Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mankness/phrasebank-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mankness/phrasebank-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mankness/phrasebank-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mankness/phrasebank-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("mankness/phrasebank-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4184976226ace1e64b623700391747f60e623cb5af8e8b9632276d37afc943a8
- Size of remote file:
- 438 MB
- SHA256:
- 8464ffac5d7a5829eafdfaa1a5ef1223a98a54249a1b1827806c015709550b11
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