Instructions to use KalaiselvanD/model_bert_7000_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_5") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KalaiselvanD/model_bert_7000_5: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/KalaiselvanD/model_bert_7000_5/resolve/main/training_args.bin
- Command line
-
hf download hf://KalaiselvanD/model_bert_7000_5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KalaiselvanD/model_bert_7000_5/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 50e02f784506c1a1ecd05c13a6f5a87d31d9034aed9a5e99cead33edc6c46437
- Size of remote file:
- 4.92 kB
- SHA256:
- 2afeeedc1d62299752e245dea8f89f93be87e61628c6e440e7db813dddf528d9
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