Instructions to use CHIPP-AI/model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CHIPP-AI/model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CHIPP-AI/model2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CHIPP-AI/model2") model = AutoModelForSequenceClassification.from_pretrained("CHIPP-AI/model2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 842 Bytes
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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
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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<br>
1--> Neutral<br>
2--> Positive
## Metrics on Test Set
Accuracy-0.93 <br>
F1-0.92<br>
Precision-0.92<br>
Recall-0.93<br>
MCC-0.81<br>
Eval Loss-0.42 <br> |