Instructions to use mantasb/autotrain-financial-sentiment-analysis-61633134830 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mantasb/autotrain-financial-sentiment-analysis-61633134830 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mantasb/autotrain-financial-sentiment-analysis-61633134830")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mantasb/autotrain-financial-sentiment-analysis-61633134830") model = AutoModelForSequenceClassification.from_pretrained("mantasb/autotrain-financial-sentiment-analysis-61633134830", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 61633134830
- CO2 Emissions (in grams): 0.4868
Validation Metrics
- Loss: 0.115
- Accuracy: 0.959
- Precision: 0.960
- Recall: 0.957
- AUC: 0.992
- F1: 0.958
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/Mantas/autotrain-financial-sentiment-analysis-61633134830
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Mantas/autotrain-financial-sentiment-analysis-61633134830", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Mantas/autotrain-financial-sentiment-analysis-61633134830", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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