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
Update README.md
Browse files
README.md
CHANGED
|
@@ -6,6 +6,7 @@ task_categories:
|
|
| 6 |
- text-classification
|
| 7 |
tags:
|
| 8 |
- arxiv:2305.06156
|
|
|
|
| 9 |
metrics:
|
| 10 |
- accuracy
|
| 11 |
widget:
|
|
@@ -66,7 +67,7 @@ More information:
|
|
| 66 |
|
| 67 |
## Model Details
|
| 68 |
* Developed by: [Fsoft AI Center](https://www.fpt-aicenter.com/ai-residency/)
|
| 69 |
-
* License:
|
| 70 |
* Model type: Transformer-Encoder based Language Model
|
| 71 |
* Architecture: BERT-base
|
| 72 |
* Data set: [The Vault](https://huggingface.co/datasets/Fsoft-AIC/thevault-function-level)
|
|
|
|
| 6 |
- text-classification
|
| 7 |
tags:
|
| 8 |
- arxiv:2305.06156
|
| 9 |
+
license: mit
|
| 10 |
metrics:
|
| 11 |
- accuracy
|
| 12 |
widget:
|
|
|
|
| 67 |
|
| 68 |
## Model Details
|
| 69 |
* Developed by: [Fsoft AI Center](https://www.fpt-aicenter.com/ai-residency/)
|
| 70 |
+
* License: MIT
|
| 71 |
* Model type: Transformer-Encoder based Language Model
|
| 72 |
* Architecture: BERT-base
|
| 73 |
* Data set: [The Vault](https://huggingface.co/datasets/Fsoft-AIC/thevault-function-level)
|