Instructions to use KalaiselvanD/model_bert_7000_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_10") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_bert_7000_10: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/KalaiselvanD/model_bert_7000_10/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_bert_7000_10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_bert_7000_10/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- fa7630fa11e079a830754d18d2a308d3bb413e15660d4b3e0d1c19bd7817070e
- Size of remote file:
- 4.92 kB
- SHA256:
- 1912e37675b5ed195d39306826a45b8410b18df4effb3880c19b7cbb13b4386e
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