Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use BaxterAI/SentimentClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaxterAI/SentimentClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaxterAI/SentimentClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaxterAI/SentimentClassifier") model = AutoModelForSequenceClassification.from_pretrained("BaxterAI/SentimentClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2d5bc5c419a4cf2b327391592298aebd2faee5e8e64a13e53e5ebb651e7f3e2
- Size of remote file:
- 268 MB
- SHA256:
- 5e8f31715cc60958192f0abbb84543e6fbab30887896c19c07dd4a9715b294b2
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