chore(deps): bump sentence-transformers from 2.7.0 to 3.0.0

[dependabot]

Bumps sentence-transformers from 2.7.0 to 3.0.0.

Release notes

Sourced from sentence-transformers's releases.

v3.0.0 - Sentence Transformer Training Refactor; new similarity methods; hyperparameter optimization; 50+ datasets release

This release consists of a major refactor that overhauls the training approach (introducing multi-gpu training, bf16, loss logging, callbacks, and much more), adds convenient similarity and similarity_pairwise methods, adds extra keyword arguments, introduces Hyperparameter Optimization, and includes a massive reformatting and release of 50+ datasets for training embedding models. In total, this is the largest Sentence Transformers update since the project was first created.

Install this version with

# Full installation:
pip install sentence-transformers[train]==3.0.0
Inference only:
pip install sentence-transformers==3.0.0

Sentence Transformer training refactor (#2449)

The v3.0 release centers around this huge modernization of the training approach for SentenceTransformer models. Whereas training before v3.0 used to be all about InputExample, DataLoader and model.fit, the new training approach relies on 5 new components. You can learn more about these components in our Training and Finetuning Embedding Models with Sentence Transformers v3 blogpost. Additionally, you can read the new Training Overview, check out the Training Examples, or read this summary:

1. Dataset A training Dataset or DatasetDict. This class is much more suited for sharing & efficient modifications than lists/DataLoaders of InputExample instances. A Dataset can contain multiple text columns that will be fed in order to the corresponding loss function. So, if the loss expects (anchor, positive, negative) triplets, then your dataset should also have 3 columns. The names of these columns are irrelevant. If there is a "label" or "score" column, it is treated separately, and used as the labels during training. A DatasetDict can be used to train with multiple datasets at once, e.g.:

   DatasetDict({
       multi_nli: Dataset({
           features: ['premise', 'hypothesis', 'label'],
           num_rows: 392702
       })
       snli: Dataset({
           features: ['snli_premise', 'hypothesis', 'label'],
           num_rows: 549367
       })
       stsb: Dataset({
           features: ['sentence1', 'sentence2', 'label'],
           num_rows: 5749
       })
   })

   When a DatasetDict is used, the loss parameter to the SentenceTransformerTrainer must also be a dictionary with these dataset keys, e.g.:

   {
       'multi_nli': SoftmaxLoss(...),
       'snli': SoftmaxLoss(...),
       'stsb': CosineSimilarityLoss(...),
   }

2. Loss Function A loss function, or a dictionary of loss functions like described above. These loss functions do not require changes compared to before this PR.
3. Training Arguments A SentenceTransformerTrainingArguments instance, subclass of a TrainingArguments instance. This powerful class controls the specific details of the training.
4. Evaluator An optional SentenceEvaluator instance. Unlike before, models can now be evaluated both on an evaluation dataset with some loss function and/or a SentenceEvaluator instance.
5. Trainer

... (truncated)

Commits

• ba908be Release v3.0.0
• e55a6d1 Merge v3.0 pre-release into master, prepare for full v3.0 release (#2685)
• 85890d5 Update training overview docs based on the blogpost reviews
• 946a97d Add missing docstrings arguments for Cached... losses
• 649a31c Add missing subset in quora-duplicates
• 2f89fd6 Link directly to the 2DMSE preprint
• 3f5dccb Update phrasing on local_files_only docstring
• 403d188 Link to the yet-to-be-released release notes instead
• a373931 Docs: pairwise_cosine_similarity -> pairwise_similarity
• 24bee09 Fix formatting of Python modules
• Additional commits viewable in compare view

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