
# mcontriever-msmarco-xnli

This model is a fine-tuned version of [facebook/mcontriever-msmarco](https://huggingface.co/facebook/mcontriever-msmarco) on the XNLI 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/mcontriever-msmarco-xnli")
```

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

```python
sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
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 = ["politics", "economy", "entertainment", "environment"]
classifier(sequence_to_classify, candidate_labels, multi_label=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/mcontriever-msmarco-xnli"
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 XNLI test sets on 14 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). The metric used is accuracy.

|Datasets|en|ar|bg|de|el|es|fr|ru|sw|th|tr|ur|vi|zh|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|[mcontriever-xnli](https://huggingface.co/mjwong/mcontriever-xnli)|0.820|0.733|0.773|0.774|0.748|0.788|0.781|0.755|0.690|0.690|0.741|0.647|0.766|0.767|
|[mcontriever-msmarco-xnli](https://huggingface.co/mjwong/mcontriever-msmarco-xnli)|0.822|0.731|0.763|0.775|0.752|0.785|0.778|0.749|0.694|0.682|0.738|0.641|0.759|0.768|

### 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

{"language": ["multilingual", "en", "ar", "bg", "de", "el", "es", "fr", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "license": "mit", "datasets": ["xnli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU", "candidate_labels": "politics, economy, entertainment, environment"}], "base_model": "facebook/mcontriever-msmarco", "model-index": [{"name": "mcontriever-msmarco-xnli", "results": []}]}
transformers pytorch safetensors bert text-classification zero-shot-classification multilingual en ar bg de el es fr ru sw th tr ur vi zh dataset:xnli arxiv:2112.09118 base_model:facebook/mcontriever-msmarco license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #zero-shot-classification #multilingual #en #ar #bg #de #el #es #fr #ru #sw #th #tr #ur #vi #zh #dataset-xnli #arxiv-2112.09118 #base_model-facebook/mcontriever-msmarco #license-mit #autotrain_compatible #endpoints_compatible #region-us 

mcontriever-msmarco-xnli
========================


This model is a fine-tuned version of facebook/mcontriever-msmarco on the XNLI 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 XNLI test sets on 14 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). 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: 2


### 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 XNLI test sets on 14 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). 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: 2 ### 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 #multilingual #en #ar #bg #de #el #es #fr #ru #sw #th #tr #ur #vi #zh #dataset-xnli #arxiv-2112.09118 #base_model-facebook/mcontriever-msmarco #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 XNLI test sets on 14 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). 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: 2 ### Framework versions


* Transformers 4.28.1
* Pytorch 1.12.1+cu116
* Datasets 2.11.0
* Tokenizers 0.12.1