nickprock/AIRC_FAQ
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Nicola Procopio
nickprock
AI & ML interests
Digital Health
Semantic Search - Vector Search
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nickprock/cross-encoder-italian-bert-stsb:Adding `safetensors` variant of this model
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nickprock/mmarco-bert-base-italian-uncased:Adding `safetensors` variant of this model
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about 1 month ago
nickprock/sentence-bert-base-italian-uncased:Adding `safetensors` variant of this model
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Adding `safetensors` variant of this model
#1 opened 28 days ago
by
SFconvertbot
SFconvertbot
Adding `safetensors` variant of this model
#1 opened about 1 month ago
by
SFconvertbot
SFconvertbot
Adding `safetensors` variant of this model
#1 opened about 1 month ago
by
SFconvertbot
SFconvertbot
updated
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dataset
about 1 month ago
upvoted
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about 1 month ago
Set `library_name` to `tf-keras`.
1
#1 opened 5 months ago
by
Wauplin
Wauplin
updated
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4 months ago
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5 months ago
Reacted to
anakin87's
post with π₯π€
5 months ago
Post
1649
π Creating adventures with local LLMs
What if π€... Homer Simpson met Spider-Man and they went on a quest for donuts? π©
Or if Fred Astaire and Corporal Hicks teamed up to fight xenomorphs? πΎ
In the words of Karpathy, LLMs are dream machines...
they seem specially made to simulate these wild scenarios!
ππ±π©ππ«π’π¦ππ§ππ’π§π π°π’ππ‘ ππ‘π’π¬ π’πππ π
Nous Research / @teknium recently released NousResearch/CharacterCodex:
a massive dataset with information on 16k characters, both fictional and real.
I couldn't wait to play it...
After a few attempts, I found that combining the information in this dataset with a good model (like meta-llama/Meta-Llama-3-8B-Instruct) opens the doors to a myriad of chat adventures.
π οΈ Stack:
πΉHaystack for orchestration ποΈ
πΉllamafile π¦ποΈ to run our model locally.
π Check out the notebook: https://t.ly/y6jrZ
(includes a bonus π΅οΈ Mystery Character Quiz)
What if π€... Homer Simpson met Spider-Man and they went on a quest for donuts? π©
Or if Fred Astaire and Corporal Hicks teamed up to fight xenomorphs? πΎ
In the words of Karpathy, LLMs are dream machines...
they seem specially made to simulate these wild scenarios!
ππ±π©ππ«π’π¦ππ§ππ’π§π π°π’ππ‘ ππ‘π’π¬ π’πππ π
Nous Research / @teknium recently released NousResearch/CharacterCodex:
a massive dataset with information on 16k characters, both fictional and real.
I couldn't wait to play it...
After a few attempts, I found that combining the information in this dataset with a good model (like meta-llama/Meta-Llama-3-8B-Instruct) opens the doors to a myriad of chat adventures.
π οΈ Stack:
πΉHaystack for orchestration ποΈ
πΉllamafile π¦ποΈ to run our model locally.
π Check out the notebook: https://t.ly/y6jrZ
(includes a bonus π΅οΈ Mystery Character Quiz)
liked
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5 months ago
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6 months ago
Article
Training and Finetuning Embedding Models with Sentence Transformers v3
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6 months ago
Reacted to
davanstrien's
post with π§
6 months ago
Post
1808
Do you need a high-quality dataset to train a custom sentence transformer model? Look no further! I've developed a pipeline that leverages an LLM to create a synthetic dataset of negative and positive sentence pairs based on domain-specific anchors.
Here's what the pipeline offers:
- **Dataset Generation**: Automatically create synthetic sentence pairs
- **Mine hard negatives**: Use an existing embedding model to mine hard negatives
- **Model Training**: Train a model using the latest release of Sentence Transformers.
Check out this collection ( davanstrien/sentence-transformers-from-synthetic-data-66571a6133480d1b70066b70) to see an example of what you can achieve with this pipeline. It features a sentence transformer model to detect coding prompt similarities in a @bigcode dataset.
Excited to get started? Find a tutorial here: https://github.com/davanstrien/awesome-synthetic-datasets/tree/main/examples/embedding-datasets.
Here's what the pipeline offers:
- **Dataset Generation**: Automatically create synthetic sentence pairs
- **Mine hard negatives**: Use an existing embedding model to mine hard negatives
- **Model Training**: Train a model using the latest release of Sentence Transformers.
Check out this collection ( davanstrien/sentence-transformers-from-synthetic-data-66571a6133480d1b70066b70) to see an example of what you can achieve with this pipeline. It features a sentence transformer model to detect coding prompt similarities in a @bigcode dataset.
Excited to get started? Find a tutorial here: https://github.com/davanstrien/awesome-synthetic-datasets/tree/main/examples/embedding-datasets.
- 1 reply
Reacted to
tomaarsen's
post with π₯
6 months ago
Post
1942
βΌοΈSentence Transformers v3.0 is out! You can now train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also release 50+ datasets to train on.
1οΈβ£ Training Refactor
Embedding models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Improved model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-sentence-transformers
2οΈβ£ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use
3οΈβ£ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!
4οΈβ£ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5οΈβ£ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
1οΈβ£ Training Refactor
Embedding models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Improved model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-sentence-transformers
2οΈβ£ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use
model.similarity(embeddings1, embeddings2) and you'll get your similarity scores immediately. Model authors can specify their desired similarity score, so you don't have to worry about it anymore!3οΈβ£ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!
4οΈβ£ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5οΈβ£ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
liked
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6 months ago
updated
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6 months ago