Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use victorwkey/sequence-model-platzi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use victorwkey/sequence-model-platzi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="victorwkey/sequence-model-platzi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("victorwkey/sequence-model-platzi") model = AutoModelForSequenceClassification.from_pretrained("victorwkey/sequence-model-platzi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from victorwkey/sequence-model-platzi: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/victorwkey/sequence-model-platzi/resolve/main/training_args.bin
- Command line
-
hf download hf://victorwkey/sequence-model-platzi/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/victorwkey/sequence-model-platzi/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 2bd27a52b63bbf86f043fa92fe550fe62b78fe61208bac9afbe732a26781a467
- Size of remote file:
- 5.37 kB
- SHA256:
- 59a158e0578486f5a16761b5565ab55d0423cabd4d8f288c82f04b32f3f998b9
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