Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use saming32/bert_model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saming32/bert_model_out with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="saming32/bert_model_out")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("saming32/bert_model_out") model = AutoModelForSequenceClassification.from_pretrained("saming32/bert_model_out", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from saming32/bert_model_out: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/saming32/bert_model_out/resolve/main/training_args.bin
- Command line
-
hf download hf://saming32/bert_model_out/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/saming32/bert_model_out/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 48fd5f8bf17f7056c50ceb6f883f8acf678822eb63a603c585b740d284108854
- Size of remote file:
- 5.37 kB
- SHA256:
- 03c505220d11ccc20e57e9857e55e4d84b3c0bbc44cebe1c6f673d8b0c458e62
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