Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use mxy680/sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mxy680/sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mxy680/sentiment-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mxy680/sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("mxy680/sentiment-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66a6b2709dbeed2ece449a9cb8f58da3a00fadc5eb88fe6696f628e28cd5eb1b
- Size of remote file:
- 4.92 kB
- SHA256:
- a3b6656a72ffcb250db72fd26873b267b62849e7fc1b0d7453c1d66545c7af3a
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