Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use sssey/bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sssey/bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sssey/bert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sssey/bert_model") model = AutoModelForSequenceClassification.from_pretrained("sssey/bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sssey/bert_model: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/sssey/bert_model/resolve/main/training_args.bin
- Command line
-
hf download hf://sssey/bert_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sssey/bert_model/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 8962a927fe81006e67b110466815d1677563f993fb68ca9abf7057954e06027e
- Size of remote file:
- 5.3 kB
- SHA256:
- 8da293d161cf1f28715e48488428b6f8a48769689bda01affbb0cad000a6f5dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.