Datasets:

PhilipMay

/

stsb_multi_mt

like 63

Hello @PhilipMay !

Pull Request overview

• Add the sentence-transformers tag.

Details

The upcoming Sentence Transformers v3 update will introduce training directly with Dataset instances, e.g. like so:

from datasets import load_dataset
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
from sentence_transformers.losses import MultipleNegativesRankingLoss

# 1. Load a model to finetune
model = SentenceTransformer("microsoft/mpnet-base")

# 2. Load a dataset to finetune on
dataset = load_dataset("sentence-transformers/all-nli", "pair")
train_dataset = dataset["train"]
eval_dataset = dataset["dev"]

# 3. Define a loss function
loss = MultipleNegativesRankingLoss(model)

# 4. Create a trainer & train
trainer = SentenceTransformerTrainer(
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss=loss,
)
trainer.train()

# 5. Save the trained model
model.save_pretrained("models/mpnet-base-all-nli")

In preparation for the release, I'm going through and tagging some excellent datasets that immediately match one of the dataset formats required for one of the loss functions as sentence-transformers. Then I can link to datasets with this tag in the Sentence Transformers documentation.

This dataset in particular matches the (sentence_A, sentence_B) pairs with a similarity score format, allowing this dataset to be used out of the box for CosineSimilarityLoss, AnglELoss, CoSENTLoss.

• Tom Aarsen