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:
- 1d360f0c58c3dd7a40c84e90c4e09c6b250b7a7d1c4b89a2cc23ae196e397ec7
- Size of remote file:
- 268 MB
- SHA256:
- 70edd103af21a97e878e5d75d49f8bde8c528ee4ea122b77a1a85ed6c5f62009
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