Text Classification
Keras
English
chemistry
scientific-literature
multilabel
classification
embeddings
Eval Results (legacy)
Instructions to use Bocklitz-Lab/lit2vec-subfield-classifier-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Bocklitz-Lab/lit2vec-subfield-classifier-model with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Bocklitz-Lab/lit2vec-subfield-classifier-model") - Notebooks
- Google Colab
- Kaggle
Mahmoud Amiri commited on
Commit ·
baff529
1
Parent(s): 22cb24e
update readme file
Browse files
README.md
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# Lit2Vec Subfield Classifier (MLP)
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> Multi-label classifier for chemistry subfields using dense text embeddings.
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---
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language:
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- en
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library_name: keras
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pipeline_tag: text-classification
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license: cc-by-4.0
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tags:
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- chemistry
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- scientific-literature
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- multilabel
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- classification
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- embeddings
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- keras
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datasets:
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- Bocklitz-Lab/lit2vec-subfield-classifier-dataset
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model-index:
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- name: lit2vec-subfield-classifier-model
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results:
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- task:
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name: Multi-label Text Classification
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type: text-classification
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dataset:
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name: Lit2Vec Subfield Classifier Dataset
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type: Bocklitz-Lab/lit2vec-subfield-classifier-dataset
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split: test
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size: 7482
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metrics:
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- type: micro_f1
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value: 0.81
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- type: macro_f1
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value: 0.75
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- type: weighted_f1
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value: 0.80
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- type: pr_auc
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value: 0.8688
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- type: roc_auc
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value: 0.9725
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---
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# Lit2Vec Subfield Classifier (MLP)
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> Multi-label classifier for chemistry subfields using dense text embeddings.
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