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
metadata
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
View label scheme (32 labels for 2 components)
| 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 |
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 |