added model card for codeswitch-spaeng-sentiment-analysis-lince (#6727)

* added model card for codeswitch-spaeng-sentiment-analysis-lince model also update other model card

* fixed typo

* fixed typo

* fixed typo

* fixed typo

* fixed typo

* fixed typo

* fixed typo

* Update README.md

model_cards/sagorsarker/codeswitch-hineng-lid-lince/README.md

@@ -2,19 +2,35 @@
 language:
 - hi
 - en
+datasets:
+- LinCE
+license: "MIT"
+tags:
+- codeswitching
+- hindi-english
+- language-identification
 ---
 
 # 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)
 
+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
+* **Method-1**
 
 ```py
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-hineng-lid-lince")
 
@@ -25,10 +41,9 @@ lid_model("put any hindi english code-mixed sentence")
 
 ```
 
-* Method-2
+* **Method-2**
 
 ```py
-# !pip install codeswitch
 from codeswitch.codeswitch import LanguageIdentification
 lid = LanguageIdentification('hin-eng') 
 text = "" # your code-mixed sentence 

model_cards/sagorsarker/codeswitch-hineng-ner-lince/README.md

@@ -2,6 +2,13 @@
 language:
 - hi
 - en
+datasets:
+- LinCE
+license: "MIT"
+tags:
+- codeswitching
+- hindi-english
+- ner
 ---
 
 # codeswitch-hineng-ner-lince
@@ -17,13 +24,13 @@ To install codeswitch:
 pip install codeswitch
 ```
 
-## Identify Language
+## Name Entity Recognition of Code-Mixed Data
 
-* Method-1
+* **Method-1**
 
 ```py
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-hineng-ner-lince")
 
@@ -35,7 +42,7 @@ ner_model("put any hindi english code-mixed sentence")
 
 ```
 
-* Method-2
+* **Method-2**
 
 ```py
 from codeswitch.codeswitch import NER

model_cards/sagorsarker/codeswitch-hineng-pos-lince/README.md

@@ -2,6 +2,13 @@
 language:
 - hi
 - en
+datasets:
+- LinCE
+license: "MIT"
+tags:
+- codeswitching
+- hindi-english
+- pos
 ---
 
 # codeswitch-hineng-pos-lince
@@ -17,13 +24,13 @@ To install codeswitch:
 pip install codeswitch
 ```
 
-## Identify Language
+## Part-of-Speech Tagging of Hindi-English Mixed Data
 
-* Method-1
+* **Method-1**
 
 ```py
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-hineng-pos-lince")
 
@@ -34,7 +41,7 @@ pos_model("put any hindi english code-mixed sentence")
 
 ```
 
-* Method-2
+* **Method-2**
 
 ```py
 from codeswitch.codeswitch import POS

model_cards/sagorsarker/codeswitch-nepeng-lid-lince/README.md

@@ -2,6 +2,13 @@
 language:
 - ne
 - en
+datasets:
+- LinCE
+license: "MIT"
+tags:
+- codeswitching
+- nepali-english
+- language-identification
 ---
 
 # codeswitch-nepeng-lid-lince
@@ -19,11 +26,11 @@ pip install codeswitch
 
 ## Identify Language
 
-* Method-1
+* **Method-1**
 
 ```py
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-nepeng-lid-lince")
 
@@ -34,7 +41,7 @@ lid_model("put any nepali english code-mixed sentence")
 
 ```
 
-* Method-2
+* **Method-2**
 
 ```py
 from codeswitch.codeswitch import LanguageIdentification

model_cards/sagorsarker/codeswitch-spaeng-lid-lince/README.md

@@ -2,19 +2,35 @@
 language:
 - es
 - en
+datasets:
+- LinCE
+license: "MIT"
+tags:
+- codeswitching
+- spanish-english
+- language-identification
 ---
 
 # 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)
 
+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
+* **Method-1**
 
 ```py
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-spaeng-lid-lince")
 
@@ -25,10 +41,9 @@ lid_model("put any spanish english code-mixed sentence")
 
 ```
 
-* Method-2
+* **Method-2**
 
 ```py
-# !pip install codeswitch
 from codeswitch.codeswitch import LanguageIdentification
 lid = LanguageIdentification('spa-eng') 
 text = "" # your code-mixed sentence 

model_cards/sagorsarker/codeswitch-spaeng-ner-lince/README.md

@@ -2,6 +2,13 @@
 language:
 - es
 - en
+datasets:
+- LinCE
+license: "MIT"
+tags:
+- codeswitching
+- spanish-english
+- ner
 ---
 
 # codeswitch-spaeng-ner-lince
@@ -17,13 +24,13 @@ To install codeswitch:
 pip install codeswitch
 ```
 
-## Identify Language
+## Name Entity Recognition of Spanish-English Mixed Data
 
-* Method-1
+* **Method-1**
 
 ```py
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-spaeng-ner-lince")
 
@@ -35,7 +42,7 @@ ner_model("put any spanish english code-mixed sentence")
 
 ```
 
-* Method-2
+* **Method-2**
 
 ```py
 from codeswitch.codeswitch import NER

model_cards/sagorsarker/codeswitch-spaeng-pos-lince/README.md

@@ -2,6 +2,13 @@
 language:
 - es
 - en
+datasets:
+- LinCE
+license: "MIT"
+tags:
+- codeswitching
+- spanish-english
+- pos
 ---
 
 # codeswitch-spaeng-pos-lince
@@ -17,13 +24,13 @@ To install codeswitch:
 pip install codeswitch
 ```
 
-## Identify Language
+## Part-of-Speech Tagging of Spanish-English Mixed Data
 
-* Method-1
+* **Method-1**
 
 ```py
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-spaeng-pos-lince")
 
@@ -34,7 +41,7 @@ pos_model("put any spanish english code-mixed sentence")
 
 ```
 
-* Method-2
+* **Method-2**
 
 ```py
 from codeswitch.codeswitch import POS

model_cards/sagorsarker/codeswitch-spaeng-sentiment-analysis-lince/README.md

@@ -0,0 +1,53 @@
+---
+language:
+- es
+- en
+datasets:
+- LinCE
+license: "MIT"
+tags:
+- codeswitching
+- spanish-english
+- sentiment-analysis
+---
+
+# codeswitch-spaeng-sentiment-analysis-lince
+This is a pretrained model for **Sentiment Analysis** 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
+```
+
+## Sentiment Analysis of Spanish-English  Code-Mixed Data
+
+* **Method-1**
+
+```py
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-spaeng-sentiment-analysis-lince")
+
+model = AutoModelForSequenceClassification.from_pretrained("sagorsarker/codeswitch-spaeng-sentiment-analysis-lince")
+
+nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+sentence = "El perro le ladraba a La Gatita .. .. lol #teamlagatita en las playas de Key Biscayne este Memorial day"
+nlp(sentence)
+
+```
+
+* **Method-2**
+
+```py
+from codeswitch.codeswitch import SentimentAnalysis
+sa = SentimentAnalysis('spa-eng')
+sentence = "El perro le ladraba a La Gatita .. .. lol #teamlagatita en las playas de Key Biscayne este Memorial day"
+result = sa.analyze(sentence)
+print(result)
+```