
# contriever-mnli

This model is a fine-tuned version of [facebook/contriever](https://huggingface.co/facebook/contriever) 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

### With the zero-shot classification pipeline

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-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)
```

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)
```

### With manual PyTorch

The model can also be applied on NLI tasks like so:

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# device = "cuda:0" or "cpu"
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model_name = "mjwong/contriever-mnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

premise = "But I thought you'd sworn off coffee."
hypothesis = "I thought that you vowed to drink more coffee."

input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device))
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 2) for pred, name in zip(prediction, label_names)}
print(prediction)
```

### 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

{"language": ["en"], "license": "mit", "datasets": ["glue"], "pipeline_tag": "zero-shot-classification", "model-index": [{"name": "contriever-mnli", "results": []}]}
transformers pytorch safetensors bert text-classification zero-shot-classification en dataset:glue arxiv:2112.09118 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #zero-shot-classification #en #dataset-glue #arxiv-2112.09118 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

contriever-mnli
===============


This model is a fine-tuned version of facebook/contriever 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
--------------------


### With the zero-shot classification pipeline


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


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


If more than one candidate label can be correct, pass 'multi\_class=True' to calculate each class independently:


### With manual PyTorch


The model can also be applied on NLI tasks like so:


### Eval results


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



### 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



### With the zero-shot classification pipeline


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


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


If more than one candidate label can be correct, pass 'multi\_class=True' to calculate each class independently: ### With manual PyTorch


The model can also be applied on NLI tasks like so: ### Eval results


The model was evaluated using the dev sets for MultiNLI and test sets for ANLI. The metric used is accuracy. ### 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
TAGS
#transformers #pytorch #safetensors #bert #text-classification #zero-shot-classification #en #dataset-glue #arxiv-2112.09118 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### With the zero-shot classification pipeline


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


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


If more than one candidate label can be correct, pass 'multi\_class=True' to calculate each class independently: ### With manual PyTorch


The model can also be applied on NLI tasks like so: ### Eval results


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