tomaarsen HF staff commited on
Commit
c9b6360
1 Parent(s): 5ce5373

Add 'sentence-transformers' tag for easier discoverability

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Hello!

## 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:

```python
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("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](https://sbert.net/docs/training/loss_overview.html) as [`sentence-transformers`](https://huggingface.co/datasets?other=sentence-transformers). Then I can link to datasets with this tag in the Sentence Transformers documentation.

This dataset in particular matches the `(anchor, positive) pairs` without any label, allowing this dataset to be used out of the box for CachedMultipleNegativesRankingLoss, MultipleNegativesRankingLoss, MultipleNegativesSymmetricRankingLoss, MegaBatchMarginLoss, CachedGISTEmbedLoss, and GISTEmbedLoss.

- Tom Aarsen

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  1. README.md +2 -0
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  - question-answering
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  task_ids:
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  - closed-domain-qa
 
 
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  ---
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  # Dataset Card Creation Guide
 
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  - question-answering
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  task_ids:
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  - closed-domain-qa
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+ tags:
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+ - sentence-transformers
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  ---
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  # Dataset Card Creation Guide