Instructions to use CHIPP-AI/model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CHIPP-AI/model3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CHIPP-AI/model3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CHIPP-AI/model3") model = AutoModelForSequenceClassification.from_pretrained("CHIPP-AI/model3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
datasets:
- McAuley-Lab/Amazon-Reviews-2023
license: mit
language:
- en
base_model:
- microsoft/deberta-v3-base
pipeline_tag: text-classification
Model Card for Model ID
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Ruchit Pokhrel and Sandra
- Language(s) (NLP): English
- License: MIT
- Finetuned from model microsoft/deberta-v3-base
Label
0 --> Negative
1--> Neutral
2--> Positive
Metrics on Test Set
Accuracy-0.93
F1-0.92
Precision-0.92
Recall-0.93
MCC-0.81
Eval Loss-0.42