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RTIInternational / SMART

Smarter Manual Annotation for Resource-constrained collection of Training data
MIT License
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Bump sentence-transformers from 2.1.0 to 2.2.2 in /backend/docker #267

Closed dependabot[bot] closed 9 months ago

dependabot[bot] commented 11 months ago

Bumps sentence-transformers from 2.1.0 to 2.2.2.

Release notes

Sourced from sentence-transformers's releases.

v2.2.1 - Update huggingface_hub & fixes

Version 0.8.1 of huggingface_hub introduces several changes that resulted in errors and warnings. This version of sentence-transformers fixes these issues.

Further, several improvements have been added / merged:

  • util.community_detection was improved: 1) It works in a batched mode to save memory, 2) Overlapping clusters are no longer dropped but removed by overlapping items, 3) The parameter init_max_size was removed and replaced by a heuristic to estimate the max size of clusters
  • #1581 the training dataset names can be saved in the model card
  • #1426 fix the text summarization example
  • #1487 Rekursive sentence-transformers models are now possible
  • #1522 Private models can now be loaded
  • #1551 DataLoaders can now have workers
  • #1565 Models are just checked on the hub if they don't exist in the cache. Fixes issues with connectivity issues
  • #1591 Example added how to stream encode larger datasets

v2.2.0 - T5 Encoder & Private models

T5

You can now use the encoder from T5 to learn text embeddings. You can use it like any other transformer model:

from sentence_transformers import SentenceTransformer, models
word_embedding_model = models.Transformer('t5-base', max_seq_length=256)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])

See T5-Benchmark results - the T5 encoder is not the best model for learning text embeddings models. It requires quite a lot of training data and training steps. Other models perform much better, at least in the given experiment with 560k training triplets.

New Models

The models from the papers Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models and Large Dual Encoders Are Generalizable Retrievers have been added:

For benchmark results, see https://seb.sbert.net

Private Models

Thanks to #1406 you can now load private models from the hub:

model = SentenceTransformer("your-username/your-model", use_auth_token=True)
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dependabot[bot] commented 9 months ago

OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.