Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use pnskmj1017/bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pnskmj1017/bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pnskmj1017/bert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pnskmj1017/bert_model") model = AutoModelForSequenceClassification.from_pretrained("pnskmj1017/bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pnskmj1017/bert_model: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/pnskmj1017/bert_model/resolve/main/training_args.bin
- Command line
-
hf download hf://pnskmj1017/bert_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pnskmj1017/bert_model/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 141837969c0cfcd6ea64772875db836d37e3ad1b511bd6454a2d5d24f70d8095
- Size of remote file:
- 5.3 kB
- SHA256:
- bfcf7d8770184750607c43db896f39950649581f950b090dbd9b666f78f48712
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