Update README.md

README.md

@@ -19,31 +19,57 @@ widget:
 license: apache-2.0
 ---
 
-# bert-multilingual-go-emtions
+# Multilingual (English and Chinese) GoEmotions Classification Model
 
-This a bert-base-multilingual-cased model finetuned for sentiment analysis in two languages: English, Simplified Chinese.
+This repository hosts a fine-tuned BERT model for cross-language emotion classification on the GoEmotions dataset. This model is unique as it has been trained on a multilingual dataset comprising of English and Chinese texts. It is capable of classifying text into one of 28 different emotion categories.
 
-This cross-language model is intended for direct use as a sentiment analysis model for text in any of the two languages above, or for further finetuning on related sentiment analysis tasks.
+The 28 emotion categories, according to the GoEmotions taxonomy, are: 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', and 'neutral'.
+
+# Model Performance
+The model demonstrates high performance on the validation set, with the following scores:
+
+Accuracy: 85.95%
+Precision: 91.99%
+Recall: 89.56%
+F1 Score: 90.17%
+These results indicate the model's high accuracy and precision in predicting the correct emotion category for a given input text, regardless of the language (English or Chinese).
 
 ## Training data
 
-Here is the number of product reviews we used for finetuning the model: 
+The dataset used for training the model is a combined dataset of the original English GoEmotions dataset and a machine translated Chinese version of the GoEmotions dataset.
+
+The dataset is split into two parts:
+
+- **Labeled data**: Used for initial training. It includes both English and machine translated Chinese samples. This labeled data is further split into a training set (80%) and a validation set (20%).
+- **Unlabeled data**: Used for making predictions and adding confidently predicted samples to the training data. It includes both English and machine translated Chinese samples.
+
+## Training
+
+The model is trained in two stages:
+
+1. Initial training on the labeled data.
+2. Predictions are made on the unlabeled data, and the most confidently predicted samples are added to the training data. The model is then retrained on this updated labeled data.
+
+The model is trained for a total of 20 epochs (10 epochs for each stage). Precision, recall, and F1 score are logged during training.
+
+## Usage
 
-| Language | Number of reviews |
-| -------- | ----------------- |
-| English  |           |
-| Chinese(Machine Translate)    |             |
+Here is a code snippet showing how to use this model:
 
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
 
-## Accuracy
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("SchuylerH/bert-multilingual-go-emtions")
+model = AutoModelForSequenceClassification.from_pretrained("SchuylerH/bert-multilingual-go-emtions")
 
-The finetuned model obtained the following accuracy on - in each of the languages:
+text = "I love you."
+nlp = pipeline("sentiment-analysis", model = model, tokenizer = tokenizer)
 
-| Language | Accuracy 
-| -------- | ---------------------- |
-| English  | -%                 | 
-| Chinese    | -%                 |
+result = nlp(text)
 
+print(result)
 
 ## Contact