Text Classification
Transformers
PyTorch
JAX
Safetensors
code
English
roberta
text-embeddings-inference
Instructions to use Fsoft-AIC/Codebert-docstring-inconsistency with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/Codebert-docstring-inconsistency with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/Codebert-docstring-inconsistency")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency") - Notebooks
- Google Colab
- Kaggle
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README.md
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Using model with Jax
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```python
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from transformers import AutoTokenizer, FlaxAutoModelForSequenceClassification
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#Load jax model
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model = FlaxAutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency")
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```
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Using model with Pytorch
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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#Load torch model
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model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency")
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"""
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```
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Using model with Jax and Pytorch
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```python
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from transformers import AutoTokenizer, FlaxAutoModelForSequenceClassification
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#Load jax model
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model = FlaxAutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency")
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#Load torch model
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model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency")
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