Gte Multilingual Base Xnli Anli

🚀 gte-multilingual-base-xnli-anli

This model is a fine - tuned version of [Alibaba - NLP/gte - multilingual - base](https://huggingface.co/Alibaba - NLP/gte - multilingual - base) on the XNLI and ANLI dataset. It is designed for zero - shot classification tasks, providing a powerful tool for classifying text in multiple languages.

✨ Features

• Multilingual Support: Supports 15 languages including English, Arabic, Bulgarian, etc.
• Fine - Tuned on Datasets: Fine - tuned on XNLI and ANLI datasets for better performance.
• Zero - Shot Classification: Can be used for zero - shot classification tasks out - of - the - box.

📦 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 AutoTokenizer, pipeline
model = "mjwong/gte - multilingual - base - xnli - anli"
tokenizer = AutoTokenizer.from_pretrained(model)
classifier = pipeline("zero - shot - classification",
                      model=model,
                      tokenizer=tokenizer,
                      trust_remote_code=True
                      )

You can then use this pipeline to classify sequences into any of the class names you specify.

sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)

Advanced Usage

If more than one candidate label can be correct, pass multi_class=True to calculate each class independently:

candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_class=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/gte - multilingual - base - xnli - anli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)

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

mGTE: Generalized Long - Context Text Representation and Reranking Models for Multilingual Text Retrieval. Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang, arXiv 2024

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

Framework versions

• Transformers 4.41.0
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.19.1

📄 License

This model is licensed under the Apache - 2.0 license.