Update README.md

README.md

@@ -16,7 +16,8 @@ Model Description
 This model is based on the BERT (Bidirectional Encoder Representations from Transformers) model, specifically bert-base-uncased.
 
 Training Procedure
-The model was trained on the TripAdvisor hotel reviews dataset. Each review in the dataset is associated with a rating from 1 to 5. The ratings were converted to sentiment labels as follows:
+The model was trained on the TripAdvisor hotel reviews dataset. Each review in the dataset is associated with a rating from 1 to 5. 
+The ratings were converted to sentiment labels as follows:
 
 Ratings of 1 and 2 were labelled as 'Negative'
 Rating of 3 was labelled as 'Neutral'
@@ -26,17 +27,16 @@ The text of each review was preprocessed by lowercasing, removing punctuation, e
 The model was trained with a learning rate of 2e-5, an epsilon of 1e-8, and a batch size of 6 for 5 epochs.
 
 Evaluation
-The model was evaluated using a weighted F1 score. The specific performance metrics obtained during evaluation will be updated here.
+The model was evaluated using a weighted F1 score. 
 
 Usage
 To use the model, load it and use it to classify a review. For example:
 
-python
-Copy code
+
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-tokenizer = AutoTokenizer.from_pretrained("<model-name>")
-model = AutoModelForSequenceClassification.from_pretrained("<model-name>")
+tokenizer = AutoTokenizer.from_pretrained("<Group209>")
+model = AutoModelForSequenceClassification.from_pretrained("<Group209>")
 
 text = "The hotel was great and the staff were very friendly."
 
@@ -45,10 +45,7 @@ output = model(**encoded_input)
 predictions = output.logits.argmax(dim=1)
 
 print(predictions)
-Replace <model-name> with the actual model name.
 
 Limitations and Bias
-The model is trained on English data, so it might not perform well on reviews in other languages. Furthermore, it might be biased towards certain phrases or words that are commonly used in the training dataset.
-
-Licensing
-Please add licensing information here if applicable.
+The model is trained on English data, so it might not perform well on reviews in other languages. 
+Furthermore, it might be biased towards certain phrases or words that are commonly used in the training dataset.