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:
- 7e2f8a5186dd9d8db348ed1f6ace6f5a7e25f4bb5d5d550ad6e8641375713a9f
- Size of remote file:
- 268 MB
- SHA256:
- 0daf9ba2780048fddb0a40da9c151530e65f42e9baa18a4fa4efa51d33ea3f21
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