Instructions to use ModelTC/bart-base-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bart-base-sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-sst2") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bart-base-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ModelTC/bart-base-sst2: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/ModelTC/bart-base-sst2/resolve/main/optimizer.pt
- Command line
-
hf download hf://ModelTC/bart-base-sst2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ModelTC/bart-base-sst2/resolve/main/optimizer.pt
1.12 GB
- Xet hash:
- a5ae733167f1679eace6106c07886b6a6e7fd95ed9e1e4184f83decf4a254421
- Size of remote file:
- 1.12 GB
- SHA256:
- 7691083da663882dc13cef10ee60da6de916d387d448735e30c18f332b4cdffd
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