---
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](https://huggingface.co/intfloat/e5-large) on the glue (mnli) and anli dataset.

## Model description

[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
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:

```python
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.

```python
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:

```python
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]}
```

### 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|
| :---: | :---: | :---: | :---: | :---: | :---: |
|[e5-base-mnli](https://huggingface.co/mjwong/e5-base-mnli)|0.840|0.839|0.231|0.285|0.309|
|[e5-base-v2-mnli](https://huggingface.co/mjwong/e5-base-v2-mnli)|0.844|0.838|0.253|0.288|0.301|
|[e5-large-mnli](https://huggingface.co/mjwong/e5-large-mnli)|0.868|0.869|0.301|0.296|0.294|
|[e5-large-v2-mnli](https://huggingface.co/mjwong/e5-large-v2-mnli)|0.875|0.876|0.354|0.298|0.313|
|[e5-large-unsupervised-mnli](https://huggingface.co/mjwong/e5-large-unsupervised-mnli)|0.865|0.867|0.314|0.285|0.303|
|[e5-large-mnli-anli](https://huggingface.co/mjwong/e5-large-mnli-anli)|0.843|0.848|0.646|0.484|0.458|
|[e5-large-unsupervised-mnli-anli](https://huggingface.co/mjwong/e5-large-unsupervised-mnli-anli)|0.836|0.842|0.634|0.481|0.478|

### 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