Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use hiwensen/bert_sql_classfication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiwensen/bert_sql_classfication with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hiwensen/bert_sql_classfication")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hiwensen/bert_sql_classfication") model = AutoModelForSequenceClassification.from_pretrained("hiwensen/bert_sql_classfication", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hiwensen/bert_sql_classfication: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/hiwensen/bert_sql_classfication/resolve/main/model.safetensors
- Command line
-
hf download hf://hiwensen/bert_sql_classfication/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hiwensen/bert_sql_classfication/resolve/main/model.safetensors
268 MB
- Xet hash:
- 37024e8497c7b6bfebb6cd7e99f4e251187dbf82e0f955a6eb1fee6d8671cd23
- Size of remote file:
- 268 MB
- SHA256:
- 394179cd1421c770d95fd5e4a12f5fd86cc06bc727737d6bec96582438c43928
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.