Text Classification
Transformers
PyTorch
Safetensors
Spanish
electra
restaurant
classification
reviews
Instructions to use mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c421398ff2a7cd4b6b78f278da2dcf3e95cf6afb6abf77fee91d4eb00eb99dc
- Size of remote file:
- 54.5 MB
- SHA256:
- 496d48f844cdb277219696bb91c0c6146f9ab02542f311c6794504e8c5e96b0d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.