Instructions to use cungnlp/FineTuningBERTbaseClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cungnlp/FineTuningBERTbaseClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cungnlp/FineTuningBERTbaseClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cungnlp/FineTuningBERTbaseClassification") model = AutoModelForSequenceClassification.from_pretrained("cungnlp/FineTuningBERTbaseClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
302f1a9 -
Update README.md
e1cecdb -
Update README.md
e915602 -
Update README.md
716c708 -
Create README.md
English sentence classification model /////////////////////////// labels meaning 0 Positive 1 Negative 2 Neutral 3 Extremely Positive 4 Extremely Negativec29c01d -
Upload tokenizer
473f29c -
Upload BertForSequenceClassification
5c8733e -
initial commit
80323d5