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autotrain-imdb-sentiment-analysis-864927559
anwesham/autotrain-imdb-sentiment-analysis-864927559
autotrain-imdb-sentiment-analysis-864927559 is a text classification model from anwesham. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 864927559 - CO2 Emissions (in grams): 0.2033402242358345
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- distilbert
- Access
- Public
- Created
- May 14, 2022
- Updated
- May 14, 2022
- SHA
- d47528c6
Model card
From the Hugging Face model README
- Problem type: Binary Classification
- Model ID: 864927559
- CO2 Emissions (in grams): 0.2033402242358345
Validation Metrics
- Loss: 0.18383920192718506
- Accuracy: 0.9318
- Precision: 0.9560625264047318
- Recall: 0.9052
- AUC: 0.98281574
- F1: 0.9299363057324841
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 AutoTrain"}' https://api-inference.huggingface.co/models/anwesham/autotrain-imdb-sentiment-analysis-864927559
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("anwesham/autotrain-imdb-sentiment-analysis-864927559", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("anwesham/autotrain-imdb-sentiment-analysis-864927559", use_auth_token=True)
inputs = tokenizer("I love to eat food", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Languages
- unk
- Created
- May 14, 2022
- Updated
- May 14, 2022