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 training_args.bin from petermutwiri/NLP_Capstone: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/petermutwiri/NLP_Capstone/resolve/main/training_args.bin
- Command line
-
hf download hf://petermutwiri/NLP_Capstone/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/petermutwiri/NLP_Capstone/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- e567bf39d4c6d322ab90c0d425c738395bbc70295daf5daaf4124e1965b27ce8
- Size of remote file:
- 4.47 kB
- SHA256:
- 8b8fd5192c1bb171fcfb4873935df6ae28235f0f0e8002c6ac6af9db265a7327
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