Model V0 Datacard Update

README.md

@@ -9,13 +9,13 @@ pipeline_tag: text-classification
 
 # Topic Classifier
 
-This repository contains the Topic Classifier model developed by DAXA.AI. The Topic Classifier is a machine learning model designed to categorize text documents across various domains, such as corporate documents, financial texts, harmful content, and medical documents.
+This repository contains the Pebblo Classifier model developed by DAXA.AI. The Pebblo Classifier is a machine learning model designed to categorize text documents across various domains, such as corporate documents, financial texts, harmful content, and medical documents.
 
 ## Model Details
 
 ### Model Description
 
-The Topic Classifier is a BERT-based model, fine-tuned from the `distilbert-base-uncased` model. It is intended for categorizing text into specific topics, including "CORPORATE_DOCUMENTS," "FINANCIAL," "HARMFUL," and "MEDICAL." This model streamlines text classification tasks across multiple sectors, making it suitable for various business use cases.
+The Pebblo Classifier is a BERT-based model, fine-tuned from the distilbert-base-uncased model. It is intended for categorizing text into specific topics, including "CORPORATE_DOCUMENTS," "FINANCIAL," "HARMFUL," and "MEDICAL." This model streamlines text classification tasks across multiple sectors, making it suitable for various business use cases.
 
 - **Developed by:** DAXA.AI
 - **Funded by:** Open Source
@@ -26,14 +26,14 @@ The Topic Classifier is a BERT-based model, fine-tuned from the `distilbert-base
 
 ### Model Sources
 
-- **Repository:** [https://huggingface.co/daxa-ai/topic-classifier](https://huggingface.co/daxa-ai/Topic-Classifier-2)
+- **Repository:** [https://huggingface.co/daxa-ai/pebblo-classifier-v2](https://huggingface.co/daxa-ai/pebblo-classifier-v2)
 - **Demo:** [https://huggingface.co/spaces/daxa-ai/Topic-Classifier-2](https://huggingface.co/spaces/daxa-ai/Topic-Classifier-2)
 
 ## Usage
 
 ### How to Get Started with the Model
 
-To use the Topic Classifier in your Python project, you can follow the steps below:
+To use the Pebblo Classifier in your Python project, you can follow the steps below:
 
 ```python
 # Import necessary libraries
@@ -43,8 +43,8 @@ import joblib
 from huggingface_hub import hf_hub_url, cached_download
 
 # Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("daxa-ai/topic-classifier")
-model = AutoModelForSequenceClassification.from_pretrained("daxa-ai/topic-classifier")
+tokenizer = AutoTokenizer.from_pretrained("daxa-ai/pebblo-classifier-v2")
+model = AutoModelForSequenceClassification.from_pretrained("daxa-ai/pebblo-classifier-v2")
 
 # Example text
 text = "Please enter your text here."
@@ -58,7 +58,7 @@ probabilities = torch.nn.functional.softmax(output.logits, dim=-1)
 predicted_label = torch.argmax(probabilities, dim=-1)
 
 # URL of your Hugging Face model repository
-REPO_NAME = "daxa-ai/topic-classifier"
+REPO_NAME = "daxa-ai/pebblo-classifier-v2"
 
 # Path to the label encoder file in the repository
 LABEL_ENCODER_FILE = "label_encoder.joblib"
@@ -161,6 +161,6 @@ def predict_fn(data, model_and_tokenizer):
 
 ## Conclusion
 
-The Topic Classifier achieves high accuracy, precision, recall, and F1-score, making it a reliable model for categorizing text across the domains of corporate documents, financial content, harmful content, and medical texts. The model is optimized for immediate deployment and works efficiently in real-world applications.
+The Pebblo Classifier achieves high accuracy, precision, recall, and F1-score, making it a reliable model for categorizing text across the domains of corporate documents, financial content, harmful content, and medical texts. The model is optimized for immediate deployment and works efficiently in real-world applications.
 
 For more information or to try the model yourself, check out the public space [here](https://huggingface.co/spaces/daxa-ai/Topic-Classifier-2).