Instructions to use mrm8488/codebert-base-finetuned-stackoverflow-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/codebert-base-finetuned-stackoverflow-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mrm8488/codebert-base-finetuned-stackoverflow-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/codebert-base-finetuned-stackoverflow-ner") model = AutoModelForTokenClassification.from_pretrained("mrm8488/codebert-base-finetuned-stackoverflow-ner") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -8,4 +8,15 @@ widget:
|
|
| 8 |
|
| 9 |
---
|
| 10 |
|
| 11 |
-
# Codebert (base) fine-tuned
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 8 |
|
| 9 |
---
|
| 10 |
|
| 11 |
+
# Codebert (base) fine-tuned this [dataset](https://aclanthology.org/2020.acl-main.443/) for NER
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
## Eval metrics
|
| 15 |
+
|
| 16 |
+
eval_accuracy_score = 0.9430622955139325
|
| 17 |
+
|
| 18 |
+
eval_precision = 0.6047440699126092
|
| 19 |
+
|
| 20 |
+
eval_recall = 0.6100755667506297
|
| 21 |
+
|
| 22 |
+
eval_f1 = 0.607398119122257
|