Text Classification
Transformers
PyTorch
English
deberta-v2
sentiment-analysis
text-embeddings-inference
Instructions to use madmancity/revmlc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madmancity/revmlc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="madmancity/revmlc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("madmancity/revmlc") model = AutoModelForSequenceClassification.from_pretrained("madmancity/revmlc") - Notebooks
- Google Colab
- Kaggle
Validation Metrics
- Loss: 0.595
- Accuracy: 0.789
- Macro F1: 0.575
- Micro F1: 0.789
- Weighted F1: 0.763
- Macro Precision: 0.630
- Micro Precision: 0.789
- Weighted Precision: 0.775
- Macro Recall: 0.588
- Micro Recall: 0.789
- Weighted Recall: 0.789
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love this product! One of my best purchases this year."}' https://api-inference.huggingface.co/models/madmancity/revmlc
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("madmancity/revmlc", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("madmancity/revmlc", use_auth_token=True)
inputs = tokenizer("I love this product! One of my best purchases this year.", return_tensors="pt")
outputs = model(**inputs)
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