# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dejanseo/sentiment")
model = AutoModelForSequenceClassification.from_pretrained("dejanseo/sentiment")Quick Links
Multi-label sentiment classification model developed by Dejan Marketing.
To see this model in action visit: Sentiment Tool
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 URL and text processing?
Please book an appointment 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.
- Downloads last month
- 58
Model tree for dejanseo/sentiment
Base model
albert/albert-base-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dejanseo/sentiment")