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mjwong/e5-base-v2-mnli-anli

sourceHugging Facecc-by-nc-4.0updated 1y agoView on Hugging Face

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e5-base-v2-mnli-anli

This model is a fine-tuned version of intfloat/e5-base-v2 on the glue (mnli) and anli 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:

python

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.

python

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:

python

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:

python

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)

Eval results

The model was 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
e5-base-v2-mnli-anli	0.812	0.809	0.557	0.460	0.448
e5-large-mnli	0.868	0.869	0.301	0.296	0.294
e5-large-mnli-anli	0.843	0.848	0.646	0.484	0.458
e5-large-v2-mnli	0.875	0.876	0.354	0.298	0.313
e5-large-v2-mnli-anli	0.846	0.848	0.638	0.474	0.479

Training hyperparameters

The following hyperparameters were used during training:

—learning_rate: 2e-05
—trainbatchsize: 16
—evalbatchsize: 16
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.1

Framework versions

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