Update README.md

README.md

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+# Model Card for BERT hate offensive tweets
+
+BERT base uncased trained on the data that can be found here: MartynaKopyta/hate_offensive_tweets (https://huggingface.co/datasets/MartynaKopyta/hate_offensive_tweets) to classify tweets as 0 - hate, 1 - offensive or 2 - neither.
+
+You can find the notebook used for training in my GitHub repo: MartynaKopyta/BERT_FINE-TUNING (https://github.com/MartynaKopyta/BERT_FINE-TUNING/blob/main/BERT_hate_offensive_speech.ipynb).
+
+## Model Details
+
+- **Finetuned from model bert-base-uncased:https://huggingface.co/bert-base-uncased** 
+
+
+## Bias, Risks, and Limitations
+
+The dataset was not big enough for BERT to learn to classify 3 classes accurately, it is right 3/4 times.
+
+
+## How to Get Started with the Model
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained('MartynaKopyta/BERT_hate_offensive_tweets')
+tokenizer = AutoTokenizer.from_pretrained('MartynaKopyta/BERT_hate_offensive_tweets')
+
+
+#### Training Hyperparameters
+
+- **batch size:16**
+- **learning rate:2e-5**
+- **epochs:3**
+
+## Evaluation
+
+Accuracy: 0.779373368146214
+Classification Report:
+              precision    recall  f1-score   support
+
+           0       0.74      0.68      0.71      1532
+           1       0.85      0.88      0.87      1532
+           2       0.74      0.78      0.76      1532
+
+    accuracy                           0.78      4596
+   macro avg       0.78      0.78      0.78      4596
+weighted avg       0.78      0.78      0.78      4596
+
+Confusion Matrix:
+[[1043   96  393]
+ [ 169 1343   20]
+ [ 204  132 1196]]
+
+MCC: 0.670