Instructions to use JamesH/Movie_review_sentiment_analysis_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JamesH/Movie_review_sentiment_analysis_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JamesH/Movie_review_sentiment_analysis_model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JamesH/Movie_review_sentiment_analysis_model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:Config file config.json cannot be fetched (too big)
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1883864250
- CO2 Emissions (in grams): 6.9919
Validation Metrics
- Loss: 0.175
- Accuracy: 0.950
- Precision: 0.950
- Recall: 0.950
- AUC: 0.986
- F1: 0.950
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/JamesH/autotrain-third-project-1883864250
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("JamesH/autotrain-third-project-1883864250", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("JamesH/autotrain-third-project-1883864250", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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