E5 Base V2 Mnli Anli

🚀 e5-base-v2-mnli-anli

This model is a fine - tuned version of intfloat/e5-base-v2 for zero - shot classification, enhancing performance on glue (mnli) and anli datasets.

✨ Features

• Based on the pre - trained model intfloat/e5-base-v2, fine - tuned on glue (mnli) and anli datasets.
• Suitable for zero - shot classification tasks and can also be used for NLI tasks.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

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/e5-base-v2-mnli-anli")

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)

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)

Advanced Usage

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/e5-base-v2-mnli-anli"
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 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 4.28.1
• Pytorch 1.12.1+cu116
• Datasets 2.11.0
• Tokenizers 0.12.1

📄 License

This model is released under the MIT license.