Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use zera09/sapect_complaint_spanbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zera09/sapect_complaint_spanbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zera09/sapect_complaint_spanbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zera09/sapect_complaint_spanbert") model = AutoModelForSequenceClassification.from_pretrained("zera09/sapect_complaint_spanbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1dc1c64e171f7b59d0fe7575aceb3bf6fd10cfaf4fb043990d7961aa4502dd0f
- Size of remote file:
- 4.67 kB
- SHA256:
- 8982f2865b57f23bb8e669eac83d989557732de460ecac5e62e98431b6bf68d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.