Instructions to use bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif") model = AutoModelForSequenceClassification.from_pretrained("bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- c2af81d3c2c7f60214ec0f953ef85f0af1802b8366801f0a239a0a83f52ef8e2
- Size of remote file:
- 438 MB
- SHA256:
- 2d39e6425429879d8bc48129d71971eaaa6f6346b03235bc26779d65a835fc27
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