Instructions to use Jacobo/grc_roberta_lemma_trf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Jacobo/grc_roberta_lemma_trf with spaCy:
!pip install https://huggingface.co/Jacobo/grc_roberta_lemma_trf/resolve/main/grc_roberta_lemma_trf-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("grc_roberta_lemma_trf") # Importing as module. import grc_roberta_lemma_trf nlp = grc_roberta_lemma_trf.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
language:
- grc
model-index:
- name: grc_roberta_lemma_trf
results:
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9606052084
| Feature | Description |
|---|---|
| Name | grc_roberta_lemma_trf |
| Version | 3.7 |
| spaCy | >=3.7.4,<3.8.0 |
| Default Pipeline | transformer, trainable_lemmatizer |
| Components | transformer, trainable_lemmatizer |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Accuracy
| Type | Score |
|---|---|
LEMMA_ACC |
96.06 |
TRANSFORMER_LOSS |
51342.08 |
TRAINABLE_LEMMATIZER_LOSS |
681066.76 |