Text Classification
Transformers
ONNX
Safetensors
English
reflex_decision
feature-extraction
dual-encoder
multiple-choice
dynamic-labels
research
custom_code
reflex
Eval Results (legacy)
Instructions to use gai-labs/reflex-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gai-labs/reflex-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gai-labs/reflex-1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gai-labs/reflex-1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download source_terms.json from gai-labs/reflex-1: direct link, hf CLI and curl.
- Browser
- Download file 2.73 kB
-
https://huggingface.co/gai-labs/reflex-1/resolve/main/source_terms.json
- Command line
-
hf download hf://gai-labs/reflex-1/source_terms.json
-
curl -L -o source_terms.json https://huggingface.co/gai-labs/reflex-1/resolve/main/source_terms.json
2.73 kB
| { | |
| "created_utc": "2026-10-02T23:13:59.693434+00:00", | |
| "scope": "Source terms and empirical training provenance only; no determination of the terms applicable to model weights.", | |
| "sources": { | |
| "race": { | |
| "card_sha256": "85af31bd82cc5738676e189be1f83b86a31d74c9870ed31516c4966b560af41b", | |
| "url": "https://huggingface.co/datasets/ehovy/race/blob/2fec9fd81f1dc971569a9b729c43f2f0e6436637/README.md", | |
| "license_metadata": [ | |
| "other" | |
| ], | |
| "primary_homepage": "https://www.cs.cmu.edu/~glai1/data/race/", | |
| "source_term_excerpt": "available for non-commercial research purpose only", | |
| "primary_note_summary": "The author site also excludes commercial exploitation of contexts and derived data. This report does not classify model weights as derived data." | |
| }, | |
| "sciq": { | |
| "card_sha256": "f16f71b220a0e205672f6f0c8afb40e37f7a158541f759593745fe52162f8ad8", | |
| "url": "https://huggingface.co/datasets/allenai/sciq/blob/2c94ad3e1aafab77146f384e23536f97a4849815/README.md", | |
| "license_metadata": [ | |
| "cc-by-nc-3.0" | |
| ], | |
| "license_link": "https://creativecommons.org/licenses/by-nc/3.0/", | |
| "source_term_name": "Creative Commons Attribution-NonCommercial 3.0 Unported" | |
| }, | |
| "mnli": { | |
| "card_sha256": "e9bafc4de652caab3fb991ef3f1eded3b2385b7e1b2d1df2fd70523c376566fe", | |
| "url": "https://huggingface.co/datasets/nyu-mll/multi_nli/blob/da70db2af9d09693783c3320c4249840212ee221/README.md", | |
| "license_metadata": [ | |
| "cc-by-3.0", | |
| "cc-by-sa-3.0", | |
| "mit", | |
| "other" | |
| ], | |
| "license_details": "Open Portion of the American National Corpus", | |
| "source_summary": "Card describes OANC terms for most material and multiple terms for fiction." | |
| } | |
| }, | |
| "released_base": { | |
| "weights_sha256": "b19dbd8c6c260615f855602eae0b291f7b088f73366d10495ea7b9ae61607fed", | |
| "source_checkpoint_sha256": "f91da714e639a0740aa1143945f3668c033114582a815c89c1f99678381cb96e", | |
| "selected_step": 6000, | |
| "declared_license": "apache-2.0", | |
| "race_and_sciq_used_for_training": true, | |
| "training_steps_by_source": { | |
| "race": 748, | |
| "sciq": 473 | |
| }, | |
| "question_presentations": { | |
| "race": 23995, | |
| "sciq": 15181 | |
| }, | |
| "projection_fit_source_specific_option_strings": { | |
| "race": 26319, | |
| "sciq": 3487 | |
| }, | |
| "trainer_permuted_options": true, | |
| "training_source_hash_matches_original_protocol": true | |
| }, | |
| "lineage_audit_sha256": "7e587ab2c233490ae908b61447d8806d43a3433d222c87e870b50d14217b9416", | |
| "archive_receipt_files_verified": 11, | |
| "review_status": "Training-source facts verified; the application of these terms to model weights has not been determined by this audit." | |
| } | |