Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Alejandro-sin/NPL-experiment-agl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alejandro-sin/NPL-experiment-agl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Alejandro-sin/NPL-experiment-agl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Alejandro-sin/NPL-experiment-agl") model = AutoModelForSequenceClassification.from_pretrained("Alejandro-sin/NPL-experiment-agl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4486469cfd633fcd6ec67c290652f6844afca771fa7184928bec0ffe2733a041
- Size of remote file:
- 329 MB
- SHA256:
- 127ac5b92408e3ac28e0a69233f25f90f6738cededc3814a8d839c3a5f937af1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.