Instructions to use thinhkosay/sentiment-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thinhkosay/sentiment-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thinhkosay/sentiment-bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("thinhkosay/sentiment-bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("thinhkosay/sentiment-bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from thinhkosay/sentiment-bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/thinhkosay/sentiment-bert-base-uncased/resolve/main/training_args.bin
- Command line
-
hf download hf://thinhkosay/sentiment-bert-base-uncased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/thinhkosay/sentiment-bert-base-uncased/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- f0b98ef77ebd603edc0db66a0e188855c7332ebd55b15a0f3149922803a207b3
- Size of remote file:
- 4.98 kB
- SHA256:
- 82975fd7e02d5914ff528e5772217b95b734bea867c2d6ecd11063821be3b926
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