Update README.md

README.md

@@ -1,70 +1,70 @@
----
-
-language: en
-
-tags:
-
-- sentence correction
-
-- text2text-generation
-
-license: cc-by-nc-sa-4.0
-
-datasets:
-
-- jfleg
-
----
-
-# Model
-This model utilises T5-base sentence correction pre-trained model. It was fine tuned using a modified version of the [JFLEG](https://arxiv.org/abs/1702.04066) dataset and [Happy Transformer framework](https://github.com/EricFillion/happy-transformer). This model was pre-trained for educational purposes only for correction on local Caribbean dialect. For more on Caribbean dialect checkout the library [Caribe](https://pypi.org/project/Caribe/).
-.
-___
-
-
-# Re-training/Fine Tuning
-
-The results of fine-tuning resulted in a final accuracy of 90%
-
-
-# Usage 
-
-
-
-```python
-
-from happytransformer import HappyTextToText, TTSettings
-
-pre_trained_model="T5"
-model = HappyTextToText(pre_trained_model, "KES/T5-KES")
-
-arguments = TTSettings(num_beams=4, min_length=1)
-sentence = "Wat iz your nam"
-
-correction = model.generate_text("grammar: "+sentence, args=arguments)
-if(correction.text.find(" .")):
-    correction.text=correction.text.replace(" .", ".")
-
-print(correction.text) # Correction: "What is your name?".
-
-```
-_
-# Usage with Transformers
-
-```python
-
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-tokenizer = AutoTokenizer.from_pretrained("KES/T5-KES")
-
-model = AutoModelForSeq2SeqLM.from_pretrained("KES/T5-KES")
-
-text = "I am lived with my parenmts "
-inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt')
-
-output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
-correction=tokenizer.batch_decode(output, skip_special_tokens=True)
-print("".join(correction)) #Correction: I am living with my parents.
-
-```
-
+---
+
+language: en
+
+tags:
+
+- sentence correction
+
+- text2text-generation
+
+license: cc-by-nc-sa-4.0
+
+datasets:
+
+- jfleg
+
+---
+
+# Model
+This model utilises T5-base sentence correction pre-trained model. It was fine tuned using a modified version of the [JFLEG](https://arxiv.org/abs/1702.04066) dataset and [Happy Transformer framework](https://github.com/EricFillion/happy-transformer). This model was pre-trained for educational purposes only for correction on local Caribbean English Creole. For more on the Caribbean English Creole checkout the library [Caribe](https://pypi.org/project/Caribe/).
+.
+___
+
+
+# Re-training/Fine Tuning
+
+The results of fine-tuning resulted in a final accuracy of 90%
+
+
+# Usage 
+
+
+
+```python
+
+from happytransformer import HappyTextToText, TTSettings
+
+pre_trained_model="T5"
+model = HappyTextToText(pre_trained_model, "KES/T5-KES")
+
+arguments = TTSettings(num_beams=4, min_length=1)
+sentence = "Wat iz your nam"
+
+correction = model.generate_text("grammar: "+sentence, args=arguments)
+if(correction.text.find(" .")):
+    correction.text=correction.text.replace(" .", ".")
+
+print(correction.text) # Correction: "What is your name?".
+
+```
+_
+# Usage with Transformers
+
+```python
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("KES/T5-KES")
+
+model = AutoModelForSeq2SeqLM.from_pretrained("KES/T5-KES")
+
+text = "I am lived with my parenmts "
+inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt')
+
+output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
+correction=tokenizer.batch_decode(output, skip_special_tokens=True)
+print("".join(correction)) #Correction: I am living with my parents.
+
+```
+