Update README.md

README.md

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-license: bigscience-openrail-m
----
+---
+license: bigscience-openrail-m
+pipeline_tag: text-classification
+
+widget:
+  - example_title: "Very Positive"
+    text: "This product is absolutely amazing!"
+  - example_title: "Positive"
+    text: "I like the features of this app."
+  - example_title: "Somewhat Positive"
+    text: "The service is pretty good."
+  - example_title: "Neutral"
+    text: "It meets my expectations."
+  - example_title: "Somewhat Negative"
+    text: "The experience was not as bad."
+  - example_title: "Negative"
+    text: "I'm disappointed with the quality."
+  - example_title: "Very Negative"
+    text: "This is the worst purchase I've ever made."
+
+---
+Multi-label sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/).
+
+The model is designed to be deployed in an automated pipeline capable of classifying text sentiment for thousands (or even millions) of text chunks or as a part of a scraping pipeline.
+
+This is a demo model which may occassionally misclasify some texts. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.
+
+# Engage Our Team
+Interested in using this in an automated pipeline for bulk query processing?
+
+Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs.
+
+# Base Model
+
+albert/albert-base-v2
+
+
+## Labels
+
+sentiment_labels = {
+    0: "very positive",
+    1: "positive",
+    2: "somewhat positive",
+    3: "neutral",
+    4: "somewhat negative",
+    5: "negative",
+    6: "very negative"
+}
+
+# Sources of Training Data
+
+Synthetic. Llama3.