license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-common-corpus-topic-simple-batch
results: []
Pleias-Topic-Detection
Pleias-Topic-Detection is an encoder-decoder specialized for topic detection. Given a document Pleias-Topic-Deduction will return a main topic that can be used for further downstream tasks (annotation, embedding indexation)
Pleias-Topic-Detection is a finetuned version of t5-small on a set of 70,000 documents and associated topics from Common Corpus. While t5-small has been reportedly only trained in English, the model actually shows unexpected capacities for multilingual annotation. The final corpus include a significant amount of texts in French, Spanish, Italian, Dutch and German and has been proven to work somewhat in all of theses languages.
Given that Pleias-Topic-Detection is a relatively lightweight model (70 million parameters) it can be used for classification at scale on a large corpus.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP