Instructions to use magepol/en_tech_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use magepol/en_tech_model with spaCy:
!pip install https://huggingface.co/magepol/en_tech_model/resolve/main/en_tech_model-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_tech_model") # Importing as module. import en_tech_model nlp = en_tech_model.load() - Notebooks
- Google Colab
- Kaggle
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Download README.md from magepol/en_tech_model: direct link, hf CLI and curl.
- Browser
- Download file 1.74 kB
-
https://huggingface.co/magepol/en_tech_model/resolve/main/README.md
- Command line
-
hf download hf://magepol/en_tech_model/README.md
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curl -L -o README.md https://huggingface.co/magepol/en_tech_model/resolve/main/README.md
1.74 kB
| tags: | |
| - spacy | |
| - token-classification | |
| - text-classification | |
| language: | |
| - en | |
| model-index: | |
| - name: en_tech_model | |
| results: | |
| - task: | |
| name: NER | |
| type: token-classification | |
| metrics: | |
| - name: NER Precision | |
| type: precision | |
| value: 0.9530851606 | |
| - name: NER Recall | |
| type: recall | |
| value: 0.9504195271 | |
| - name: NER F Score | |
| type: f_score | |
| value: 0.9517504774 | |
| | Feature | Description | | |
| | --- | --- | | |
| | **Name** | `en_tech_model` | | |
| | **Version** | `0.0.4` | | |
| | **spaCy** | `>=3.7.5,<3.8.0` | | |
| | **Default Pipeline** | `tok2vec`, `ner`, `textcat` | | |
| | **Components** | `tok2vec`, `ner`, `textcat` | | |
| | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | | |
| | **Sources** | n/a | | |
| | **License** | n/a | | |
| | **Author** | [n/a]() | | |
| ### Label Scheme | |
| <details> | |
| <summary>View label scheme (32 labels for 2 components)</summary> | |
| | Component | Labels | | |
| | --- | --- | | |
| | **`ner`** | `BATTERY`, `BRAND`, `CACHE`, `CAM_RES`, `CHARGE`, `CLOCK_SPEED`, `COLOR`, `CORE_COUNT`, `FEATURE`, `GEN`, `GRAPHICS`, `GRAPHICS_RAM`, `MEM_TYPE`, `MODEL_NUMBER`, `OPERATING_SYSTEM`, `PROCESSOR`, `PROCESSOR_MODEL`, `PRODUCT_SERIES`, `RAM`, `RESOLUTION`, `SCREEN_SIZE`, `SCREEN_TYPE`, `SOCKET`, `STORAGE`, `STORAGE_TYPE`, `TAG`, `TYPE` | | |
| | **`textcat`** | `267`, `292`, `297`, `325`, `328` | | |
| </details> | |
| ### Accuracy | |
| | Type | Score | | |
| | --- | --- | | |
| | `ENTS_F` | 95.18 | | |
| | `ENTS_P` | 95.31 | | |
| | `ENTS_R` | 95.04 | | |
| | `CATS_SCORE` | 99.51 | | |
| | `CATS_MICRO_P` | 99.52 | | |
| | `CATS_MICRO_R` | 99.52 | | |
| | `CATS_MICRO_F` | 99.52 | | |
| | `CATS_MACRO_P` | 99.55 | | |
| | `CATS_MACRO_R` | 99.48 | | |
| | `CATS_MACRO_F` | 99.51 | | |
| | `CATS_MACRO_AUC` | 100.00 | | |
| | `TOK2VEC_LOSS` | 1022799.22 | | |
| | `NER_LOSS` | 52929.76 | | |
| | `TEXTCAT_LOSS` | 4.84 | |