Multilingual E5 Base Xnli

🚀 multilingual-e5-base-xnli

This model is a fine - tuned version of intfloat/multilingual-e5-base on the XNLI dataset, which can be used for zero - shot classification tasks across multiple languages.

🚀 Quick Start

This model is a fine - tuned version of intfloat/multilingual-e5-base on the XNLI dataset.

✨ Features

• Multilingual Support: It supports multiple languages including 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), Vietnamese (vi), and Chinese (zh).
• Zero - Shot Classification: Can be used with the zero - shot - classification pipeline to classify sequences into user - specified class names.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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 - base - 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)

Advanced Usage

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 - base - 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)

📚 Documentation

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

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.

The model was also evaluated using the dev sets for MultiNLI and test sets for ANLI. 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: 1

Framework versions

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

🔧 Technical Details

The model is based on the pre - trained intfloat/multilingual - e5 - base model and fine - tuned on the XNLI dataset. The paper Text Embeddings by Weakly - Supervised Contrastive Pre - training provides more theoretical basis.

📄 License

This project is licensed under the MIT license.