metadata

datasets:
  - glue
  - anli
model-index:
  - name: e5-large-mnli-anli
    results: []
pipeline_tag: zero-shot-classification
language:
  - en
license: mit

e5-large-mnli-anli

This model is a fine-tuned version of intfloat/e5-large on the glue (mnli) and anli dataset.

Model description

Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022

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/e5-large-mnli-anli")

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': [0.9878318905830383, 0.01044005248695612, 0.001728130504488945]}

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': ['exploration', 'travel', 'dancing', 'cooking'],
# 'scores': [0.9956096410751343,
#  0.9929478764533997,
#  0.21706733107566833,
#  0.0005817742203362286]}

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: 2

Framework versions

Transformers 4.28.1
Pytorch 1.12.1+cu116
Datasets 2.11.0
Tokenizers 0.12.1