Text Classification
Transformers
PyTorch
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use petermutwiri/NLP_Capstone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petermutwiri/NLP_Capstone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="petermutwiri/NLP_Capstone")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("petermutwiri/NLP_Capstone") model = AutoModelForSequenceClassification.from_pretrained("petermutwiri/NLP_Capstone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from petermutwiri/NLP_Capstone: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/petermutwiri/NLP_Capstone/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://petermutwiri/NLP_Capstone/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/petermutwiri/NLP_Capstone/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 5ce645a88a63a26327ee49a9f07320244f0dc8c7625d6b97861184cb1401d135
- Size of remote file:
- 499 MB
- SHA256:
- 05b81f97c2b44a35dc7a9a25c99fd2ba272414250ec802f9fe22a6a8d9d4f9ca
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