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:
- 429ab17a03f09e078120cd2dba9a5c5f52e9b576e3b0933e262f4a4a242acfe6
- Size of remote file:
- 4.41 kB
- SHA256:
- 601569673c17110ce7eb1215931980d59ac71df74ea3ae1c26fdaf9e066216df
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