Text Classification
Transformers
PyTorch
Safetensors
English
distilbert
sentiment-analysis
text-embeddings-inference
Instructions to use juliensimon/reviews-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliensimon/reviews-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="juliensimon/reviews-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("juliensimon/reviews-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("juliensimon/reviews-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8637ee48cd39247aba15140dc040509972dc13c9309dc0b03eee1a19ea92d06b
- Size of remote file:
- 268 MB
- SHA256:
- 54542046a4f7431dc616d9741172f5d3a51e37898bce44202575d7a94f4f732a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.