Multilingual MiniLMv2 L12 Mnli Xnli

🚀 Multilingual MiniLMv2-L12-mnli-xnli

This multilingual model can perform natural language inference (NLI) on over 100 languages, making it ideal for multilingual zero-shot classification. It offers a balance between performance and efficiency, with various use-cases and training details to explore.

🚀 Quick Start

Simple zero-shot classification pipeline

from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/multilingual-MiniLMv2-L12-mnli-xnli")

sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)

NLI use-case

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model_name = "MoritzLaurer/multilingual-MiniLMv2-L12-mnli-xnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
hypothesis = "Emmanuel Macron is the President of France"

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

✨ Features

• Multilingual Support: Capable of performing natural language inference (NLI) on 100+ languages, suitable for multilingual zero-shot classification.
• Distilled Model: Smaller than its teacher model (XLM-RoBERTa-large), offering faster inference and lower memory requirements.
• Fine-tuned on Quality Data: Trained on the XNLI development dataset and the MNLI train dataset, using professional translations to avoid quality issues.

📦 Installation

No specific installation steps are provided in the original README. If you want to use this model, you can install the necessary libraries such as transformers and torch using pip:

pip install transformers torch

💻 Usage Examples

Basic Usage

from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/multilingual-MiniLMv2-L12-mnli-xnli")

sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)

Advanced Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model_name = "MoritzLaurer/multilingual-MiniLMv2-L12-mnli-xnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
hypothesis = "Emmanuel Macron is the President of France"

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

📚 Documentation

Model description

This multilingual model can perform natural language inference (NLI) on 100+ languages and is therefore also suitable for multilingual zero-shot classification. The underlying multilingual-MiniLM-L12 model was created by Microsoft and was distilled from XLM-RoBERTa-large (see details in the original paper and newer information in this repo). The model was then fine-tuned on the XNLI dataset, which contains hypothesis-premise pairs from 15 languages, as well as the English MNLI dataset.

The main advantage of distilled models is that they are smaller (faster inference, lower memory requirements) than their teachers (XLM-RoBERTa-large). The disadvantage is that they lose some of the performance of their larger teachers.

For highest inference speed, I recommend using the 6-layer model (the model on this page has 12 layers and is slower). For higher performance I recommend mDeBERTa-v3-base-mnli-xnli (as of 14.02.2023).

Training data

This model was trained on the XNLI development dataset and the MNLI train dataset. The XNLI development set consists of 2490 professionally translated texts from English to 14 other languages (37350 texts in total) (see this paper). Note that the XNLI contains a training set of 15 machine translated versions of the MNLI dataset for 15 languages, but due to quality issues with these machine translations, this model was only trained on the professional translations from the XNLI development set and the original English MNLI training set (392 702 texts). Not using machine translated texts can avoid overfitting the model to the 15 languages; avoids catastrophic forgetting of the other languages it was pre-trained on; and significantly reduces training costs.

Training procedure

The model was trained using the Hugging Face trainer with the following hyperparameters. The exact underlying model is mMiniLMv2-L12-H384-distilled-from-XLMR-Large.

training_args = TrainingArguments(
    num_train_epochs=3,              # total number of training epochs
    learning_rate=4e-05,
    per_device_train_batch_size=64,   # batch size per device during training
    per_device_eval_batch_size=120,    # batch size for evaluation
    warmup_ratio=0.06,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
)

Eval results

The model was evaluated on the XNLI test set on 15 languages (5010 texts per language, 75150 in total). Note that multilingual NLI models are capable of classifying NLI texts without receiving NLI training data in the specific language (cross-lingual transfer). This means that the model is also able of doing NLI on the other languages it was training on, but performance is most likely lower than for those languages available in XNLI.

The average XNLI performance of multilingual-MiniLM-L12 reported in the paper is 0.711 (see table 11). This reimplementation has an average performance of 0.75. This increase in performance is probably thanks to the addition of MNLI in the training data and this model was distilled from XLM-RoBERTa-large instead of -base (multilingual-MiniLM-L12-v2).

|Datasets|avg_xnli|ar|bg|de|el|en|es|fr|hi|ru|sw|th|tr|ur|vi|zh| | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |Accuracy|0.75|0.73|0.78|0.762|0.754|0.821|0.779|0.775|0.724|0.76|0.689|0.738|0.732|0.7|0.762|0.751| |Speed text/sec (A100 GPU, eval_batch=120)|4535.0|4629.0|4417.0|4500.0|3938.0|4959.0|4634.0|4152.0|4190.0|4368.0|4630.0|4698.0|4929.0|4291.0|4420.0|5275.0|

🔧 Technical Details

• Model Type: Multilingual MiniLMv2-L12 distilled from XLM-RoBERTa-large, fine-tuned for NLI and zero-shot classification.
• Training Data: XNLI development dataset (professional translations) and MNLI train dataset.
• Training Hyperparameters:
  • num_train_epochs: 3
  • learning_rate: 4e-05
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 120
  • warmup_ratio: 0.06
  • weight_decay: 0.01

📄 License

This project is licensed under the MIT license.

Limitations and bias

Please consult the original paper and literature on different NLI datasets for potential biases.

Citation

If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k.

Ideas for cooperation or questions?

If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or LinkedIn