Instructions to use cungnlp/FT-BERT-Task3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cungnlp/FT-BERT-Task3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cungnlp/FT-BERT-Task3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cungnlp/FT-BERT-Task3") model = AutoModelForSequenceClassification.from_pretrained("cungnlp/FT-BERT-Task3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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example : With the new production plant the company would increase its capacity to meet the expected increase in demand and would improve the use of raw materials and therefore increase the production profitability . (positive- label_0) Net sales fell by 5 % from the previous accounting period .(negative - label_1 ) The sale price was not disclosed . (neutral -label_2)d5a2a35 -
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