Instructions to use KalaiselvanD/model_bert_7000_32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_32") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_bert_7000_32: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/model_bert_7000_32/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_bert_7000_32/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_bert_7000_32/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 4c1d2da4bb4bba51e7d903c32968a7bc19e5e277d11425d2218a88f8cb9c4d18
- Size of remote file:
- 4.98 kB
- SHA256:
- a34de12270ca026886caac19445e0b6ea47cab6ff1486a91aca0a557e2787448
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