model_cards/loodos/bert-base-turkish-uncased/README.md
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@@ -4,11 +4,11 @@ language: tr
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here](https://github.com/Loodos/turkish-language-models)
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
# Turkish BERT-Base (uncased)
This is BERT-Base model which has 12 encoder layer with 768 hidden layer size trained on uncased Turkish dataset.
This is BERT-Base model which has 12 encoder layers with 768 hidden layer size trained on uncased Turkish dataset.
## Usage
@@ -16,16 +16,19 @@ Using AutoModel and AutoTokenizer from Transformers, you can import the model as
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("loodos/bert-base-turkish-uncased", do_lower_case=False)
model = AutoModel.from_pretrained("loodos/bert-base-turkish-uncased")
normalizer = TextNormalization()
normalized_text = normalizer(text, do_lower_case=True)
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)
tokenizer.tokenize(normalized_text)
```
### Notes on Tokenizers
Currently, huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are 2 reasons.
Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.
1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.
@@ -39,11 +42,11 @@ respectively. However, in Turkish, 'I' and 'İ' are two different letters.
We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).
# Contact
## Details and Contact
You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
# Acknowledgments
## Acknowledgments
Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.

22
model_cards/loodos/electra-base-turkish-64k-uncased-discriminator/README.md
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@@ -4,28 +4,31 @@ language: tr
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here](https://github.com/Loodos/turkish-language-models)
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
# Turkish ELECTRA-Base-discriminator (uncased/64k)
This is ELECTRA-Base model's discriminator which has the same structure with BERT-Base trained on uncased Turkish dataset. This version has a vocab of size 64k different from default, 32k.
This is ELECTRA-Base model's discriminator which has the same structure with BERT-Base trained on uncased Turkish dataset. This version has a vocab of size 64k, different from default 32k.
## Usage
Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.
```python
from transformers import AutoModel, AutoTokenizer
from transformers import AutoModel, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("loodos/electra-base-turkish-64k-uncased-discriminator", do_lower_case=False)
model = AutoModel.from_pretrained("loodos/electra-base-turkish-64k-uncased-discriminator")
model = AutoModelWithLMHead.from_pretrained("loodos/electra-base-turkish-64k-uncased-discriminator")
normalizer = TextNormalization()
normalized_text = normalizer(text, do_lower_case=True)
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)
tokenizer.tokenize(normalized_text)
```
### Notes on Tokenizers
Currently, huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are 2 reasons.
Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.
1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.
@@ -38,11 +41,12 @@ respectively. However, in Turkish, 'I' and 'İ' are two different letters.
We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).
# Details and Contact
## Details and Contact
You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
# Acknowledgments
## Acknowledgments
Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.

23
model_cards/loodos/electra-base-turkish-uncased/README.md → model_cards/loodos/electra-base-turkish-uncased-discriminator/README.md
View File

@@ -4,28 +4,31 @@ language: tr
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here](https://github.com/Loodos/turkish-language-models)
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
# Turkish ELECTRA-Base (uncased)
# Turkish ELECTRA-Base-discriminator (uncased)
This is ELECTRA-Base model's discriminator which has the same structure with BERT-Base trained on uncased Turkish dataset.
## Usage
Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("loodos/electra-base-turkish-uncased", do_lower_case=False)
model = AutoModel.from_pretrained("loodos/electra-base-turkish-uncased")
from transformers import AutoModel, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("loodos/electra-base-turkish-uncased-discriminator", do_lower_case=False)
model = AutoModelWithLMHead.from_pretrained("loodos/electra-base-turkish-uncased-discriminator")
normalizer = TextNormalization()
normalized_text = normalizer(text, do_lower_case=True)
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)
tokenizer.tokenize(normalized_text)
```
### Notes on Tokenizers
Currently, huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are 2 reasons.
Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.
1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.
@@ -39,11 +42,11 @@ respectively. However, in Turkish, 'I' and 'İ' are two different letters.
We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).
# Details and Contact
## Details and Contact
You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
# Acknowledgments
## Acknowledgments
Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.

19
model_cards/loodos/electra-small-turkish-cased-discriminator/README.md
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@@ -4,24 +4,29 @@ language: tr
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here](https://github.com/Loodos/turkish-language-models)
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
# Turkish ELECTRA-Small-discriminator (cased)
This is ELECTRA-Small model's discriminator which has 12 encoder layers with 256 hidden layer size trained on cased Turkish dataset.
This is ELECTRA-Small model's discriminator which has 12 encoder layers with 256 hidden layers size trained on cased Turkish dataset.
## Usage
Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.
```python
from transformers import AutoModel, AutoTokenizer
from transformers import AutoModel, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("loodos/electra-small-turkish-cased-discriminator")
model = AutoModel.from_pretrained("loodos/electra-small-turkish-cased-discriminator")
model = AutoModelWithLMHead.from_pretrained("loodos/electra-small-turkish-cased-discriminator")
```
# Details and Contact
## Details and Contact
You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
# Acknowledgments
## Acknowledgments
Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.

21
model_cards/loodos/electra-small-turkish-uncased-discriminator/README.md
View File

@@ -4,28 +4,31 @@ language: tr
# Turkish Language Models with Huggingface's Transformers
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. The details about pretrained models and evaluations on downstream tasks can be found [here](https://github.com/Loodos/turkish-language-models)
As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
# Turkish ELECTRA-Small-discriminator (uncased)
This is ELECTRA-Small model's discriminator which has 12 encoder layers with 256 hidden layer size trained on uncased Turkish dataset. Please refer to
This is ELECTRA-Small model's discriminator which has 12 encoder layers with 256 hidden layer size trained on uncased Turkish dataset.
## Usage
Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
Using AutoModelWithLMHead and AutoTokenizer from Transformers, you can import the model as described below.
```python
from transformers import AutoModel, AutoTokenizer
from transformers import AutoModel, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("loodos/electra-small-turkish-uncased-discriminator", do_lower_case=False)
model = AutoModel.from_pretrained("loodos/electra-small-turkish-uncased-discriminator")
model = AutoModelWithLMHead.from_pretrained("loodos/electra-small-turkish-uncased-discriminator")
normalizer = TextNormalization()
normalized_text = normalizer(text, do_lower_case=True)
normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)
tokenizer.tokenize(normalized_text)
```
### Notes on Tokenizers
Currently, huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are 2 reasons.
Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.
1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.
@@ -39,11 +42,11 @@ respectively. However, in Turkish, 'I' and 'İ' are two different letters.
We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).
# Details and Contact
## Details and Contact
You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
# Acknowledgments
## Acknowledgments
Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.