Instructions to use KalaiselvanD/model_bert_7000_26 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_26 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_26")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_26") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_26", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_bert_7000_26: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/KalaiselvanD/model_bert_7000_26/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_bert_7000_26/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_bert_7000_26/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 7f20fd6cb004a061a7a971375af71f533db04019e1280339964d9a8bda1ef52e
- Size of remote file:
- 4.98 kB
- SHA256:
- dc6928f8b7931073141bec107aadafee0279096cf7ff2175a67dbf9b43935d04
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.