language:
- multilingual
- en
- ar
- bg
- de
- el
- es
- fr
- hi
- 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: intfloat/multilingual-e5-large
model-index:
- name: multilingual-e5-large-xnli
results: []
multilingual-e5-large-xnli
This model is a fine-tuned version of intfloat/multilingual-e5-large on the XNLI dataset.
Model description
Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
How to use the model
With the zero-shot classification pipeline
The model can be loaded with the zero-shot-classification pipeline like so:
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 like so:
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/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)
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 | 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 | 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 | 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 | 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 |
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 | 0.835 | 0.837 | 0.287 | 0.276 | 0.301 |
| multilingual-e5-base-xnli-anli | 0.814 | 0.811 | 0.588 | 0.437 | 0.439 |
| multilingual-e5-large-xnli | 0.865 | 0.865 | 0.312 | 0.316 | 0.300 |
| multilingual-e5-large-xnli-anli | 0.863 | 0.863 | 0.623 | 0.456 | 0.455 |
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