|
--- |
|
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](https://huggingface.co/facebook/contriever-msmarco) on the glue dataset. |
|
|
|
## Model description |
|
|
|
[Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). |
|
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: |
|
|
|
```python |
|
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. |
|
|
|
```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': [0.9954835772514343, 0.002568634692579508, 0.00194773287512362]} |
|
``` |
|
|
|
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': ['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](https://huggingface.co/mjwong/contriever-mnli)|0.821|0.822|0.247|0.281|0.312| |
|
|[contriever-msmarco-mnli](https://huggingface.co/mjwong/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 |
|
|
