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")# pip install -U transformers accelerate # 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 training_args.bin from bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif/resolve/main/training_args.bin
- Command line
-
hf download hf://bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 73392404f7bb6653630555c17c4a7fd0ff5e773c425c12af0d8862be5540f975
- Size of remote file:
- 3.44 kB
- SHA256:
- c319ab08e29df5a2eaa2e1a1980bee27cb3b60f7d6c4a3b474c1aff201ad4f23
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.