Instructions to use mb7419/bert-base-uncased-amazon-ic-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mb7419/bert-base-uncased-amazon-ic-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mb7419/bert-base-uncased-amazon-ic-tuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mb7419/bert-base-uncased-amazon-ic-tuned") model = AutoModelForSequenceClassification.from_pretrained("mb7419/bert-base-uncased-amazon-ic-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 679 Bytes
a9d0164 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"4": "Amazon 2-Day Shipping Delays",
"5": "Issues with Amazon sellers",
"16": "Book Purchase Challenges",
"0": "Miscellaneous Amazon Topic",
"13": "Amazon Echo user experience",
"9": "Amazon Wish List Functionality",
"10": "Video Game Pre-Order Delivery Disappointment",
"6": "Unauthorized access to Amazon accounts",
"1": "Amazon Prime Day Sales",
"12": "Cross-border shopping on Amazon",
"7": "Amazon Delivery Issues",
"8": "Amazon Fire TV jailbreaking",
"2": "Amazon vs Shopify ecommerce",
"3": "Fake reviews on Amazon",
"14": "Amazon Rewards Visa Card",
"11": "Amazon refund and return process",
"15": "Amazon shipping carrier selection"
} |