Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use subzero9954/bert_model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subzero9954/bert_model_out with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="subzero9954/bert_model_out")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("subzero9954/bert_model_out") model = AutoModelForSequenceClassification.from_pretrained("subzero9954/bert_model_out", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from subzero9954/bert_model_out: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/subzero9954/bert_model_out/resolve/main/training_args.bin
- Command line
-
hf download hf://subzero9954/bert_model_out/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/subzero9954/bert_model_out/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 7f769a498192db5b7e5de8e56e8c599c35e03d37153bef001f67bcba7d2b9497
- Size of remote file:
- 5.37 kB
- SHA256:
- 81816999196a52cd3fd5dc960d0ff67ef86085a2e6ec7b64ec15861274708e76
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.