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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hiwensen/bert_sql_classfication") model = AutoModelForSequenceClassification.from_pretrained("hiwensen/bert_sql_classfication") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: bert_sql_classfication | |
| results: [] | |
| pipeline_tag: text-classification | |
| # bert_sql_classfication | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a dataset based on the spider dataset. | |