Update README.md

README.md

@@ -1,46 +1,46 @@
----
-license: apache-2.0
-language: en
-tags:
-- hate
-- speech
-
-widget:
-- text: "RT @ShenikaRoberts: The shit you hear about me might be true or it might be faker than the bitch who told it to ya &#5736"
-
----
-
-# Dataset Collection:
-* The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.
-* The dataset has the two classes hatespeech and non hatespeech.
-* The class distribution is equal
-* Different strategies have been followed during the data gathering phase.
-* The dataset is collected from relevant sources.
-
-# distilbert-base-uncased model is fine-tuned for Hate Speech Detection
-* The model is fine-tuned on the dataset.
-* This model can be used to create the labels for academic purposes or for industrial purposes.
-* This model can be used for the inference purpose as well.
-
-# Data Fields:
- 
-**label**: 0 - it is a hate speech, 1 - not a hate speech
-
-# Application:
-* This model is useful for the detection of hatespeech in the tweets.
-* There are numerous situations where we have tweet data but no labels, so this approach can be used to create labels.
-* You can fine-tune this model for your particular use cases.
-
-# Model Implementation
-
-# !pip install transformers[sentencepiece]
-
-from transformers import pipeline
-
-model_name="Sakil/distilbert_lazylearner_hatespeech_detection"
-
-classifier = pipeline("text-classification",model=model_name)
-
-classifier("!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...")
-
-# Github: [Sakil Ansari](https://github.com/Sakil786/hate_speech_detection_pretrained_model)
+---
+license: apache-2.0
+language: en
+tags:
+- hate
+- speech
+
+widget:
+- text: "RT @ShenikaRoberts: The shit you hear about me might be true or it might be faker than the bitch who told it to ya &#5736"
+
+---
+
+# Dataset Collection:
+* The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.
+* The dataset has the two classes hatespeech and non hatespeech.
+* The class distribution is equal
+* Different strategies have been followed during the data gathering phase.
+* The dataset is collected from relevant sources.
+
+# distilbert-base-uncased model is fine-tuned for Hate Speech Detection
+* The model is fine-tuned on the dataset.
+* This model can be used to create the labels for academic purposes or for industrial purposes.
+* This model can be used for the inference purpose as well.
+
+# Data Fields:
+ 
+**label**: 0 - it is a hate speech, 1 - not a hate speech
+
+# Application:
+* This model is useful for the detection of hatespeech in the tweets.
+* There are numerous situations where we have tweet data but no labels, so this approach can be used to create labels.
+* You can fine-tune this model for your particular use cases.
+
+# Model Implementation
+
+# !pip install transformers[sentencepiece]
+
+from transformers import pipeline
+
+model_name="Sakil/distilbert_lazylearner_hatespeech_detection"
+
+classifier = pipeline("text-classification",model=model_name)
+
+classifier("!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...")
+
+# Github: [Sakil Ansari](https://github.com/Sakil786/sentence_similarity_semantic_search)