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
| 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` |