đ multilingual-e5-large-xnli
This model is a fine - tuned version of intfloat/multilingual-e5-large on the XNLI dataset, enabling zero - shot classification across multiple languages.
đ Quick Start
This model can be used for zero - shot classification tasks. You can load the model with the zero - shot - classification
pipeline or use it manually with PyTorch.
⨠Features
- Multilingual Support: Supports multiple languages such as English, Arabic, Bulgarian, etc.
- Zero - Shot Classification: Can classify sequences into user - specified classes without prior training on those classes.
đĻ Installation
No specific installation steps are provided in the original document.
đģ Usage Examples
Basic Usage
With the zero - shot classification pipeline
The model can be loaded with the zero - shot - classification
pipeline:
from transformers import pipeline
classifier = pipeline("zero - shot - classification",
model="mjwong/multilingual - e5 - large - xnli")
You can then use this pipeline to classify sequences into any of the class names you specify:
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:
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:
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "mjwong/multilingual - e5 - large - 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)
đ Documentation
Eval results
The model was evaluated using the XNLI test sets on 15 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Hindi (hi), 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 |
hi |
ru |
sw |
th |
tr |
ur |
vi |
zh |
[multilingual - e5 - base - xnli](https://huggingface.co/mjwong/multilingual - e5 - base - xnli) |
0.849 |
0.768 |
0.803 |
0.800 |
0.792 |
0.809 |
0.805 |
0.738 |
0.782 |
0.728 |
0.756 |
0.766 |
0.713 |
0.787 |
0.785 |
[multilingual - e5 - base - xnli - anli](https://huggingface.co/mjwong/multilingual - e5 - base - xnli - anli) |
0.811 |
0.711 |
0.751 |
0.759 |
0.746 |
0.778 |
0.765 |
0.685 |
0.728 |
0.662 |
0.705 |
0.716 |
0.683 |
0.736 |
0.740 |
[multilingual - e5 - large - xnli](https://huggingface.co/mjwong/multilingual - e5 - large - xnli) |
0.867 |
0.791 |
0.832 |
0.825 |
0.823 |
0.837 |
0.824 |
0.778 |
0.806 |
0.749 |
0.787 |
0.793 |
0.738 |
0.813 |
0.808 |
[multilingual - e5 - large - xnli - anli](https://huggingface.co/mjwong/multilingual - e5 - large - xnli - anli) |
0.865 |
0.765 |
0.811 |
0.811 |
0.795 |
0.823 |
0.816 |
0.743 |
0.785 |
0.713 |
0.765 |
0.774 |
0.706 |
0.788 |
0.787 |
[multilingual - e5 - large - instruct - xnli](https://huggingface.co/mjwong/multilingual - e5 - large - instruct - xnli) |
0.864 |
0.793 |
0.839 |
0.821 |
0.824 |
0.837 |
0.823 |
0.770 |
0.810 |
0.744 |
0.784 |
0.791 |
0.716 |
0.807 |
0.807 |
[multilingual - e5 - large - instruct - xnli - anli](https://huggingface.co/mjwong/multilingual - e5 - large - instruct - xnli - anli) |
0.861 |
0.780 |
0.816 |
0.808 |
0.806 |
0.825 |
0.816 |
0.758 |
0.799 |
0.727 |
0.775 |
0.780 |
0.721 |
0.787 |
0.795 |
The model was also 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 |
[multilingual - e5 - base - xnli](https://huggingface.co/mjwong/multilingual - e5 - base - xnli) |
0.835 |
0.837 |
0.287 |
0.276 |
0.301 |
[multilingual - e5 - base - xnli - anli](https://huggingface.co/mjwong/multilingual - e5 - base - xnli - anli) |
0.814 |
0.811 |
0.588 |
0.437 |
0.439 |
[multilingual - e5 - large - xnli](https://huggingface.co/mjwong/multilingual - e5 - large - xnli) |
0.865 |
0.865 |
0.312 |
0.316 |
0.300 |
[multilingual - e5 - large - xnli - anli](https://huggingface.co/mjwong/multilingual - e5 - large - xnli - anli) |
0.863 |
0.863 |
0.623 |
0.456 |
0.455 |
[multilingual - e5 - large - instruct - xnli](https://huggingface.co/mjwong/multilingual - e5 - large - instruct - xnli) |
0.867 |
0.866 |
0.341 |
0.330 |
0.323 |
[multilingual - e5 - large - instruct - xnli - anli](https://huggingface.co/mjwong/multilingual - e5 - large - instruct - xnli - anli) |
0.862 |
0.862 |
0.615 |
0.459 |
0.462 |
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: 1
Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.12.1
đ§ Technical Details
The model is based on the pre - trained model [intfloat/multilingual - e5 - large](https://huggingface.co/intfloat/multilingual - e5 - large) and fine - tuned on the XNLI dataset. It uses techniques from Text Embeddings by Weakly - Supervised Contrastive Pre - training.
đ License
This project is licensed under the MIT license.