metadata

license: apache-2.0
base_model: bert-base-multilingual-cased
model-index:
  - name: >-
      bert-base-multilingual-cased-finetuned-openalex-topic-classification-title-abstract
    results: []
pipeline_tag: text-classification
widget:
  - text: >-
      Cleavage of Structural Proteins during the Assembly of the Head of
      Bacteriophage T4

bert-base-multilingual-cased-finetuned-openalex-topic-classification-title-abstract

This model is a fine-tuned version of bert-base-multilingual-cased on a labeled dataset provided by CWTS: [CWTS Labeled Data]

This is NOT the full model being used to tag OpenAlex works with a topic. For that, check out the following github repo: OpenAlex Topic Classification

That repository will also contain information about text preprocessing, modeling, testing, and deployment.

Model description

The model was trained using the following input data format (so it is recommended the data be in this format as well):

"<TITLE> {insert-processed-title-here}\n<ABSTRACT> {insert-processed-abstract-here}"

The quickest way to use this model in Python is with the following code (assuming you have the transformers library installed):

from transformers import pipeline

title = "{insert-processed-title-here}"
abstract = "{insert-processed-abstract-here}"

classifier = \
    pipeline(model="OpenAlex/bert-base-multilingual-cased-finetuned-openalex-topic-classification-title-abstract", top_k=10)

classifier(f"""<TITLE> {title}\n<ABSTRACT> {abstract}""")

Intended uses & limitations

The model is intended to be used as part of a larger model that also incorporates journal information and citation features. However, this model is good if you want to use it for quickly generating a topic based only on a title/abstract.

Since this model was fine-tuned on a BERT model, all of the biases seen in that model will most likely show up in this model as well.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 6e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 6e-05, 'decay_steps': 335420, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 500, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Epoch
4.8075	3.6686	0.3839	0
3.4867	3.3360	0.4337	1
3.1865	3.2005	0.4556	2
2.9969	3.1379	0.4675	3
2.8489	3.0900	0.4746	4
2.7212	3.0744	0.4799	5
2.6035	3.0660	0.4831	6
2.4942	3.0737	0.4846	7

Framework versions

Transformers 4.35.2
TensorFlow 2.13.0
Datasets 2.15.0
Tokenizers 0.15.0