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Reason: \n\nVersion: 1.11.5, build: dee106d7\nAccess web UI at http://localhost:6333/dashboard\n\n2024-10-19T03:01:33.540257Z INFO storage::content_manager::consensus::persistent: Loading raft state from /data/storage/raft_state.json \n2024-10-19T03:01:33.544251Z DEBUG storage::content_manager::consensus::persistent: State: Persistent { state: RaftState { hard_state: HardState { term: 0, vote: 0, commit: 0 }, conf_state: ConfState { voters: [2387245629298653], learners: [], voters_outgoing: [], learners_next: [], auto_leave: false } }, latest_snapshot_meta: SnapshotMetadataSer { term: 0, index: 0 }, apply_progress_queue: EntryApplyProgressQueue(None), peer_address_by_id: RwLock { data: {} }, peer_metadata_by_id: RwLock { data: {} }, cluster_metadata: {}, this_peer_id: 2387245629298653, path: \"/data/storage/raft_state.json\", dirty: false } \n2024-10-19T03:01:33.557278Z INFO storage::content_manager::toc: Loading collection: song_sheets \n2024-10-19T03:01:33.801333Z ERROR 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Increasingly, LLMs are becoming very useful for helping scale annotation tasks, i.e. labelling and filtering. When combined with the structured generation, this can be a very scalable way of doing some pre-annotation without requiring a large team of human annotators.

However, there are quite a few cases where it still doesn't work well. This is a nice paper looking at the limitations of LLM as an annotator for Low Resource Languages: On Limitations of LLM as Annotator for Low Resource Languages (2411.17637).

Humans will still have an important role in the loop to help improve models for all languages (and domains).

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First dataset for the new Hugging Face Bluesky community organisation: bluesky-community/one-million-bluesky-posts 🦋

📊 1M public posts from Bluesky's firehose API
🔍 Includes text, metadata, and language predictions
🔬 Perfect to experiment with using ML for Bluesky 🤗

Excited to see people build more open tools for a more open social media platform!

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synthetic-data-generation-demos

A collection of demos for various approaches to synthetic data generation

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sentence-transformers-from-synthetic-data

Example of using distilabel to generate synthetic triplets data for fine-tuning a Sentence Transformer model

• bigcode/self-oss-instruct-sc2-exec-filter-50k

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  davanstrien/code-prompt-similarity-model

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arxiv:2211.10086

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davanstrien/fineweb-edu-classifier-onnx

Text Classification • Updated 15 days ago • 17

davanstrien/Qwen2.5-0.5B-Instruct-extra-token

Text Generation • Updated Oct 31 • 9

davanstrien/document-classifier-convnextv2-tiny-22k-224

Image Classification • Updated Oct 23 • 9

davanstrien/document-classifier-convnextv2-tiny-1k-224

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davanstrien/document-classifier-deit_base_patch16_224

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davanstrien/contrastive-bert-base-uncased

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davanstrien/convnextv2-tiny-1k-224-text

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davanstrien/fineweb-edu-llama3-annotations-sample-5-ratings-100-raw

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Viewer • Updated 17 days ago • 100 • 58

davanstrien/fineweb-edu-llama3-annotations-pairs-data-sample-ranked-raw

Viewer • Updated 21 days ago • 248 • 40

davanstrien/fineweb-edu-llama3-annotations-pairs-data

Viewer • Updated 21 days ago • 248 • 35

davanstrien/would-you-read-it

Viewer • Updated 21 days ago • 266 • 175 • 4

davanstrien/exaple_audio_image_text_triplet

Viewer • Updated Oct 30 • 1 • 43

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