Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use misaza/Sentimiento-appmovilesdisbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use misaza/Sentimiento-appmovilesdisbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="misaza/Sentimiento-appmovilesdisbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("misaza/Sentimiento-appmovilesdisbert") model = AutoModelForSequenceClassification.from_pretrained("misaza/Sentimiento-appmovilesdisbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13db87de99948ab50acdcc185a2e13e6fd9f78d2257330c2b5cb6a1f708f430c
- Size of remote file:
- 4.98 kB
- SHA256:
- 1244d4e22c28e3ab5e5ef233aa2852129e24234c704f7b60a0948bca22581b48
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.