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:
- 096b975a55bc8cfafabe0e7372c68f538b6cb451c524a22edebecefb3e8a82c0
- Size of remote file:
- 268 MB
- SHA256:
- bec29b15ca5254013649885b0a210ee17ffc9a7529f8e811171baec7c37c38ac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.