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
File size: 388 Bytes
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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.
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