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---
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]}
```
### 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
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