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added multiple model_cards for below models (#6666)
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* added multiple codeswitch model
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sagorbrur authored Aug 24, 2020
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# codeswitch-hineng-lid-lince
This is a pretrained model for **language identification** of `hindi-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)


## Identify Language

* Method-1
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47 changes: 47 additions & 0 deletions model_cards/sagorsarker/codeswitch-hineng-ner-lince/README.md
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---
language:
- hi
- en
---

# codeswitch-hineng-ner-lince
This is a pretrained model for **Name Entity Recognition** of `Hindi-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)

This model is trained for this below repository.

[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)

To install codeswitch:

```
pip install codeswitch
```

## Identify Language

* Method-1

```py

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-hineng-ner-lince")

model = AutoModelForTokenClassification.from_pretrained("sagorsarker/codeswitch-hineng-ner-lince")

ner_model = pipeline('ner', model=model, tokenizer=tokenizer)

ner_model("put any hindi english code-mixed sentence")

```

* Method-2

```py
from codeswitch.codeswitch import NER
ner = NER('hin-eng')
text = "" # your mixed sentence
result = ner.tag(text)
print(result)

```
45 changes: 45 additions & 0 deletions model_cards/sagorsarker/codeswitch-hineng-pos-lince/README.md
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---
language:
- hi
- en
---

# codeswitch-hineng-pos-lince
This is a pretrained model for **Part of Speech Tagging** of `hindi-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)

This model is trained for this below repository.

[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)

To install codeswitch:

```
pip install codeswitch
```

## Identify Language

* Method-1

```py

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-hineng-pos-lince")

model = AutoModelForTokenClassification.from_pretrained("sagorsarker/codeswitch-hineng-pos-lince")
pos_model = pipeline('ner', model=model, tokenizer=tokenizer)

pos_model("put any hindi english code-mixed sentence")

```

* Method-2

```py
from codeswitch.codeswitch import POS
pos = POS('hin-eng')
text = "" # your mixed sentence
result = pos.tag(text)
print(result)
```
47 changes: 47 additions & 0 deletions model_cards/sagorsarker/codeswitch-nepeng-lid-lince/README.md
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---
language:
- ne
- en
---

# codeswitch-nepeng-lid-lince
This is a pretrained model for **language identification** of `nepali-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home).

This model is trained for this below repository.

[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)

To install codeswitch:

```
pip install codeswitch
```

## Identify Language

* Method-1

```py

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-nepeng-lid-lince")

model = AutoModelForTokenClassification.from_pretrained("sagorsarker/codeswitch-nepeng-lid-lince")
lid_model = pipeline('ner', model=model, tokenizer=tokenizer)

lid_model("put any nepali english code-mixed sentence")

```

* Method-2

```py
from codeswitch.codeswitch import LanguageIdentification
lid = LanguageIdentification('nep-eng')
text = "" # your code-mixed sentence
result = lid.identify(text)
print(result)

```

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# codeswitch-spaeng-lid-lince
This is a pretrained model for **language identification** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)


## Identify Language

* Method-1
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---
language:
- es
- en
---

# codeswitch-spaeng-ner-lince
This is a pretrained model for **Name Entity Recognition** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)

This model is trained for this below repository.

[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)

To install codeswitch:

```
pip install codeswitch
```

## Identify Language

* Method-1

```py

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-spaeng-ner-lince")

model = AutoModelForTokenClassification.from_pretrained("sagorsarker/codeswitch-spaeng-ner-lince")

ner_model = pipeline('ner', model=model, tokenizer=tokenizer)

ner_model("put any spanish english code-mixed sentence")

```

* Method-2

```py
from codeswitch.codeswitch import NER
ner = NER('spa-eng')
text = "" # your mixed sentence
result = ner.tag(text)
print(result)
```
45 changes: 45 additions & 0 deletions model_cards/sagorsarker/codeswitch-spaeng-pos-lince/README.md
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---
language:
- es
- en
---

# codeswitch-spaeng-pos-lince
This is a pretrained model for **Part of Speech Tagging** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)

This model is trained for this below repository.

[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)

To install codeswitch:

```
pip install codeswitch
```

## Identify Language

* Method-1

```py

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-spaeng-pos-lince")

model = AutoModelForTokenClassification.from_pretrained("sagorsarker/codeswitch-spaeng-pos-lince")
pos_model = pipeline('ner', model=model, tokenizer=tokenizer)

pos_model("put any spanish english code-mixed sentence")

```

* Method-2

```py
from codeswitch.codeswitch import POS
pos = POS('spa-eng')
text = "" # your mixed sentence
result = pos.tag(text)
print(result)
```

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