metadata

datasets:
  - glue
model-index:
  - name: contriever-msmarco-mnli
    results: []
pipeline_tag: zero-shot-classification
language:
  - en
license: mit

contriever-msmarco-mnli

This model is a fine-tuned version of facebook/contriever-msmarco on the glue dataset.

Model description

Unsupervised Dense Information Retrieval with Contrastive Learning. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave, arXiv 2021

How to use the model

The model can be loaded with the zero-shot-classification pipeline like so:

from transformers import pipeline
classifier = pipeline("zero-shot-classification",
                      model="mjwong/contriever-msmarco-mnli")

You can then use this pipeline to classify sequences into any of the class names you specify.

sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
#{'sequence': 'one day I will see the world',
# 'labels': ['travel', 'dancing', 'cooking'],
# 'scores': [0.9954835772514343, 0.002568634692579508, 0.00194773287512362]}

If more than one candidate label can be correct, pass multi_class=True to calculate each class independently:

candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_class=True)
#{'sequence': 'one day I will see the world',
# 'labels': ['travel', 'exploration', 'cooking', 'dancing'],
# 'scores': [0.9968098998069763,
#  0.9796287417411804,
#  0.027883002534508705,
#  0.0008239754824899137]}

Eval results

The model was evaluated using the dev sets for MultiNLI and test sets for ANLI. The metric used is accuracy.

Datasets	mnli_dev_m	mnli_dev_mm	anli_test_r1	anli_test_r2	anli_test_r3
contriever-mnli	0.821	0.822	0.247	0.281	0.312
contriever-msmarco-mnli	0.820	0.819	0.244	0.296	0.306

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Framework versions

Transformers 4.28.1
Pytorch 1.12.1+cu116
Datasets 2.11.0
Tokenizers 0.12.1