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--- |
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datasets: |
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- xnli |
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model-index: |
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- name: mcontriever-xnli |
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results: [] |
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pipeline_tag: zero-shot-classification |
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language: |
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- multilingual |
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- en |
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- ar |
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- bg |
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- de |
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- el |
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- es |
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- fr |
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- ru |
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- sw |
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- th |
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- tr |
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- ur |
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- vi |
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- zh |
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license: mit |
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widget: |
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- text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" |
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candidate_labels: "politics, economy, entertainment, environment" |
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--- |
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# mcontriever-xnli |
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This model is a fine-tuned version of [facebook/mcontriever](https://huggingface.co/facebook/mcontriever) on the XNLI dataset. |
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## Model description |
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[Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). |
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Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave, arXiv 2021 |
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## How to use the model |
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The model can be loaded with the `zero-shot-classification` pipeline like so: |
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```python |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", |
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model="mjwong/mcontriever-xnli") |
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``` |
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You can then use this pipeline to classify sequences into any of the class names you specify. |
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```python |
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sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" |
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candidate_labels = ["politics", "economy", "entertainment", "environment"] |
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classifier(sequence_to_classify, candidate_labels) |
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``` |
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If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently: |
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```python |
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candidate_labels = ["politics", "economy", "entertainment", "environment"] |
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classifier(sequence_to_classify, candidate_labels, multi_label=True) |
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``` |
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### Eval results |
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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. |
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|Datasets|en|ar|bg|de|el|es|fr|ru|sw|th|tr|ur|vi|zh| |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
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|[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| |
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|[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| |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 2 |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 1.12.1+cu116 |
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- Datasets 2.11.0 |
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- Tokenizers 0.12.1 |
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