| Feature | Description |
|---|---|
| Name | en_tech |
| Version | 0.0.7 |
| 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 (33 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, 4745 |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
94.37 |
ENTS_P |
94.46 |
ENTS_R |
94.29 |
CATS_SCORE |
99.62 |
CATS_MICRO_P |
99.58 |
CATS_MICRO_R |
99.58 |
CATS_MICRO_F |
99.58 |
CATS_MACRO_P |
99.54 |
CATS_MACRO_R |
99.72 |
CATS_MACRO_F |
99.62 |
CATS_MACRO_AUC |
100.00 |
TOK2VEC_LOSS |
7666339.45 |
NER_LOSS |
135520.97 |
TEXTCAT_LOSS |
13.82 |
- Downloads last month
- 1
Evaluation results
- NER Precisionself-reported0.945
- NER Recallself-reported0.943
- NER F Scoreself-reported0.944
!pip install https://huggingface.co/magepol/en_tech/resolve/main/en_tech-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_tech") # Importing as module. import en_tech nlp = en_tech.load()