Instructions to use interneuronai/company_sentiment_analysis_bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/company_sentiment_analysis_bart with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/company_sentiment_analysis_bart")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("interneuronai/company_sentiment_analysis_bart") model = AutoModelForSequenceClassification.from_pretrained("interneuronai/company_sentiment_analysis_bart") - Notebooks
- Google Colab
- Kaggle
Company_Sentiment_Analysis
Description: Analyze customer opinions, feedback, and reviews about the company software, websites, and IT services to gain insights and improve products and services
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/company_sentiment_analysis_bart"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
return predictions.item()
text = "Your text here"
print("Category:", classify_text(text))
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