Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use dynncat/bert_model_result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dynncat/bert_model_result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dynncat/bert_model_result")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dynncat/bert_model_result") model = AutoModelForSequenceClassification.from_pretrained("dynncat/bert_model_result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dynncat/bert_model_result: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/dynncat/bert_model_result/resolve/main/training_args.bin
- Command line
-
hf download hf://dynncat/bert_model_result/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dynncat/bert_model_result/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 151da4389df6d5b2913dcfbbbc1bd1fafb67c2a556dbbf993753efd1bac3f2bc
- Size of remote file:
- 5.37 kB
- SHA256:
- d699f40b9e1c3ea762ac372a155679c20f83a0397b247487e9222a7ae092467d
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