Instructions to use projecte-aina/multiner_ceil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projecte-aina/multiner_ceil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="projecte-aina/multiner_ceil")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/multiner_ceil") model = AutoModelForTokenClassification.from_pretrained("projecte-aina/multiner_ceil") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -76,7 +76,7 @@ Accuracy was calculated using the development set, and reflects the non-balanced
|
|
| 76 |
|
| 77 |
| Type | Accuracy | num. Instances in dev set |
|
| 78 |
| ------ | ------ | ------ |
|
| 79 |
-
| CW | 0.842 | 4551 |
|
| 80 |
| GPE | 0.914 | 19751 |
|
| 81 |
| Other | 0.69 | 2824 |
|
| 82 |
| building | 0.736 | 2188 |
|
|
@@ -86,6 +86,7 @@ Accuracy was calculated using the development set, and reflects the non-balanced
|
|
| 86 |
| person | 0.903 | 21689 |
|
| 87 |
| product | 0.64 | 1038 |
|
| 88 |
|
|
|
|
| 89 |
|
| 90 |
### Subtypes
|
| 91 |
|
|
|
|
| 76 |
|
| 77 |
| Type | Accuracy | num. Instances in dev set |
|
| 78 |
| ------ | ------ | ------ |
|
| 79 |
+
| CW * | 0.842 | 4551 |
|
| 80 |
| GPE | 0.914 | 19751 |
|
| 81 |
| Other | 0.69 | 2824 |
|
| 82 |
| building | 0.736 | 2188 |
|
|
|
|
| 86 |
| person | 0.903 | 21689 |
|
| 87 |
| product | 0.64 | 1038 |
|
| 88 |
|
| 89 |
+
*: Cultural Work
|
| 90 |
|
| 91 |
### Subtypes
|
| 92 |
|