Instructions to use Maaly/host with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maaly/host with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Maaly/host")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Maaly/host") model = AutoModelForTokenClassification.from_pretrained("Maaly/host", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1 +1,8 @@
|
|
| 1 |
-
host model is a Named Entity Recognition (NER) model that identifies and annotates the host (living organism) of microbiome samples in texts.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
host model is a Named Entity Recognition (NER) model that identifies and annotates the host (living organism) of microbiome samples in texts.
|
| 2 |
+
|
| 3 |
+
Testing examples:
|
| 4 |
+
|
| 5 |
+
1. Turkestan cockroach nymphs (Finke, 2013) were fed to the treefrogs at a quantity of 10% of treefrog biomass twice a week.
|
| 6 |
+
2. The feces samples were collected from three healthy sub-adult pandas (Chengdu Research Base of Giant Panda Breeding, CHN) and mixed.
|
| 7 |
+
3. Field-collected bee samples were dissected on dry ice and separated into head, thorax (excluding legs and wings), and abdomens. For each colony, parts were separately pooled into 15 heads, 15 thorax, and 30 abdomens. Head and thorax weights were determined by drying at 60 °C to a constant weight and recording to the nearest 0.1 mg.
|
| 8 |
+
|