Instructions to use mayflowergmbh/tractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use mayflowergmbh/tractor with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("mayflowergmbh/tractor") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
Download source-lock.json from mayflowergmbh/tractor: direct link, hf CLI and curl.
- Browser
- Download file 5.16 kB
-
https://huggingface.co/mayflowergmbh/tractor/resolve/main/source-lock.json
- Command line
-
hf download hf://mayflowergmbh/tractor/source-lock.json
-
curl -L -o source-lock.json https://huggingface.co/mayflowergmbh/tractor/resolve/main/source-lock.json
5.16 kB
| { | |
| "release_version": "2026.10.21", | |
| "weights_sha256": "f9b177aa18d1f968cf8e2b3a99cff8938c096be8570064182f84c5a16488b90a", | |
| "gliner_commit": "b86d35c02f2c1a4d9840ea50197bd1b6d13b031e", | |
| "pipeline_sources": { | |
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| "field_contracts.py": "9351fb3745a469632aa5cbbbfed2595e7392e08934e7b595187514d51b574d84", | |
| "field_normalization.py": "cc788dbc2d6879c5aa0127cda0b42ff05f44b3c1ddb1ecfd1392e38cab602026", | |
| "pipeline_evaluation.py": "1c9504f87e7ad02848eebe8402d48c7af46f7c2410dbc4b8b910d4e229aadc13", | |
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| "normalize.py": "8923d46705b9243543661f7ad21807da9937d00942af88f06c8712c25f85bf2e", | |
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| "service.py": "b1b7234f38c309f8289c09060875c13046b825204c587cc4a984fffd568d7710", | |
| "contact_normalization.py": "b9f76034be2d6969aabf34b388f9a2cc6a88ea6882ab00250b670b352616da83", | |
| "temporal_normalization.py": "0e01bf9f884c21927f8fe2f21325c1fda97a30f89a1569ce1dd1a6ee3e6591cc", | |
| "scalar_normalization.py": "6cdc6e130fc3c3bc094a2c5c12788e69ec7b3776d9e81d5fd19fbf6cd8ea54a1", | |
| "identifier_normalization.py": "5602163b9d6d40ca851c3130f9f918205546fba08a541f4941f7d8463e1bbff7", | |
| "address_normalization.py": "a7333c17d3ab2dbd810119cc83c65bf6ad384f226b3423722e192933bfa6f6a3", | |
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| "document_eval.py": "cbe6dfabba39470264e44fd4f819f14ecdb6de77cb6dc0602be0881e1c128008", | |
| "inference_context.py": "c6631350cc2c0fff86e5c943f02eaa36b7fcd2a9be33c23072ccc38db1219c69", | |
| "record_contracts.py": "b4a1b444c9eb84d3eb0be91934fd0ef3144c901b76dfdc9aba17d2e4f137a874", | |
| "evaluation_reference.py": "3b586fba528bcae02a113e9f90fb5825cc85437517df3d9d41e39e2dab449683", | |
| "document_quality.py": "91fc5d0f9e227488367b1a3b89be3013147661e0aa64731c46e1cd961fd65b18" | |
| }, | |
| "dataset": { | |
| "repository": "mayflowergmbh/tractor", | |
| "revision": "b07be20e73f60237ebb5a8aa37be4400a2d92097", | |
| "manifest_sha256": "a6b119cdaea8e247d595c74b466911a05ecad97c65914731843f542d26ce09fd" | |
| }, | |
| "training": { | |
| "epochs_completed": 4, | |
| "optimizer_steps": 42940, | |
| "sample_exposures": 1374008, | |
| "unique_train_episodes": 341237, | |
| "native_train_requests": 343502, | |
| "selected_epoch": 3, | |
| "selected_step": 32205 | |
| }, | |
| "promotion": { | |
| "authorized_date": "2026-10-21", | |
| "decision": "Promote the best full-training generalist with disclosed regressions.", | |
| "strict_release_gates_passed": false, | |
| "regression_checks_passed": false, | |
| "failed_regression_checks": 12 | |
| }, | |
| "checkpoint_selection_pipeline_sources": { | |
| "document_pipeline.py": "75823c89ba55f8fb34ba4daab7ee3da0a4167fa0f20d502371b10593d3697a27", | |
| "field_contracts.py": "9351fb3745a469632aa5cbbbfed2595e7392e08934e7b595187514d51b574d84", | |
| "field_normalization.py": "cc788dbc2d6879c5aa0127cda0b42ff05f44b3c1ddb1ecfd1392e38cab602026", | |
| "pipeline_evaluation.py": "1c9504f87e7ad02848eebe8402d48c7af46f7c2410dbc4b8b910d4e229aadc13", | |
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| "normalize.py": "8923d46705b9243543661f7ad21807da9937d00942af88f06c8712c25f85bf2e", | |
| "model.py": "9565f80cadc1505ffdc2fe14173dc6346ac231940b803b578b359dbbfe0fc4c8", | |
| "address_normalization.py": "a7333c17d3ab2dbd810119cc83c65bf6ad384f226b3423722e192933bfa6f6a3", | |
| "contact_normalization.py": "b9f76034be2d6969aabf34b388f9a2cc6a88ea6882ab00250b670b352616da83", | |
| "scalar_normalization.py": "6cdc6e130fc3c3bc094a2c5c12788e69ec7b3776d9e81d5fd19fbf6cd8ea54a1", | |
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| "service.py": "8d08033b43fc66e0b15639b4c8bdc2acf21960075ff38202021a115fc075756a", | |
| "types.py": "92767e4e51aacdbf7a47f6c9fe636e6e53cfbfcc128b8aa311d013aca9ea727c", | |
| "record_contracts.py": "b4a1b444c9eb84d3eb0be91934fd0ef3144c901b76dfdc9aba17d2e4f137a874", | |
| "evaluation_reference.py": "3b586fba528bcae02a113e9f90fb5825cc85437517df3d9d41e39e2dab449683", | |
| "episodes.py": "d2772a5f74064ffc2dc30f5c9f4f31b1af2680f29b39347c8b6ec480f58686a0", | |
| "episode_metrics.py": "3eb7da4076418e89047d145b1fd91e3eadde6eda474dbe5948a6d6d30fd0b0d3", | |
| "document_quality.py": "91fc5d0f9e227488367b1a3b89be3013147661e0aa64731c46e1cd961fd65b18" | |
| }, | |
| "normalization_artifacts": { | |
| "flake.lock": "42bad7791ef69530577c036a45a545a8734df47c8db2601280c34634e66e52a3", | |
| "flake.nix": "5be860e97e139e1ef0e745f6ee61058d4545b04cc0ef8258ef4b7f04107fcb4f", | |
| "uv.lock": "d29fdf67210e1a51d04252bc73941b40745f77b9afac34afe54dd94c747b27c5", | |
| "configs/libpostal-data.sha256": "71763ad90ba7c9c438dab4fab54cd0f50228edb539b30a075e4228b80c9debcf" | |
| } | |
| } | |