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:
- 1408111a423a75c1320413ddc0a639d69a417091414b5d29ebec3d5c4222fcf7
- Size of remote file:
- 268 MB
- SHA256:
- 8d7cce575d27b8b617a31686830ba85f5b907b4b4fbd8b9f72811d9892c54658
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.