Create README.md

README.md

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+---
+language: en
+datasets:
+- tweets_hate_speech_detection
+---
+
+
+# ByT5-base fine-tuned for Hate Speech Detection (on Tweets)
+[ByT5](https://huggingface.co/google/byt5-base) base fine-tuned on [tweets hate speech detection](https://huggingface.co/datasets/tweets_hate_speech_detection) dataset for **Sequence Classification** downstream task.
+
+# Details of ByT5 - Base
+
+ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-base).
+ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
+ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-base` significantly outperforms [mt5-base](https://huggingface.co/google/mt5-base) on [TweetQA](https://arxiv.org/abs/1907.06292).
+Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/pdf/1910.10683.pdf)
+Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel* 
+
+
+## Details of the downstream task (Sequence Classification as Text generation) - Dataset 📚
+
+[tweets_hate_speech_detection](hhttps://huggingface.co/datasets/tweets_hate_speech_detection)
+
+
+The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.
+
+Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.
+
+- Data Instances:
+
+The dataset contains a label denoting is the tweet a hate speech or not
+
+```json
+{'label': 0,  # not a hate speech
+ 'tweet': ' @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run'}
+```
+- Data Fields:
+ 
+**label**: 1 - it is a hate speech, 0 - not a hate speech
+
+**tweet**: content of the tweet as a string
+
+- Data Splits:
+
+The data contains training data with **31962** entries
+
+## Test set metrics 🧾
+
+We created a representative test set with the 5% of the entries.
+
+The dataset is so imbalanced and we got a **F1 score of 79.8**
+    
+
+
+## Model in Action 🚀
+
+```sh
+git clone https://github.com/huggingface/transformers.git
+pip install -q transformers
+```
+
+```python
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+tokenizer = AutoTokenizer.from_pretrained("Narrativa/byt5-base-tweet-hate-detection")
+
+model = T5ForConditionalGeneration.from_pretrained("Narrativa/byt5-base-tweet-hate-detection").to("cuda")
+
+def classify_tweet(tweet):
+
+    inputs = tokenizer([tweet], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
+    input_ids = inputs.input_ids.to("cuda")
+    attention_mask = inputs.attention_mask.to("cuda")
+
+    output = model.generate(input_ids, attention_mask=attention_mask)
+
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    
+classify_tweet('here goes your tweet...')
+```
+
+> Created by [Narrativa](https://www.narrativa.com/)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain