Instructions to use BILALfym/skimlit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use BILALfym/skimlit-model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://BILALfym/skimlit-model") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
language:
- en
library_name: keras
tags:
- SkimLit
- NLP
- Abstract Classification
---
# SkimLit Model
Keras model for classifying scientific abstract sections
(Background, Objective, Methods, Results, Conclusions).
## Usage
```python
import tensorflow as tf
model = tf.keras.models.load_model('model_5.keras')
Model Details
- Framework: TensorFlow/Keras
- Input: Token embeddings + character text + positional info
- Output: 5 classes (Background, Objective, Methods, Results,
Conclusions)
**Metadata à remplir:**
- **license**: `apache-2.0` (ou autre)
- **language**: `English`
- **tags**: `skimlit`, `nlp`, `text-classification`
- **pipeline_tag**: `text-classification` |