Instructions to use barto17/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use barto17/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="barto17/output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("barto17/output") model = AutoModelForSequenceClassification.from_pretrained("barto17/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from barto17/output: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/barto17/output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://barto17/output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/barto17/output/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- dc37db33c7587ac025058f839962b92817c01bffbb18999a674f673578b83446
- Size of remote file:
- 268 MB
- SHA256:
- 4f46ba32bea1eac01780faafcacbaab5d829656a69a4ed6e81df865e55259e45
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