Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example") model = AutoModelForSequenceClassification.from_pretrained("ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55f55665ffed0863a1c34c8e647146af257f2c10372dcf1cae8533cc6186231d
- Size of remote file:
- 268 MB
- SHA256:
- d35f39d6033f7658fc80c028f0dde7d0c657081bdb9d995c0c83e0624e3fed97
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