loodos turkish model cards added (#6840)

model_cards/loodos/albert-base-turkish-uncased/README.md

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+---
+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)
+
+# 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.
+
+## Usage
+
+Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
+
+```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)
+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.
+
+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.
+
+2- Python's default ```string.lower()``` and ```string.upper()``` make the conversions
+
+- "I" and "İ" to 'i'
+- 'i' and 'ı' to 'I'
+
+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
+
+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
+
+Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
+

model_cards/loodos/bert-base-turkish-uncased/README.md

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+---
+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)
+
+# Turkish BERT-Base (uncased)
+
+This is BERT-Base model which has 12 encoder layer with 768 hidden layer size trained on uncased Turkish dataset.
+
+## Usage
+
+Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
+
+```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)
+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.
+
+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.
+
+2- Python's default ```string.lower()``` and ```string.upper()``` make the conversions
+
+- "I" and "İ" to 'i'
+- 'i' and 'ı' to 'I'
+
+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
+
+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
+
+Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
+

model_cards/loodos/electra-base-turkish-64k-uncased-discriminator/README.md

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+---
+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)
+
+# 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.
+
+## Usage
+
+Using AutoModel 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-64k-uncased-discriminator", do_lower_case=False)
+model = AutoModel.from_pretrained("loodos/electra-base-turkish-64k-uncased-discriminator")
+ 
+normalizer = TextNormalization()
+normalized_text = normalizer(text, do_lower_case=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.
+
+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.
+
+2- Python's default ```string.lower()``` and ```string.upper()``` make the conversions
+
+- "I" and "İ" to 'i'
+- 'i' and 'ı' to 'I'
+
+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
+
+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
+
+Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
+

model_cards/loodos/electra-base-turkish-uncased/README.md

@@ -0,0 +1,49 @@
+---
+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)
+
+# Turkish ELECTRA-Base (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.
+
+```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")
+ 
+normalizer = TextNormalization()
+normalized_text = normalizer(text, do_lower_case=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.
+
+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.
+
+2- Python's default ```string.lower()``` and ```string.upper()``` make the conversions
+
+- "I" and "İ" to 'i'
+- 'i' and 'ı' to 'I'
+
+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
+
+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
+
+Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
+

model_cards/loodos/electra-small-turkish-cased-discriminator/README.md

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+---
+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)
+
+# 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.
+
+## Usage
+
+Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
+
+```python
+from transformers import AutoModel, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("loodos/electra-small-turkish-cased-discriminator")
+model = AutoModel.from_pretrained("loodos/electra-small-turkish-cased-discriminator")
+```
+
+# 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

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

@@ -0,0 +1,49 @@
+---
+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)
+
+# 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 
+
+## Usage
+
+Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
+
+```python
+from transformers import AutoModel, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("loodos/electra-small-turkish-uncased-discriminator", do_lower_case=False)
+model = AutoModel.from_pretrained("loodos/electra-small-turkish-uncased-discriminator")
+ 
+normalizer = TextNormalization()
+normalized_text = normalizer(text, do_lower_case=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.
+
+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.
+
+2- Python's default ```string.lower()``` and ```string.upper()``` make the conversions
+
+- "I" and "İ" to 'i'
+- 'i' and 'ı' to 'I'
+
+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
+
+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
+
+Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
+