Instructions to use CAMeL-Lab/readability-camelbert-word-CE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAMeL-Lab/readability-camelbert-word-CE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CAMeL-Lab/readability-camelbert-word-CE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CAMeL-Lab/readability-camelbert-word-CE") model = AutoModelForSequenceClassification.from_pretrained("CAMeL-Lab/readability-camelbert-word-CE") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -11,7 +11,7 @@ pipeline_tag: text-classification
|
|
| 11 |
|
| 12 |
## Model description
|
| 13 |
**CAMeLBERT+Word+CE** is a readability assessment model that was built by fine-tuning the **CAMeLBERT-msa** model with cross-entropy loss (**CE**).
|
| 14 |
-
For the fine-tuning, we used the **Word** input variant from BAREC-Corpus-v1.0.
|
| 15 |
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment](https://arxiv.org/abs/2502.13520)."*
|
| 16 |
|
| 17 |
## Intended uses
|
|
|
|
| 11 |
|
| 12 |
## Model description
|
| 13 |
**CAMeLBERT+Word+CE** is a readability assessment model that was built by fine-tuning the **CAMeLBERT-msa** model with cross-entropy loss (**CE**).
|
| 14 |
+
For the fine-tuning, we used the **Word** input variant from [BAREC-Corpus-v1.0](https://huggingface.co/datasets/CAMeL-Lab/BAREC-Corpus-v1.0).
|
| 15 |
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment](https://arxiv.org/abs/2502.13520)."*
|
| 16 |
|
| 17 |
## Intended uses
|