dejanai/sentiment
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How to use dejanai/sentiment with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="dejanai/sentiment") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dejanai/sentiment")
model = AutoModelForSequenceClassification.from_pretrained("dejanai/sentiment", device_map="auto")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.
Interested in using this in an automated pipeline for bulk URL and text processing?
Please book an appointment to discuss your needs.
albert/albert-base-v2
sentiment_labels = {
0: "very positive",
1: "positive",
2: "somewhat positive",
3: "neutral",
4: "somewhat negative",
5: "negative",
6: "very negative"
}
Synthetic. Llama3.
Base model
albert/albert-base-v2