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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example/resolve/main/training_args.bin
- Command line
-
hf download hf://ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ajtamayoh/Curso_NLP_UdeA_Sequence_Classification_Example/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- eb0c32fde2f9ac551df12c4b1bb29c77076c861adaf76fa1112fa3bf16013966
- Size of remote file:
- 5.2 kB
- SHA256:
- 72afdb3d3db84dc08a2aad71d02f428ef9dfea3a07b9be7307c9e34755174b5d
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