content.raw: I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details:
content.type: text
content.value: I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details:
content.type: new_line
content.type: new_line
content.raw: 1️⃣ Gradient checkpointing allows for much less memory usage at a cost of ~20% training speed. Seems to allow for higher batch sizes, which is quite important for loss functions with in-batch negatives. 
content.type: text
content.value: 1️⃣ Gradient checkpointing allows for much less memory usage at a cost of ~20% training speed. Seems to allow for higher batch sizes, which is quite important for loss functions with in-batch negatives. 
content.type: new_line
content.raw: 2️⃣ You can specify 
content.type: text
content.value: 2️⃣ You can specify 
content.code: args.push_to_hub=True
content.raw: `args.push_to_hub=True`
content.type: inline_code
content.raw:  and 
content.type: text
content.value:  and 
content.code: args.hub_model_id
content.raw: `args.hub_model_id`
content.type: inline_code
content.raw:  to upload your model checkpoints to Hugging Face while training. It also uploads your emissions (if codecarbon is installed) and your Tensorboard logs (if tensorboard is installed)
content.type: text
content.value:  to upload your model checkpoints to Hugging Face while training. It also uploads your emissions (if codecarbon is installed) and your Tensorboard logs (if tensorboard is installed)
content.type: new_line
content.raw: 3️⃣ Model card improvements: improved automatic widget examples, better tags, and the default of "sentence_transformers_model_id" now gets replaced when possible.
content.type: text
content.value: 3️⃣ Model card improvements: improved automatic widget examples, better tags, and the default of "sentence_transformers_model_id" now gets replaced when possible.
content.type: new_line
content.raw: 4️⃣ Several evaluator fixes, see release notes for details.
content.type: text
content.value: 4️⃣ Several evaluator fixes, see release notes for details.
content.type: new_line
content.raw: 5️⃣ Fixed a bug with MatryoshkaLoss throwing an error if the supplied Matryoshka dimensions are ascending instead of descending.
content.type: text
content.value: 5️⃣ Fixed a bug with MatryoshkaLoss throwing an error if the supplied Matryoshka dimensions are ascending instead of descending.
content.type: new_line
content.raw: 6️⃣ Full Safetensors support; even the uncommon modules can now save and load "model.safetensors" files: no more pickle risks.
content.type: text
content.value: 6️⃣ Full Safetensors support; even the uncommon modules can now save and load "model.safetensors" files: no more pickle risks.
content.type: new_line
content.type: new_line
content.raw: Check out the full release notes here: 
content.type: text
content.value: Check out the full release notes here: 
content.href: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.1
content.raw: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.1
content.type: link
content.type: new_line
content.type: new_line
content.raw: And let me know what kind of features you'd like to see next! I have some plans already (ONNX, Sparse models, ColBERT, PEFT), but I don't yet know how I should prioritize everything.
content.type: text
content.value: And let me know what kind of features you'd like to see next! I have some plans already (ONNX, Sparse models, ColBERT, PEFT), but I don't yet know how I should prioritize everything.
rawContent: I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details:
  
  1️⃣ Gradient checkpointing allows for much less memory usage at a cost of ~20% training speed. Seems to allow for higher batch sizes, which is quite important for loss functions with in-batch negatives. 
  2️⃣ You can specify `args.push_to_hub=True` and `args.hub_model_id` to upload your model checkpoints to Hugging Face while training. It also uploads your emissions (if codecarbon is installed) and your Tensorboard logs (if tensorboard is installed)
  3️⃣ Model card improvements: improved automatic widget examples, better tags, and the default of "sentence_transformers_model_id" now gets replaced when possible.
  4️⃣ Several evaluator fixes, see release notes for details.
  5️⃣ Fixed a bug with MatryoshkaLoss throwing an error if the supplied Matryoshka dimensions are ascending instead of descending.
  6️⃣ Full Safetensors support; even the uncommon modules can now save and load "model.safetensors" files: no more pickle risks.
  
  Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.1
  
  And let me know what kind of features you'd like to see next! I have some plans already (ONNX, Sparse models, ColBERT, PEFT), but I don't yet know how I should prioritize everything.
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commentators.fullname: Pavan Satish
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commentators.fullname: Aaron Chibb
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