Update README.md

README.md

@@ -7,6 +7,11 @@ tags:
 model-index:
 - name: sentiment-thai-text-model
   results: []
+datasets:
+- pythainlp/wisesight_sentiment
+language:
+- th
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -18,18 +23,42 @@ This model is a fine-tuned version of [poom-sci/WangchanBERTa-finetuned-sentimen
 
 ## Model description
 
-More information needed
+This model is a fine-tuned version of poom-sci/WangchanBERTa-finetuned-sentiment, specifically tailored for sentiment analysis on Thai-language texts. The fine-tuning was performed to improve performance on a custom Thai dataset for sentiment classification. The model is based on WangchanBERTa, a powerful transformer-based language model developed for Thai by the National Electronics and Computer Technology Center (NECTEC) in Thailand.
 
 ## Intended uses & limitations
 
-More information needed
+This model is designed to perform sentiment analysis, categorizing input text into three classes: positive, neutral, and negative. It can be used in a variety of natural language processing (NLP) applications such as:
+
+Social media sentiment analysis
+Product or service reviews sentiment classification
+Customer feedback processing
+
+Limitations:
+Language: The model is specialized for Thai text and may not perform well with other languages.
+Generalization: The model's performance depends on the quality and diversity of the dataset used for fine-tuning. It may not generalize well to domains that differ significantly from the training data.
+Ambiguity: Handling of highly ambiguous or sarcastic sentences may still be challenging.
 
 ## Training and evaluation data
 
-More information needed
+The model was fine-tuned on a sentiment classification dataset composed of Thai-language text. The dataset includes sentences and texts from multiple domains, such as social media, product reviews, and general user feedback, labeled into three categories:
+
+Positive: Indicates that the text expresses positive sentiment.
+Neutral: Indicates that the text is neutral or objective in sentiment.
+Negative: Indicates that the text expresses negative sentiment.
+More details on the dataset used can be provided upon request.
 
 ## Training procedure
 
+The model was trained using the following hyperparameters:
+
+Learning rate: 2e-05
+Batch size: 32 for both training and evaluation
+Seed: 42 (for reproducibility)
+Optimizer: Adam (with betas=(0.9, 0.999) and epsilon=1e-08)
+Scheduler: Linear learning rate scheduler
+Number of epochs: 2
+The training used a combination of cross-entropy loss for multi-class classification and early stopping based on evaluation metrics.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -46,4 +75,4 @@ The following hyperparameters were used during training:
 - Transformers 4.44.2
 - Pytorch 2.4.1+cu121
 - Datasets 3.0.1
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1