Token Classification
spaCy
English
Named Entity Recognition Model
NER
Cryptocurrency
Crypto
Eval Results (legacy)
Instructions to use KonstIT/Crypto_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use KonstIT/Crypto_NER with spaCy:
!pip install https://huggingface.co/KonstIT/Crypto_NER/resolve/main/Crypto_NER-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("Crypto_NER") # Importing as module. import Crypto_NER nlp = Crypto_NER.load() - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - spacy | |
| - token-classification | |
| - Named Entity Recognition Model | |
| - NER | |
| - Cryptocurrency | |
| - Crypto | |
| language: | |
| - en | |
| model-index: | |
| - name: Crypto NER | |
| results: | |
| - task: | |
| name: NER | |
| type: token-classification | |
| metrics: | |
| - name: NER Precision | |
| type: precision | |
| value: 0.8416666667 | |
| - name: NER Recall | |
| type: recall | |
| value: 0.808 | |
| - name: NER F Score | |
| type: f_score | |
| value: 0.8244897959 | |
| | Feature | Description | | |
| | --- | --- | | |
| | **Name** | `Crypto NER` | | |
| | **Version** | `1.0.0` | | |
| | **spaCy** | `>=3.4.4,<3.5.0` | | |
| | **Default Pipeline** | `transformer`, `ner` | | |
| | **Components** | `transformer`, `ner` | | |
| | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | |
| | **Sources** | n/a | | |
| | **License** | n/a | | |
| | **Author** | [n/a]() | | |
| ### Label Scheme | |
| <details> | |
| <summary>View label scheme (3 labels for 1 components)</summary> | |
| | Component | Labels | | |
| | --- | --- | | |
| | **`ner`** | `Cryptocurrency`, `ORG`, `PER` | | |
| </details> | |
| ### Accuracy | |
| | Type | Score | | |
| | --- | --- | | |
| | `ENTS_F` | 82.45 | | |
| | `ENTS_P` | 84.17 | | |
| | `ENTS_R` | 80.80 | | |
| | `TRANSFORMER_LOSS` | 727.79 | | |
| | `NER_LOSS` | 1327.85 | |