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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cungnlp/FineTuningBERTbaseClassification") model = AutoModelForSequenceClassification.from_pretrained("cungnlp/FineTuningBERTbaseClassification") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cungnlp/FineTuningBERTbaseClassification")
model = AutoModelForSequenceClassification.from_pretrained("cungnlp/FineTuningBERTbaseClassification")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Fine Tuning BERT-base for Classification
labels_meaning : 0_Positive, 1_Negative, 2_Neutral, 3_Extremely Positive, 4_Extremely Negative.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cungnlp/FineTuningBERTbaseClassification")