Instructions to use buyanov-tech/ru_ner_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use buyanov-tech/ru_ner_v2 with spaCy:
!pip install https://huggingface.co/buyanov-tech/ru_ner_v2/resolve/main/ru_ner_v2-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("ru_ner_v2") # Importing as module. import ru_ner_v2 nlp = ru_ner_v2.load() - Notebooks
- Google Colab
- Kaggle
Download meta.json from buyanov-tech/ru_ner_v2: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/buyanov-tech/ru_ner_v2/resolve/main/meta.json
- Command line
-
hf download hf://buyanov-tech/ru_ner_v2/meta.json
-
curl -L -o meta.json https://huggingface.co/buyanov-tech/ru_ner_v2/resolve/main/meta.json
2.39 kB
| { | |
| "lang":"ru", | |
| "name":"ner_v2", | |
| "version":"0.1.0", | |
| "description":"", | |
| "author":"", | |
| "email":"", | |
| "url":"", | |
| "license":"", | |
| "spacy_version":">=3.8.14,<3.9.0", | |
| "spacy_git_version":"0069cf9", | |
| "vectors":{ | |
| "width":0, | |
| "vectors":0, | |
| "keys":0, | |
| "name":null | |
| }, | |
| "labels":{ | |
| "tok2vec":[ | |
| ], | |
| "ner":[ | |
| "BRAND", | |
| "COLOR", | |
| "DENSITY", | |
| "DIAMETER", | |
| "LENGTH", | |
| "MODEL", | |
| "POWER", | |
| "PRODUCT_TYPE", | |
| "PROTECTION_RATING", | |
| "QUANTITY", | |
| "SIZE", | |
| "STANDARD", | |
| "VOLUME", | |
| "WEIGHT" | |
| ] | |
| }, | |
| "pipeline":[ | |
| "tok2vec", | |
| "ner" | |
| ], | |
| "components":[ | |
| "tok2vec", | |
| "ner" | |
| ], | |
| "disabled":[ | |
| ], | |
| "performance":{ | |
| "ents_f":0.8055687391, | |
| "ents_p":0.8131587352, | |
| "ents_r":0.7981191223, | |
| "ents_per_type":{ | |
| "BRAND":{ | |
| "p":0.8227183191, | |
| "r":0.8489159892, | |
| "f":0.8356118706 | |
| }, | |
| "SIZE":{ | |
| "p":0.8380743982, | |
| "r":0.8711144807, | |
| "f":0.8542750929 | |
| }, | |
| "PRODUCT_TYPE":{ | |
| "p":0.8543247344, | |
| "r":0.8536770281, | |
| "f":0.8540007584 | |
| }, | |
| "WEIGHT":{ | |
| "p":0.7619047619, | |
| "r":0.856187291, | |
| "f":0.8062992126 | |
| }, | |
| "DIAMETER":{ | |
| "p":0.7792207792, | |
| "r":0.6282722513, | |
| "f":0.6956521739 | |
| }, | |
| "LENGTH":{ | |
| "p":0.8387096774, | |
| "r":0.845, | |
| "f":0.8418430884 | |
| }, | |
| "MODEL":{ | |
| "p":0.7206557377, | |
| "r":0.6549463647, | |
| "f":0.6862316578 | |
| }, | |
| "COLOR":{ | |
| "p":0.8300835655, | |
| "r":0.8453900709, | |
| "f":0.8376669009 | |
| }, | |
| "QUANTITY":{ | |
| "p":0.7292418773, | |
| "r":0.6139817629, | |
| "f":0.6666666667 | |
| }, | |
| "STANDARD":{ | |
| "p":0.8925619835, | |
| "r":0.675, | |
| "f":0.768683274 | |
| }, | |
| "DENSITY":{ | |
| "p":0.8103448276, | |
| "r":0.6103896104, | |
| "f":0.6962962963 | |
| }, | |
| "POWER":{ | |
| "p":0.693877551, | |
| "r":0.6476190476, | |
| "f":0.6699507389 | |
| }, | |
| "PROTECTION_RATING":{ | |
| "p":0.9642857143, | |
| "r":0.8181818182, | |
| "f":0.8852459016 | |
| }, | |
| "VOLUME":{ | |
| "p":0.8205128205, | |
| "r":0.7559055118, | |
| "f":0.7868852459 | |
| } | |
| }, | |
| "tok2vec_loss":9234.9019866119, | |
| "ner_loss":14099.9501953125 | |
| }, | |
| "requirements":[ | |
| "spacy>=3.8.14,<3.9.0" | |
| ] | |
| } |